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An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes
... The mathematical expression of this problem has been shown in "Appendix B.3". Several algorithms have been reported for solving this problem, such as GA (Gandomi et al., 2011), TLBO (Huang et al., 2015), teaching-learning-based cuckoo search algorithm (TLCS) (Huang et al., 2015), differential evolution (DE) (Gandomi et al., 2011), and firefly algorithm (FA) (Gandomi et al., 2011). For solving this problem, the maximum number of function valuations for ISGTOA is set to 40,000. ...
... The mathematical expression of this problem has been shown in "Appendix B.3". Several algorithms have been reported for solving this problem, such as GA (Gandomi et al., 2011), TLBO (Huang et al., 2015), teaching-learning-based cuckoo search algorithm (TLCS) (Huang et al., 2015), differential evolution (DE) (Gandomi et al., 2011), and firefly algorithm (FA) (Gandomi et al., 2011). For solving this problem, the maximum number of function valuations for ISGTOA is set to 40,000. ...
Most of the reported metaheuristic methods need the control parameters except the essential population size and terminal condition. When these methods are used for solving an unknown problem, how to set the most suitable values for their control parameters to achieve the optimal solution is a great challenge. Group teaching optimization algorithm (GTOA) is a newly presented metaheuristic method, whose remarkable feature is that it only relies on the essential population size and terminal condition for optimization. However, GTOA may get trapped in the local optimal solutions for solving complex optimization problems due to the lack of communication between outstanding group and average group. In order to improve the performance of GTOA, this paper proposes a new variant of GTOA, namely group teaching optimization algorithm with information sharing (ISGTOA). Like GTOA, ISGTOA doesn’t introduce any other control parameters, which enhances the communication between outstanding group and average group by reusing the individuals in the built two archives. The performance of ISGTOA is investigated by CEC 2014 test suite, CEC 2015 test suite, and four challenging constrained engineering design problems. Experimental results prove the superiority of ISGTOA for expensive optimization problems with multimodal properties by comparing with GTOA and other powerful methods. The source codes of the proposed ISGTOA can be found in https://ww2.mathworks.cn/matlabcentral/fileexchange/98629-the-source-code-of-isgtoa and https://github.com/jsuzyy/The-source-code-of-ISGTOA-for-global-optimization.
... In the past, different optimization methods from quadratic programming to the Harris hawks algorithm were used for finding optimal parameters of the grinding problems [74][75][76][77][78][79][80][81][82][83][84][85][86][87][88]. Wen et al. [74] used a quadratic programming approach to minimize the total cost of grinding. ...
... The comparisons of all algorithms are presented in Table 2 and Table 3. The best optimal values of the parameters in the literature [74,75,78,79,84,86,87,88] for both rough grinding and finish grinding are accomplished by the HSHO-NM, which provide the best results with 15.000 NEF (number of function evaluation). ...
In this research, a novel optimization algorithm,
which is a hybrid spotted hyena-Nelder-Mead optimization algorithm (HSHO-NM) algorithm, has been introduced in solving grinding optimization problems.
A well-known grinding optimization problem is
solved to prove the superiority of the HSHO-NM over
other algorithms. The results of the HSHO-NM are
compared with others. The results show that HSHONM is an efficient optimization approach for obtaining the optimal manufacturing variables in grinding
operations.
... Alsamia et al. [13] have applied in their study several efficient metaheuristics, such as artificial bee colony (ABC), Harris hawks optimizer (HHO), and flower pollination algorithm (FPA), to achieve the optimum penetration rate of the drilling process. Other examples of research using metaheuristic algorithms in the manufacturing optimization field can be found in the references [14][15][16][17][18][19]. ...
Deterministic optimization of machining processes in the manufacturing industry usually leads to suboptimal results with a high failure probability. This is due to the uncertainty and random variation of the input data which can be derived from diverse sources. Therefore, the purpose of this research work is to introduce a probabilistic optimization (PO) for handling manufacturing processes in the presence of uncertainties. First, a new PO approach (ESMV-GOA) is developed based on integrating the strategy of enriched self-adjusted mean value (ESMV) into the grasshopper optimization algorithm (GOA). Then, the proposed approach is applied to select the optimal machining parameters of a well-known grinding optimization problem. The obtained results indicate that the ESMV-GOA is a competent tool for optimizing manufacturing problems while guaranteeing the desired reliability level.
... They procreate in this way to increase the likelihood that their eggs will survive. When host birds discover these kinds of eggs, they might relocate and build a new nest, or they'll throw out the alien eggs [15]. Certain species have unique egg-laying schedules; parasitic cuckoos frequently select nest sites where their host bird has recently produced eggs. ...
... 3, as quoted by Stronge et al. (2011)]. Some research has been published with a combination of measures ("multiple measurements") (e.g., Cinnamon et al., 2021;Lohman, 2021;Taylor et al., 2021), while some papers chose not to measure TE even though they were writing about TE (e.g., Voogt et al., 2013;Huang et al., 2015). ...
Teaching effectiveness (TE) research impacts educators and their careers, learners and the quality of education they receive, and administrators and the organizations they safeguard. However, there is a lack of consistency in how TE has been conceptualized: many papers used inaccurate or implied definitions of TE, or despite discussing and often measuring TE, did not define TE—other papers defined TE without proposed measures or provided measures of the concept without defining it. We found two dimensions of TE, student-focused (outcome) and educator-focused (input), and an existing TE definition evaluated as the strongest for both dimensions. Further, TE measurements may be summarized in five categories: student evaluation of teaching effectiveness (SETE), objective measures, peer review, administrative evaluation, and self-reflection. To conceptualize TE, our findings suggest pairing the TE student-focused construct with SETE and objective measures, while the educator-focused dimensions of TE should be measured with peer review, self-assessment, and administrator evaluation. By consistently conceptualizing TE, researchers may contribute to rigorous research and work together to consistently add to the body of knowledge, thus furthering the quality of TE research.
... To examine the performance of the proposed hybrid algorithm, a real-life manufacturing problem of multi-pass milling (both face and end) operation is considered and investigated for optimum design variables so that the total production time can be minimized. The schematic diagram of this design problem is illustrated in Figure 28 and mathematically formulated as per the initial study of [65,66] as follows. Table 27 shows that the solutions calculated by using PGSA, GA, GP, and Tribes are infeasible as they violate the imposed constraints. ...
... Some recently developed optimization algorithms are adaptive firefly algorithm (AFA) (Baykasoglu and Ozsoydan 2015), teaching-learning-based CS (TLCS) algorithm (Huang et al. 2015), moth-flame optimization (MFO) algorithm (Mirjalili 2015), crow search algorithm (CSA) (Askarzadeh 2016), sine cosine algorithm (SCA) (Mirjalili 2016), whale optimization algorithm (WOA) (Mirjalili and Lewis 2016), Jaya algorithm (Rao 2016), elephant herding optimization (EHO) , salp swarm algorithm (SSA) (Mirjalili et al. 2017), improved Jaya (IJAYA) algorithm (Yu et al. 2017), emperor penguin optimizer (EPO) (Dhiman and Kumar 2018), novel DE (NDE) (Mohamed 2018), modified Jaya algorithm (MJAYA) (Elattar and Elsayed 2019), Henry gas solubility optimization (HGSO) (Hashim et al. 2019), Harris Hawks optimization (HHO) (Heidari et al. 2019), poor and rich optimization (PRO) (Moosavi and Bardsiri 2019), performance-guided Jaya algorithm (PGJAYA) (Yu et al. 2019), supply-demand-based optimization (SDO) (Zhao et al. 2019), multi-strategy SCA (MSCA) (Chen et al. 2020), symbiotic organisms search (ISOS) (Ç elik 2020), equilibrium optimizer (EO) (Faramarzi et al. 2020), hybrid HHO-SCA (Kamboj et al. 2020), manta ray foraging optimization (MRFO) , grey prediction evolution algorithm (GPEA) (Hu et al. 2020), political optimizer (PO) (Askari et al. 2020), enhanced Jaya algorithm (EJAYA) (Zhang et al. 2021). Rao (2020) has recently developed three metaphor-less optimization algorithms named Rao algorithms that do not have algorithm-specific control parameters. ...
This paper proposes a simple and effective optimization algorithm named as "Improved Rao (I-Rao) algorithm". The I-Rao algorithm consists of two phases: the local exploitation phase and the global exploration phase. The local exploitation phase improves the exploitation of the search process of Rao algorithms and enhances the algorithm's convergence speed. The global exploration phase helps the algorithm to get away from the local optimum solutions and enhances the exploration ability of the search process of the algorithm. Like Rao algorithms, the I-Rao algorithm also does not need algorithm-specific control parameters. The effectiveness of the I-Rao algorithm is tested on 25 unconstrained benchmark functions, 45 complex CEC test functions, and 19 real-world constrained mechanical design optimization problems of CEC2020. The comparison of results reveals the effectiveness of the I-Rao algorithm over many state-of-the-art advanced optimization algorithms such as GWO, SCA, PSO, WOA, Jaya, IJAYA, MJAYA, PGJAYA, EJAYA, IUDE, εMAgES, iLSHADEε, and COLSHADE. The Friedman test is also carried out to check the overall performance of the I-Rao algorithm as compared to other advanced optimization algorithms considered. The convergence plots illustrate the convergence speed of the I-Rao algorithm.
... The feasibility of cuckoo optimization and hoopoe heuristic to handle multi-response machining problems of grinding, drilling, ultrasonic machining, abrasive jet machining (AJM), water jet machining (WJM) and abrasive water jet machining (AWJM) were investigated by Mellal and Williams (2016). A teaching-learning-based cuckoo search (TLCS) was proposed by Huang et al. (2015) to solve the AWJM, grinding and milling processes by leveraging the excellent global and local search abilities of Levy flight and TLBO, respectively. A multi-objective Jaya (MO-Jaya) was proposed by Rao et al. (2017) to tackle various modern machining processes including the wire-EDM, ECM, FIB micro-milling and laser cutting. ...
Many real-world engineering problems such as machining processes are multi-objective optimization problems (MOPs) because multiple performance characteristics are considered to satisfy their contradictory goals. An improved multi objective teaching-learning-based optimization with refined knowledge sharing mechanisms (IMTLBO-RKSM) is proposed to tackle these MOPs effectively. Pareto dominance concept is first incorporated into IMTLBO-RKSM to handle the tradeoffs of multiple contradictory objectives. Appropriate modifications are incorporated into both teacher and learner phases of IMTLBO-RKSM to emulate to emulate the knowledge sharing processes of classroom more accurately, hence achieving better balancing of exploration and exploitation searches. Particularly, both concepts of Euclidean-distance based teacher assignment scheme and social learning are incorporated into the IMTLBO-RKSM’s teacher phase to derive the unique directional information that can provide better guidance for each learner. The learner phase of IMTLBO-RKSM is also modified by designing two new learning mechanisms known as independent learning and adaptive peer learning, aiming to facilitate different preferences of learners in acquiring new knowledge. The performance of IMTLBO-RKSM is evaluated and compared with six multi-objective optimization methods by using five case studies of multi-response machining problems and twelve MOP benchmark functions. Extensive simulation studies show that IMTLBO-RKSM have more competitive performance than other methods by generating Pareto fronts with better quality in terms of accuracy and diversity of solution members for most tested problems.
... et al., 2020) (TSA), Sooty Tern Optimization Algorithm (Gaurav and Amandeep, 2019) (STOA), Seagull optimization algorithm (Gaurav and Vijay, 2019) (SOA), Chimp Optimization Algorithm (Khishe and Mosavi, 2020) (ChOA), Aquila Optimizer (Abualigah et al., 2021c) (AO), Salp Swarm Algorithm (Mirjalili et al., 2016a) (SSA), Whale Optimization Algorithm (Mirjalili and Lewis, 2016) (WOA), Grasshopper Optimization Algorithm (Saremi et al., 2017) (GOA), Multi-Verse Optimizer (Mirjalili et al., 2016b) (MVO), Slime Mould Algorithm (Li et al., 2020) (SMA), Reptile Search Algorithm (Abualigah et al., 2022) (RSA), Moth-Flame Optimization Algorithm (Mirjalili, 2015) (MFO), Marine Predators Algorithm (Faramarzi et al., 2020a) (MPA), HGSO(Hashim et al., 2019), Teaching-learning-based optimization(Huang et al., 2015) (TLBO), and Equilibrium optimizer(Faramarzi et al., 2020b) (EO). In addition, some well-known improved algorithms are introduced as comparison algorithms, including Adaptive opposition slime mould algorithm(Naik et al., 2021) (AOSMA), Hybrid GWO with WOA (Mohammed and Rashid, 2020) (WOAGWO), Socio-behavioural ...
In this paper, a self-adaptive classification learning hybrid JAYA and Rao-1 algorithm, which is called EHRJAYA, is proposed for solving large-scale numerical problems and real-world complex engineering optimization problems. JAYA algorithm and Rao-1 algorithm are two kinds of algorithms with simple structure and superior performance, which have the characteristics of no public parameters. In EHRJAYA the evolution strategies of the two algorithms are selected through a random selection mechanism. Then, a novel self-adaptive classification learning strategy is proposed, which fully utilizes information from different individuals. On this basis, two different adaptive coefficients are introduced to guide the population towards the optimal individual and away from the worst individual. Finally, combining the linear population reduction strategy and the dynamic lens opposition-based learning strategy, the convergence speed and ability to jump out of local optimum of the algorithm are greatly improved. To verify the performance of the proposed EHRJAYA, 59 complex functions from the CEC2014 and CEC2017 competitions are solved by EHRJAYA. Then, EHRJAYA and more than 20 algorithms with superior performance jointly solve ten challenging real-world engineering optimization problems. Experimental results show that the proposed EHRJAYA can obtain optimal results with the least computational resources in most cases. Therefore, in the face of these problems, effective solutions can be provided by EHRJAYA.
... CS and PSO are well-known global-search algorithms with solid exploration power [39,40]. However, they are both prone to getting trapped in local optima. ...
Major corporations compete over the strengths of their supply chains. Integrating production and distribution operations helps improve supply chain connectedness and responsiveness beyond the standalone optimization norms. This study proposes an original Mixed-Integer Linear Programming (MILP) formulation for the Production scheduling-based Routing Problem with Time Window and Setup Times (PRP-TWST). For this purpose, the identical parallel machine scheduling is integrated with the vehicle routing problem. Considering the highly intractable solution spaces of the integrated problem, hybrid metaheuristics based on the Variable Neighborhood Search (VNS), Particle Swarm Optimization (PSO), and Cuckoo Search (CS) algorithms are developed to solve the PRP-TWST problem. Extensive numerical experiments are conducted to evaluate the effectiveness of the developed algorithms with the total delay time being the objective function. Overall, the results are supportive of the VNS-based CS algorithm’s effectiveness; the developed metaheuristics can be considered strong benchmarks for further developments in the field. This study is concluded by suggesting directions for modeling and managing integrated operations in the supply chain context.
... Wang et al. proposed an intelligent optimized hybrid model based on CS algorithm to forecast solar radiation [33]. In Ref. [34], authors hybridized CS and teaching-learning-based algorithms to address optimization problems in structure designing and machining processes. Moreover, Cobos et al. developed a method based on CS and balanced Bayesian information criterion for clustering web search results [35]. ...
Cuckoo search (CS) is one of the well-known evolutionary techniques in global optimization. Despite its efficiency and wide use, CS suffers from premature convergence and poor balance between exploration and exploitation. To address these issues, a new CS extension namely snap-drift cuckoo search (SDCS)is proposed in this study. The proposed algorithm first employs a learning strategy and then considers improved search operators. The learning strategy provides an online trade-off between local and global search via two snap and drift modes. In snap mode, SDCS tends to increase global search to prevent algorithm of being trapped in a local minima; and in drift mode, it reinforces the local search to enhance the convergence rate. Thereafter, SDCS improves search capability by employing new crossover and mutation search operators. The accuracy and performance of the proposed approach are evaluated by well-known benchmark functions. Statistical comparisons of experimental results show that SDCS is superior to CS, modified CS (MCS), and state-of-the-art optimization algorithms in terms of convergence speed and robustness
... This engineering optimization problem is a maximization problem. The results obtained by ESOA are compared with those recorded in literature [124][125][126][127], and the comparison results are shown in Table 11. It can be observed from Table 11 that the final material removal rate provided by ESOA is 325.6878mm 3 /s, while the results provided by TLCS and CS are 307.87mm 3 /s and 305.76mm 3 /s respectively. ...
The seagull optimization algorithm (SOA) is a recently proposed meta-heuristic optimization algorithm inspired by seagull foraging behavior. It has the advantages of simple structure and easy implementation. However, it also has some shortcomings, such as easily falling into local optimal and low convergence accuracy when solving complex engineering optimization problems. In this paper, to overcome the defects of the original SOA, an enhanced seagull optimization algorithm (ESOA) based on mutualism mechanism and commensalism mechanism is proposed. To evaluate the performance of the ESOA algorithm, the IEEE CEC2020 benchmark suite is utilized to verify the effectiveness of the ESOA algorithm, and the results are compared and analyzed with the latest meta-heuristic optimization algorithms. In addition, the ESOA algorithm is applied to twelve different types of engineering optimization problems, including pressure vessel design problem, multiple disc clutch brake design problem, three bar truss design problem, car crashworthiness problem, cantilever beam problem, abrasive water jet machine, gas transmission compressor design problem, hydro-static thrust bearing design problem, speed reducer problem, tubular column design problem, I beam design problem and industrial refrigeration system design problem. The convergence curves of ESOA and the comparison results of the latest metaheuristic algorithms are analyzed and compared with those reported in the latest literature. The results show that the ESOA algorithm is an optimization method that can find the optimal solution in engineering design problems, and has strong competitiveness compared with other algorithms.
... The above studies successfully verified the feasibility of applying the NSGA-II algorithm in the optimization of cutting parameters. However, the optimization of milling parameter is usually a multimodal landscape optimization problem (Huang et al. 2015), which makes NSGA-II easily fall into the local optimum. In order to improve the performance of NSGA-II algorithms, Wang et al. (2011) used dynamic crowding distance and controlled elitism, which increased the diversity of algorithms. ...
Aluminum alloy has high strength and light weight. It is widely used for aircraft fuselage, propellers and other parts which work under high load conditions. High-quality parts made of aluminum alloy processed by computerized numerical control (CNC) machine often have the characteristics of high cost in their processing. In order to achieve high surface quality and control processing costs, this article takes the workpiece surface hardness and machining energy consumption as targets. Intelligent optimization algorithm is used to find the optimal combination of milling parameters to obtain ideal targets. CNC milling parameter optimization is a multi-parameter, multi-objective, multi-constraint, discrete nonlinear optimization problem which is difficult to solve. For this challenge, an improved NSGA-II is presented, named enhanced population diversity NSGA-II (EPD-NSGA-II). EPD-NSGA-II is improved with the normal distribution crossover, adaptive mutation operator of differential evolution, crowding calculation method considering variance and modified elite retention strategy to achieve enhanced population diversity. 12 test functions are chosen for experimentation to verify the performance of the EPD-NSGA-II. The values of three evaluation indicators show that the proposed approach has good distribution and convergence performance. Finally, the approach is applied in the milling parameters optimization of 7050 aluminum alloy to get the optimal solutions. Results indicate that the EPD-NSGA-II is effective in optimizing the problem of milling parameters.
... Lim and Isa [29] adapted the TLBO framework into the particle swarm optimization and proposed teaching and peer-learning particle swarm optimization. Huang et al. [30] proposed an effective teaching-learning-based cuckoo search (TLCS) algorithm for parameter optimization problems in structure designing and machining processes. Zou et al. [31] proposed an improved teaching-learning-based optimization with differential learning (DLTLBO) for IIR System Identification problems. ...
Teaching-learning-based optimization (TLBO) is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner’s learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introduces a learning enthusiasm mechanism into the basic TLBO and proposes a learning enthusiasm based TLBO (LebTLBO). In the LebTLBO, learners with good grades have high learning enthusiasm, and they have large probabilities of acquiring knowledge from others; by contrast, learners with bad grades have low learning enthusiasm, and they have relative small probabilities of acquiring knowledge from others. In addition, a poor student tutoring phase is introduced to improve the quality of the poor learners. The proposed method is evaluated on the CEC2014 benchmark functions, and the computational results demonstrate that it offers promising results compared with other efficient TLBO and non-TLBO algorithms. Finally, LebTLBO is applied to solve three optimal control problems in chemical engineering, and the competitive results show its potential for real-world problems.
... Mutation and crossover operators of differential evolution algorithm were introduced into TLBO, which can improve the exploration ability and increase the population diversity [20]. Huang et al. combined TLBO with cuckoo algorithm to enhance the local search ability of TLBO algorithm [21].Tuo et al. combined harmony search algorithm with TLBO algorithm for solving complex high-dimensional optimization problems [22]. These TLBO variants have shown faster convergence speed and better convergence accuracy than the original TLBO. ...
Recent ten years, the teaching learning based optimization algorithm (TLBO) has been widely concerned and successfully applied to solve various constraints and non-constraints problems. However, its convergence accuracy and convergence speed should be further improved. Therefore, a novel chaotic teaching learning based optimization algorithm (called CTLBO) is proposed. Firstly, chaotic variables are applied to initialize population individuals for increasing the diversity of population. Secondly, a kind of self-adaptive acceleration coefficient is introduced into teaching phase to enhance the convergence speed and solution quality. Finally, two population updating mechanisms are proposed to balance the exploration and exploitation capabilities in the learning phase. One is neighbor elitist search mechanism, another is chaos optimization mechanism. The performance of CTLBO is compared with five state-of-the-art optimization algorithms by several CEC mathematical problems. The experiment results show that the CTLBO yields better convergence rate than other algorithms on most testing functions. Additionally, the proposed CTLBO is applied to optimize the model parameters of extreme learning machine(ELM) and the tuned ELM is adopted to establish the NOx emissions model. Experiment results reveal that the NOx emissions model has good accuracy and meets the engineering requirement.
... • The second group is the TLBO hybrid approach which combines TLBO with other existing algorithms. In [38], the author introduces an efficient Teaching-Learningbased Cuckoo Search Algorithm (TLCS) for parameter optimization problems. Another hybrid based algorithm is introduced by Chen et al. in [39] where TLBO is combined with Artificial Bee Colony (ABC) algorithm. ...
With the ever increasing demand and stressed operating conditions, resource expansion is the only way to have sustainable electric grid. Transmission system expansion is one of the important aspects in this regard. In the recent years, expansion problem has been addressed by several researchers. Meta-heuristic techniques have been applied to solve expansion problems. In this paper, a new variant of Teaching Learning Based Optimization (TLBO) Algorithm is proposed by adding a sine function based diversity in the teaching phase. The proposed variant is named as Composite TLBO (C-TLBO). The efficacy of the proposed variant has been evaluated on standard benchmark functions and then it is evaluated on two standard electrical networks with cases of inclusion of uncertainty and demand burst. The results obtained from optimization processes have been evaluated with the help of several analytical and statistical tests. Results affirm that the proposed modification enhances the performance of the algorithm in a substantial manner.
... In another research paper, Huang et al. [37] developed an optimization method for the optimization of machining processes. ...
In this work, the optimization of structural and mechanical problems is carried out using the equilibrium optimizer (EO), which is a recent physical-based algorithm.The the ten-bar planar truss structure, planetary gearbox, hydrostatic thrust bearing, and robot gripper mechanism problems are solved using the EO algorithm. The results achieved using the EO in solving these problems are compared with those of recent algorithms. The computational results show that EO yields better outcomes and competitive results that can also be applied for the other problems studied.
... Huang et al. [60] also adapted two optimisation hybrid methods and applied teaching-learning based outcome (TLBO) and CSA algorithm for parameter optimisation problems in machining processes that include abrasive water jet, grinding, and milling operations. In the proposed hybrid algorithm, the main idea is to combine good search ability of CSA and the fast convergence rate of TLBO. ...
Optimisation of machining parameters is crucial to ensure higher productivity and optimum outcomes in machining processes. By optimising machining parameters, a particular machining process can produce better machining outcomes within equivalent resources. This paper reviews past studies to achieve the desired outputs; minimum surface roughness (SR), highest material removal rate (MRR), lowest production cost, and the shortest production time of machining processes and various optimisation attempts in terms of varying parameters that affect the outcomes. The review deliberates the optimisation methods employed and analyses the performance discussing the relevant parameters that must have been considered by past researchers. To date, most studies have been focusing on optimising conventional machining processes such as turning, milling, and drilling. Optimisation works have been performed parametrically, experimentally, and numerically, where discrete variations of the parameters are investigated, while others are remained constant. Lately, evolutionary algorithm, statistical approaches such as genetic algorithm (GA), particle swarm optimisation (PSO), and cuckoo search algorithm (CSA) have been utilised in simultaneous optimisation of the parameters of the desired outputs and its great potential in optimising machining processes is recognisable.
... It can obviously be concluded that the proposed CHGSO is superior to those metaheuristics presented in the literature by Wen et al. [59], Baskar et al. [61], Saravanan et al. [60], Krishna and Rao [62], Lin and Li [66], Krishna [65], Rao and Pawar [69], Zhang et al. [64], Lee et al. [63], Pawar and Rao [71], Khalilpourazari and Khalilpourazary [73], Huang et al. [72], Yildiz [37], and Yildiz et al. [5]. ...
The paper proposes a novel metaheuristic based on integrating chaotic maps into a Henry gas solubility optimization algorithm (HGSO). The new algorithm is named chaotic Henry gas solubility optimization (CHGSO). The hybridization is aimed at enhancement of the convergence rate of the original Henry gas solubility optimizer for solving real-life engineering optimization problems. This hybridization provides a problem-independent optimization algorithm. The CHGSO performance is evaluated using various conventional constrained optimization problems, e.g., a welded beam problem and a cantilever beam problem. The performance of the CHGSO is investigated using both the manufacturing and diaphragm spring design problems taken from the automotive industry. The results obtained from using CHGSO for solving the various constrained test problems are compared with a number of established and newly invented metaheuristics, including an artificial bee colony algorithm, an ant colony algorithm, a cuckoo search algorithm, a salp swarm optimization algorithm, a grasshopper optimization algorithm, a mine blast algorithm, an ant lion optimizer, a gravitational search algorithm, a multi-verse optimizer, a Harris hawks optimization algorithm, and the original Henry gas solubility optimization algorithm. The results indicate that with selecting an appropriate chaotic map, the CHGSO is a robust optimization approach for obtaining the optimal variables in mechanical design and manufacturing optimization problems.
In the era of artificial intelligence (AI), optimization and parametric studies of engineering and structural systems have become feasible tasks. AI and ML (machine learning) offer advantages over classical optimization techniques, which often face challenges such as slower convergence, difficulty handling multiobjective functions, and high computational time. Modern AI and ML techniques may not effectively address all critical design engineering problems despite these advancements. Nature-inspired algorithms based on physical phenomena in nature, human behavior, swarm intelligence, and evolutionary principles present a viable alternative for multidisciplinary design optimization challenges. This article explores the optimization of various engineering problems using a newly developed modified hiking optimization algorithm (HOA). The algorithm is inspired by human hiking techniques, such as hill climbing and hiker speed. The advantages of the modified HOA are compared with those of several famous algorithms from the literature, demonstrating superior results in terms of statistical measures.
The primary goal of a power distribution system is to provide nominal voltages and power with minimal losses to meet consumer demands under various load conditions. In the distribution system, power loss and voltage uncertainty are the common challenges. However, these issues can be resolved by integrating distributed generation (DG) units into the distribution network, which improves the overall power quality of the network. If a DG unit with an appropriate size is not inserted at the appropriate location, it might have an adverse impact on the power system’s operation. Due to the arbitrary incorporation of DG units, some issues occur such as more fluctuations in voltage, power losses, and instability, which have been observed in power distribution networks (DNs). To address these problems, it is essential to optimize the placement and sizing of DG units to balance voltage variations, reduce power losses, and improve stability. An efficient and reliable strategy is always required for this purpose. Ensuring more stable, safer, and dependable power system operation requires careful examination of the optimal size and location of DG units when integrated into the network. As a result, DG should be integrated with power networks in the most efficient way possible to enhance power dependability, quality, and performance by reducing power losses and improving the voltage profile. In order to improve the performance of the distribution system by using optimal DG integration, there are several optimization techniques to take into consideration. Computational-intelligence-based optimization is one of the best options for finding the optimal solution. In this research work, a computational intelligence approach is proposed to find the appropriate sizes and optimal placements of newly introduced different types of DGs into a network with an optimized multi-objective framework. This framework prioritizes stability, minimizes power losses, and improves voltage profiles. This proposed method is simple, robust, and efficient, and converges faster than conventional techniques, making it a powerful tool of inspiration for efficient optimization. In order to check the validity of the proposed technique standard IEEE 14-bus and 30-bus benchmark test systems are considered, and the performance and feasibility of the proposed framework are analyzed and tested on them. Detailed simulations have been performed in “MATLAB”, and the results show that the proposed method enhances the performance of the power system more efficiently as compared to conventional methods.
In this study, the Learning Search Algorithm (LSA) is introduced as an innovative optimization algorithm that draws inspiration from swarm intelligence principles and mimics the social learning behavior observed in humans. The LSA algorithm optimizes the search process by integrating historical experience and real-time social information, enabling it to effectively navigate complex problem spaces. By doing so, it enhances its global development capability and provides efficient solutions to challenging optimization tasks. Additionally, the algorithm improves the collective learning capacity by incorporating teaching and active learning behaviors within the population, leading to improved local development capabilities. Furthermore, a dynamic adaptive control factor is utilized to regulate the algorithm’s global exploration and local development abilities. The proposed algorithm is rigorously evaluated using 40 benchmark test functions from IEEE CEC 2014 and CEC 2020, and compared against nine established evolutionary algorithms as well as 11 recently improved algorithms. The experimental results demonstrate the superiority of the LSA algorithm, as it achieves the top rank in the Friedman rank-sum test, highlighting its power and competitiveness. Moreover, the LSA algorithm is successfully applied to solve six real-world engineering problems and 15 UCI datasets of feature selection problems, showcasing its significant advantages and potential for practical applications in engineering problems and feature selection problems.
In this article, a newly developed optimization approach based on a mathematics technique named the geometric mean optimization algorithm is employed to address the optimization challenge of the robot gripper, airplane bracket, and suspension arm of automobiles, followed by an additional three engineering problems. Accordingly, other challenges are the ten-bar truss, three-bar truss, tubular column, and spring systems. As a result, the algorithm demonstrates promising statistical outcomes when compared to other well-established algorithms. Additionally, it requires less iteration to achieve the global optimum solution. Furthermore, the algorithm exhibits minimal deviations in results, even when other techniques produce better or similar outcomes. This suggests that the proposed approach in this paper can be effectively utilized for a wide range of critical industrial and real-world engineering challenges.
Effective teaching is considered one of the dominant and emerging topics in education. Hence, there are two objectives of the study. The first objective is to bibliometricaly analyze the performance in terms of citations and publications of Effective Teaching studies. The second objective is to analyze the research themes of Effective Teaching studies. The motivation to conduct this study is based on the gaps in previous studies on effective teaching. This study mainly uses bibliometric analysis, whereby 986 documents were analyzed through two stages – descriptive analysis and keyword network analysis. Selected findings indicated that since 1941, effective teaching studies displayed a growing trend in publications and citation performances. In addition, five research themes dominate effective teaching - 1) effective teaching in higher education, 2) enhancing curriculum with blended teaching, 3) pedagogical professional development, 4) medical education effectiveness, and 5) teacher training for classroom effectiveness. The authors also suggested six potential future research questions in terms of mixed theories, practical and methodology aspects. Hence, the significance of the study is to inspire potential scholars to explore potential holes available in effective teaching
Nature-inspired metaheuristic optimization algorithms have many applications and are more often studied than conventional optimization techniques. This article uses the mountain gazelle optimizer, a recently created algorithm, and artificial neural network to optimize mechanical components in relation to vehicle component optimization. The family formation, territory-building, and food-finding strategies of mountain gazelles serve as the major inspirations for the algorithm. In order to optimize various engineering challenges, the base algorithm (MGO) is hybridized with the Nelder–Mead algorithm (HMGO-NM) in the current work. This considered algorithm was applied to solve four different categories, namely automobile, manufacturing, construction, and mechanical engineering optimization tasks. Moreover, the obtained results are compared in terms of statistics with well-known algorithms. The results and findings show the dominance of the studied algorithm over the rest of the optimizers. This being said the HMGO algorithm can be applied to a common range of applications in various industrial and real-world problems.
Twin‐roll strip casting is a near‐net‐shape casting technology that can produce thin steel strips directly from molten steel. Stably controlling the molten steel level is regarded as an important issue to ensure strip quality and casting process stability. As the control of the molten steel level is a time‐varying, nonlinear, and multidisturbance complex system, it is difficult to establish an accurate process model for designing a model‐based controller. Top side‐pouring twin‐roll casting is a new kind of twin‐roll strip casting technology. This study introduces the control system of the top side‐pouring twin‐roll casting process. A fuzzy logic controller (FLC) with its fuzzy rules optimized by particle swarm optimization (PSO) is developed to regulate the molten steel level. Simulation results show that the performance of the FLC can be improved while its fuzzy rules are optimized by PSO. The objective function of PSO has a great influence on the optimization of the fuzzy rules. The top side‐pouring twin‐roll casting experiments are carried out using the FLC with its fuzzy rules optimized by PSO; the results show that strip quality and casting process stability are guaranteed.
Teaching-learning-based optimization algorithm (TLBO) is a swarm intelligence optimization algorithm that simulates classroom teaching phenomenon. In order to solve the problem that TLBO algorithm is easy to fall into local optimum and has poor stability, an improved teaching-learning-based optimization algorithm based on fusion difference mutation (IDMTLBO) is proposed. Firstly, adaptive teaching factors are introduced. Secondly, in the teaching stage, each student studies according to the gap between himself and the teacher, which improves the convergence speed and convergence accuracy of the algorithm. Finally, in the learning stage, students are divided into two levels according to their learning level, and two students are randomly selected to improve the iterative equation in the learning stage with the difference mutation strategy, It improves the disadvantage that the algorithm is easy to fall into local optimum. Numerical experiments show that the convergence speed and convergence accuracy of the algorithm are obviously better than TLBO algorithm, DMTLBO algorithm, DSTLBO algorithm.
The sine cosine algorithm (SCA) has drawn significant attention from researchers in different fields because of fewer control parameters, excellent global optimization ability, and simple principles. However, there are some drawbacks of the SCA which needed immediate attention. The position-updated equation of the SCA is good at exploration but poor at exploitation, which leads to slow converging speed and low converging accuracy in some complex cases. Therefore, an exploitation-boosted sine cosine algorithm (namely, EBSCA) is proposed in this study. In order to enhance the ability of exploitation, a new position-updated equation is designed by emphasizing the position information of the best individual, thereby guiding the updating of new candidate individuals. Meanwhile, the information weights of the best individual and the current individual in the new equation are dynamically adjusted by a balance factor b to avoid over-exploitation of the information from the best individual. Furthermore, a new integration way of combining the quantization orthogonal crossover strategy with the SCA is proposed to improve the utilizing efficiency of the searching space. The performance of the EBSCA is evaluated by being tested on 13 classical benchmark functions, IEEE CEC 2015 problems, and four well-known engineering applications. The comparisons demonstrate that the EBSCA algorithm obviously improves the performance of the SCA. Additionally, the experimental results show that , the proposed EBSCA algorithm exhibits higher competitiveness compared to other algorithms participated in this research.
The flower pollination algorithm (FPA) is a nature-inspired optimization algorithm that mimics the pollination behaviour of flowering plants. Despite the promising performance of FPA in solving single objective optimization problems, its convergence still poses challenges in practice. This study proposes a modified FPA with additional features from chaos theory and frog leaping algorithm augmented by inertia weights. The modified FPA proposed in this study is tested against benchmark mathematical functions, mechanical engineering design optimization problems, and machining process optimization problems. Performance comparison with other state-of-the-art optimization algorithms has demonstrated its ability in terms of convergence. The modified FPA significantly reduced the number of function evaluations by 84.14%, as compared to FPA in optimizing the benchmark mathematical functions. Besides, the proposed modified FPA outperformed others in 12 out of 15 mechanical engineering optimization problems.
Evolutionary algorithms have been shown to be very effective in solving complex optimization problems. This has driven the research community in the development of novel, even more efficient evolutionary algorithms. The newly proposed algorithms need to be evaluated and compared with existing state-of-the-art algorithms, usually by employing benchmarks. However, comparing evolutionary algorithms is a complicated task, which involves many factors that must be considered to ensure a fair and unbiased comparison. In this paper, we focus on the impact of stopping criteria in the comparison process. Their job is to stop the algorithms in such a way that each algorithm has a fair opportunity to solve the problem. Although they are not given much attention, they play a vital role in the comparison process. In the paper, we compared different stopping criteria with different settings, to show their impact on the comparison results. The results show that stopping criteria play a vital role in the comparison, as they can produce statistically significant differences in the rankings of evolutionary algorithms. The experiments have shown that in one case an algorithm consumed 50 times more evaluations in a single generation, giving it a considerable advantage when max gen was used as the stopping criterion, which puts the validity of most published work in question.
Because of global manufacturing, the foundry production workshop has shifted from single-factory production to multi-factory production. The distributed flexible job-shop scheduling problem is studied in this paper, and a distributed sand casting job-shop scheduling problem optimization model is established. To solve this model, this paper proposes a hybrid teaching-learning-based optimization (HTLBO) algorithm that involves a three-layer coding solution and a variety of strategies for population initialization. The HTLBO consists of the teacher learning phase, teaching phase, and learning phase. To improve the quality of teachers in the proposed algorithm, this paper sets the dynamic teacher group and adopts the tabu search based on the critical path and key blocks to increase the number of teachers in the dynamic teacher group and conduct the process of the teacher learning phase. In the teaching and learning phase, a variety of crossover operators for teaching and learning operations is designed to realize the process of teaching and learning. Finally, the experimental results of a real sand casting enterprise case indicate that the proposed algorithm performs better than the other six algorithms.
This thesis deals with the study of a multi-objective optimization problem in the field of swarm robotics. The problem is to design one or more behavioral models of a set of agent-robots having as a task the grouping of objects while satisfying two objectives: maximizing the quality of the formed heap and minimizing the energy consumed to accomplish the task. The motivation to study this case, in particular, is to help pave the way for the design of a swarm robotic system aware of the consumed energy while providing good quality in terms of the given task. Three contributions have been proposed in this thesis. In the first part, the usual problem of object clustering with a single objective is remodeled to deal with the two contradictory objectives. A multi-objective optimization algorithm has been used to design the set of individual behaviors of agent-robots and to analyze the overall behavior of the swarm. Unlike other solutions proposed to solve the object clustering task, the energy consumed is estimated during the design phase. Also, a distinction is made between models of behaviors that provide the same quality of clustering. Finally, the energy consumed to accomplish the task is less varied from one experiment to another. In the second part, a behavioral model is analyzed under different circumstances; using this model, the robustness, scalability and flexibility of the system are also examined. The superiority of the proposed approach is proved by comparing two bi-objective behavioral models against another mono-objective model. In a third part, a variant of teaching-learning-based optimization algorithm is proposed to solve the object clustering problem. The algorithm has shown promising performance against the presented discrete large-scale multi-objective problem.
Jaya algorithm (JAYA) is a new metaheuristic algorithm, which has a very simple structure and only requires population size and terminal condition for optimization. Given the two features, JAYA has been widely used to solve various types of optimization problems. However, JAYA may easily get trapped in local optima for solving complex optimization problems due to its single learning strategy with little population information. To improve the global search ability of JAYA, this work proposes an enhanced Jaya algorithm (EJAYA) for global optimization. In EJAYA, local exploitation is based on the defined upper and lower local attractors and global exploration is guided by historical population. Like JAYA, EJAYA doesn’t need any effort for fine tuning initial parameters. To check the performance of the proposed EJAYA, EJAYA is first used to solve 45 test functions extracted from the well-known CEC 2014 and CEC 2015 test suites. Then EJAYA is employed to solve seven challenging real-world engineering design optimization problems. Experimental results support the strong ability of EJAYA to escape from the local optimum for solving complex optimization problems and the effectively of the introduced improved strategies to JAYA. Note that, the source codes of the proposed EJAYA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/88877-enhanced-jaya-algorithm-for-global-optimization.
In this work, a new hybrid optimization algorithm (HWW-NM), which combines the Nelder-Mead local search algorithm with the water wave algorithm, is introduced to solve real-world engineering optimization problems. This paper is one of the first studies in which both the water wave algorithm and the HWW-NM are applied to processing parameters optimization for manufacturing processes. HWW-NM performance is measured using the cantilever beam problem. Additionally, a problem for milling manufacturing optimization is posed and solved to evaluate HWW-NM performance in real-world applications. The results reveal that HWW-NM is an attractive optimization approach for optimizing real-life problems.
Abrasive water jet cutting is one of the most prominent technique for the cutting of wide range of materials. Selection of the input process parameter with optimized condition determines the productivity and process applicability. Present paper describes the nature inspired meta-heuristic chemical reaction optimization (CRO) algorithm for the selection of input process parameter for the most favorable material removal rate (MRR). In the present paper ductile material model for the MRR is considered by CRO for the solution approach. Five input variables namely water jet pressure, diameter of nozzle, feed rate of nozzle, mass flow rate of abrasive and mass flow rate of water were considered for the material removal rate in abrasive water jet machining. It was found that CRO algorithms delivers improved performance compare to different algorithms such as genetic algorithm, cuckoo search, teaching learning-based optimization and teaching learning based cuckoo search algorithm. The predicted results can be used for the identification of the input process parameter to enhance outcome at the acceptable range for machining.
his article focuses on minimizing product costs by using the newly developed political optimization algorithm (POA), the Archimedes ‘optimization algorithm(AOA), and the Levy flight algorithm(LFA) in product development processes. Three structural optimization methods, size optimization, shape optimization, and topology optimization, are extensively applied to create inexpensive structures and render designs efficient. Using size, shape, and topology optimization in an integrated way, It is possible to obtain the most efficient structures in industry. The political optimization algorithm (POA) is a metaheuristic algorithm that can be used to solve many optimization problems. This study investigates the search capability and computational efficiency of POA for optimizing vehicle structures. By examining the results obtained, we prove the apparent superiority of the POA to other recent famous metaheuristics such as the Archimedes optimization algorithm and the Levy flight algorithm. The most important result of this paperwill be to provide an impressive aid for industrial companies to fill the gaps in their product design stages.
This article presents an implementation of one of the latest optimization methods of obtaining light vehicle designs. First, the problem of coupling with a bolted rim is optimized using the butterfly optimization algorithm (BOA). Finally, the BOA is used to solve the shape optimization of a vehicle suspension arm. It is utilized from the Kriging metamodeling method to obtain equations of objective and constraint functions in shape optimization. At the end of the research effort in this paper, the weight reduction of the suspension arm by using the BOA is 32.9 %. The results show the BOA’s ability to design better optimum components in the automotive industry.
Nimonic C263, a nickel-based superalloy, finds extensive application in manufacturing of complex parts in gas turbine, aircraft and automotive industries exclusively for exhaust sections. Machining of the alloy through traditional approach is a difficult task owing to its poor thermal conductivity, work hardening and high strength properties. However, the superalloy can be machined up to desired accuracy by non-traditional machining processes like electrical discharge machining (EDM). Yet again, machining of these difficult-to-machine materials not only consumes a significant amount of energy, but also produces unpleasant noise, hindering the sustainable aspect of the process. To meet the requisite of sustainable EDM and explore the machinability, an experimental investigation is proposed in this work, when Nimonic C263 superalloy is machined with three different electrodes, viz. copper, tungsten, and copper–tungsten. Outcomes of important process parameters, viz. voltage (U), discharge current (I), pulse-on-time (Ton), duty factor (τ), and electrode material, are studied on the responses, viz. specific energy consumption, machining noise (N), material removal rate, electrode wear rate, surface roughness (Ra) and radial overcut (C). Analysis of variance is conducted to analyse the influence of each parameter on the responses. A scanning electron microscope investigation is carried out to analyse the pre-machining and post-machining scenarios on the machined surface. The multiple responses are converted into single response by calculating the net outranking flow by application of preference ranking organization method for enrichment evaluation (PROMETHEE) approach. The PROMITHEE-based results are further improved by using a hybrid cuckoo search algorithm (combined PROMITHEE-based cuckoo search). A confirmative test is further conducted on the optimum machining conditions obtained by the hybrid approach (combination of PROMITHEE and cuckoo search) indicating an overall improvement of 6.02% for the responses, validating the proposed work.
Nature-inspired algorithms are commonly used for solving the various optimization problems. In past few decades, various researchers have proposed a large number of nature-inspired algorithms. Some of these algorithms have proved to be very efficient as compared to other classical optimization methods. A young researcher attempting to undertake or solve a problem using nature-inspired algorithms is bogged down by a plethora of proposals that exist today. Not every algorithm is suited for all kinds of problem. Some score over others. In this paper, an attempt has been made to summarize various leading research proposals that shall pave way for any new entrant to easily understand the journey so far. Here, we classify the nature-inspired algorithms as natural evolution based, swarm intelligence based, biological based, science based and others. In this survey, widely acknowledged nature-inspired algorithms namely- ACO, ABC, EAM, FA, FPA, GA, GSA, JAYA, PSO, SFLA, TLBO and WCA, have been studied. The purpose of this review is to present an exhaustive analysis of various nature-inspired algorithms based on its source of inspiration, basic operators, control parameters, features, variants and area of application where these algorithms have been successfully applied. It shall also assist in identifying and short listing the methodologies that are best suited for the problem.
In this study, a new metaheuristic optimization algorithm, called cuckoo search (CS), is introduced for solving structural optimization tasks. The new CS algorithm in combination with Lévy flights is first verified using a benchmark nonlinear constrained optimization problem. For the validation against structural engineering optimization problems, CS is subsequently applied to 13 design problems reported in the specialized literature. The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area. The optimal solutions obtained by CS are mostly far better than the best solutions obtained by the existing methods. The unique search features used in CS and the implications for future research are finally discussed in detail.
In highly competitive manufacturing industries nowadays, the manufactures ultimate goals are to produce high quality product with less cost and time constraints. To achieve these goals, one of the considerations is by optimizing the machining process parameters such as the cutting speed, depth of cut, radial rake angle. Recently, alternative to conventional techniques, evolutionary optimization techniques are the new trend for optimization of the machining process parameters. This paper gives an overview and the comparison of the latest five year researches from 2007 to 2011 that used evolutionary optimization techniques to optimize machining process parameter of both traditional and modern machining. Five techniques are considered, namely genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), ant colony optimization (ACO) and artificial bee colony (ABC) algorithm. Literature found that GA was widely applied by researchers to optimize the machining process parameters. Multi-pass turning was the largest machining operation that deals with GA optimization. In terms of machining performance, surface roughness was mostly studied with GA, SA, PSO, ACO and ABC evolutionary techniques.
The optimum selection of process parameters plays a significant role to ensure quality of product, to reduce the machining cost and to increase the productivity of any machining process. This paper presents the optimization aspects of process parameters of three machining processes including an advanced machining process known as abrasive water jet machining process and two important conventional machining processes namely grinding and milling. A recently developed advanced optimization algorithm, teaching–learning-based optimization (TLBO), is presented to find the optimal combination of process parameters of the considered machining processes. The results obtained by using TLBO algorithm are compared with those obtained by using other advanced optimization techniques such as genetic algorithm, simulated annealing, particle swarm optimization, harmony search, and artificial bee colony algorithm. The results show better performance of the TLBO algorithm.
In this research, a new optimization algorithm, called the cuckoo search algorithm (CS) algorithm, is introduced for solving manufacturing optimization problems. This research is the first application of the CS to the optimization of machining parameters in the literature. In order to demonstrate the effectiveness of the CS, a milling optimization problem was solved and the results were compared with those obtained using other well-known optimization techniques like, ant colony algorithm, immune algorithm, hybrid immune algorithm, hybrid particle swarm algorithm, genetic algorithm, feasible direction method, and handbook recommendation. The results demonstrate that the CS is a very effective and robust approach for the optimization of machining optimization problems.
In this study, a recently developed metaheuristic optimization algorithm, the Firefly Algorithm (FA), is used for solving mixed continuous/discrete structural optimization problems. FA mimics the social behavior of fireflies based on their flashing characteristics. The results of a trade study carried out on six classical structural optimization problems taken from literature confirm the validity of the proposed algorithm. The unique search features implemented in FA are analyzed, and their implications for future research work are discussed in detail in the paper.
In this study, a new metaheuristic optimization algorithm, called cuckoo search (CS), is introduced for solving structural optimization tasks. The new CS algorithm in combination with Lévy flights is first verified using a benchmark nonlinear constrained optimization problem. For the validation against structural engineering optimization problems, CS is subsequently applied to 13 design problems reported in the specialized literature. The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area. The optimal solutions obtained by CS are mostly far better than the best solutions obtained by the existing methods. The unique search features used in CS and the implications for future research are finally discussed in detail.
In this paper, we intend to formulate a new meta-heuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.
This paper presents a new hybrid optimization approach based on immune algorithm and hill climbing local search algorithm. The purpose of the present research is to develop a new optimization approach for solving design and manufacturing optimization problems. This research is the first application of immune algorithm to the optimization of machining parameters in the literature. In order to evaluate the proposed optimization approach, single objective test problem, multi-objective I-beam and machine-tool optimization problems taken from the literature are solved. Finally, the hybrid approach is applied to a case study for milling operations to show its effectiveness in machining operations. The results of the hybrid approach for the case study are compared with those of genetic algorithm, the feasible direction method and handbook recommendation.
A new metaheuristic optimisation algorithm, called Cuckoo Search (CS), was developed recently by Yang and Deb (2009). This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions. We then apply the CS algorithm to solve engineering design optimisation problems, including the design of springs and welded beam structures. The optimal solutions obtained by CS are far better than the best solutions obtained by an efficient particle swarm optimiser. We will discuss the unique search features used in CS and the implications for further research. Comment: 14 pages; Yang, X.-S., and Deb, S. (2010), Engineering Optimisation by Cuckoo Search
In this paper, we intend to formulate a new metaheuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.
An experimental and theoretical investigation was conducted to study the cutting of ductile metals with high-velocity abrasive jets. The investigation involved experimental cutting tests, visualization experiments, and model development. Data were generated to study the effects of abrasive-jet parameters on the depth and quality of cuts produced. These parameters included waterjet pressure, waterjet diameter, abrasive material, particle size, abrasive flow rate, traverse rate, and number of passes. The penetration process was found to be cyclic and to consist of more than one cutting regime as the kerf developed.
This new model is based on an improved model of erosion by solid particle impact, which is also presented. The erosion model accounts for the physical and geometrical characteristics of the eroding particle and results in a velocity exponent of 2.5, which is in agreement with erosion data in the literature. The erosion model is used with a kinematic jet-solid penetration model to yield expressions for depths of cut according to different modes of erosion along the cutting kerf. The predictions of the AWJ cutting model are checked against a large database of cutting results for a wide range of parameters and metal types.
A new optimisation algorithm which hybridises cuckoo search CS with teaching-learning-based optimisation TLBO is proposed for solving unconstrained optimisation problems. The new algorithm involves the concept of Lévy flight of the solutions and the information exchange based on teaching-learning process between the best solutions. The proposed method, combining the advantage of CS and TLBO, can strengthen the local search ability and accelerate the convergence rate. The effectiveness and performance of the method is evaluated on several large scale non-linear benchmark functions with different characteristics, and the results are compared with CS and TLBO. The experimental results show that the proposed algorithm outperforms other two algorithms and has achieved satisfactory improvement.
In this paper, a hybrid genetic algorithm with flexible allowance technique (GAFAT) is proposed for solving constrained engineering design optimization problems by fusing center based differential crossover (CBDX), Levenberg-Marquardt mutation (LMM) and non-uniform mutation (NUM). Inheriting the merits of mutation of differential evolution (DE), the proposed CBDX is a multi-parent recombination operator for generating offspring based on a parent vector and two parent center vectors. As an improvement of the gradient-based mutation, the proposed LMM is more numerically stable when enhancing the feasibility of the new individuals. To enrich the population diversity, NUM is incorporated into the hybrid algorithm. In addition, a flexible allowance technique (FAT) is designed and used in the hybrid algorithm to balance the selection of bad feasible solutions and good infeasible solutions. The proposed GAFAT is first tested based on the 13 widely used benchmark functions, which shows that GAFAT is of better or competitive performances when compared with six existing algorithms. The, GAFAT is applied to solve six well-known constrained engineering design problems, which also shows that GAFAT is of superior searching quality with fewer evaluation times than other algorithms. Finally, GAFAT is successfully applied to solve a real pipe frequency improvement problem.
In the milling process, the selection of machining parameters is very important as these parameters determine the processing time, quality, cost and so on, especially in the high-accuracy machine tools. However, the parameters optimization of a multi-pass milling process is a nonlinear constrained optimization problem which is difficult to be solved by the traditional optimization techniques. Therefore, in order to solve this problem effectively, this paper proposes a novel parameters optimization method based on the cellular particle swarm optimization (CPSO). To address the constraints efficiently, the proposed method combines two constraints handling techniques, including the penalty function method and the constraints handling strategy of PSO. In the proposed CPSO, the smart cell constructs its neighborhood with self-adaptive function and constraints handling techniques, which guide the unfeasible particles to move to the feasible regions and search for better solutions. A case is adopted and solved to illustrate the effectiveness of the proposed CPSO algorithm. The results of the experiment study are analyzed and compared with those of the previous algorithms. The experimental results show that the proposed approach outperforms other algorithms and has achieved significant improvement.
Several constrained and unconstrained optimization problems have been adequately solved over the years thanks to advances in the metaheuristics area. In the last decades, different metaheuristics have been proposed employing new ideas, and hybrid algorithms that improve the original metaheuristics have been developed. One of the most successfully employed metaheuristics is the Differential Evolution. In this paper it is proposed a Multi-View Differential Evolution algorithm (MVDE) in which several mutation strategies are applied to the current population to generate different views at each iteration. The views are then merged according to the winner-takes-all paradigm, resulting in automatic exploration/exploitation balance. MVDE was tested to solve a set of well-known constrained engineering design problems and the obtained results were compared to those from many state-of-the-art metaheuristics. Results show that MVDE was very competitive in the considered problems, largely outperforming several of the compared algorithms.
A micro-computer-based optimization technique has been developed to optimize grinding conditions, viz. wheel speed, workpiece speed, depth of dressing, and lead of dressing, using a multi-objective function model with a weighted approach for surface grinding. The technique evaluates the production cost, production rate and surface finish for the optimum grinding conditions, subject to constraints such as thermal damage, wheel-wear parameters, machine-tool stiffness, and either surface finish or production rate.A computer program written in Fortran has been developed for the optimization computations. It accesses successive quadratic programming sub-routines to solve the non-linear objective with multi-constraints. The program runs in an interactive mode. The user is prompted to input all the constants related to the grinding process, workpiece and grinding wheel for the necessary computations. The user can also alter specific input values to perform sensitivity analyses of the relative contributions of the individual grinding parameters to the weighted objective function. Furthermore, an initial estimation of grinding conditions, based on experience, can be used to start the optimization iterations. Two case studies are presented to illustrate how the program can be used to give optimum production rate, low production cost and fine surface quality for the surface grinding process.
This paper presents a nonlinear response surface-based safety optimisation and robustness process. The stepwise regression and optimal Latin hyper cube sampling methods are employed to construct the ''efficient-to-compute'' surrogate model. A sequential quadratic programming method with mixed type of variables is employed for the design optimisation. A reliability based design optimisation model for robust system parameter design of vehicle safety is proposed and a Monte Carlo based stochastic simulation is used to perform the robustness assessment and the reliability-driven robust design. The methodology has been applied to the vehicle crash safety design of side impact. It shows that the vehicle weight can be significantly reduced with an improved safety performance and with a higher level of confidence. CAE simulation is used to validate the optimal results.
Selection of machining parameters in any machining process significantly affects the production rate, quality, and cost of a component. This paper presents the multi-objective optimization of process parameters of a grinding process using various non-traditional optimization techniques such as artificial bee colony, harmony search, and simulated annealing algorithms. The objectives considered in the present work are production cost, production rate, and surface finish subjected to the constraints of thermal damage, wheel wear, and machine tool stiffness. The process variables considered for optimization are wheel speed, workpiece speed, depth of dressing, and lead of dressing. The results of the algorithms presented are compared with the previously published results obtained by using other optimization techniques.
Grinding is one of the very important machining operations in engineering industries. Optimization of grinding processes still remains as one of the most challenging problems because of its high complexity and non-linearity. This makes the application of traditional optimization algorithms quite limited. Hence, there is a need to apply most recent and powerful optimization techniques to get desired accuracy of optimum solution. In this paper, a recently developed nontraditional optimization technique, particle swarm optimization (PSO) algorithm is presented to find the optimal combination of process parameters of grinding process. The objectives considered in the present work are, production cost, production rate, and surface finish subjected to the constraints of thermal damage, wheel wear, and machine tool stiffness. The process variables considered for optimization are wheel speed, workpiece speed, depth of dressing, and lead of dressing. The results of the algorithm are compared with the previously published results obtained by using other traditional optimization techniques.
In this paper, a modified generic algorithm is proposed based on the traditional genetic algorithm, he operating domain is defined and changed to be around the optimal point in its evolutionary processes so that the convergence speed and accuracy are improved. The modified genetic algorithm is used for the optimisation of milling parameters and simulation and experimental results show an improved performance.
An ant colony based optimisation procedure has been developed to optimise grinding conditions, viz. wheel speed, workpiece speed, depth of dressing and lead of dressing, using a multi-objective function model with a weighted approach for the surface grinding process. The procedure evaluates the production cost and production rate for the optimum grinding condition, subjected to constraints such as thermal damage, wheel wear parameters, machine tool stiffness and surface finish. The results are compared with Genetic Algorithm (GA) and Quadratic Programming (QP) techniques.
The effective optimization of machining process parameters affects dramatically the cost and production time of machined components as well as the quality of the final products. This paper presents optimization aspects of a multi-pass milling operation. The objective considered is minimization of production time (i.e. maximization of production rate) subjected to various constraints of arbor strength, arbor deflection, and cutting power. Various cutting strategies are considered to determine the optimal process parameters like the number of passes, depth of cut for each pass, cutting speed, and feed. The upper and lower bounds of the process parameters are also considered in the study. The optimization is carried out using three non-traditional optimization algorithms namely, artificial bee colony (ABC), particle swarm optimization (PSO), and simulated annealing (SA). An application example is presented and solved to illustrate the effectiveness of the presented algorithms. The results of the presented algorithms are compared with the previously published results obtained by using other optimization techniques.
This paper outlines the development of an optimization strategy to determine the optimum cutting parameters for multi-tool milling operations like face milling, corner milling, pocket milling and slot milling. The developed strategy based on the maximum profit rate criterion and incorporates five technological constraints. In this paper, optimization procedures based on the genetic algorithm, hill climbing algorithm and memetic algorithm were demonstrated for the optimization of machining parameters for milling operation. This paper describes development and utilization of an optimization system, which determines optimum machining parameters for milling operations. An objective function based on maximum profit in milling operation has been developed. Results obtained are used in NC machine. An example has been presented at the end of the paper to give clear picture from the application of the system and its efficiency. The results are compared and analyzed with method of feasible directions and handbook recommendations.
This study presents a harmony search (HS) algorithm to determine the optimum cutting parameters for multi-pass face-milling. The optimum value of machining parameters including number of passes, depth of cut in each pass, speed and feed is obtained to minimize total production cost while considering technological constraints such as allowable speed, feed, surface finish, tool life and machine tool capabilities. An illustrative example is used to demonstrate the ability of the HS algorithm and for validation purpose, the genetic algorithm (GA) is used to solve the same problem. Comparison of the results reveals that the HS algorithm converges to optimum solution with higher accuracy in comparison with GA.
This paper proposes a new optimization technique based on the ant colony algorithm for solving multi-pass turning optimization problems. The cutting process has roughing and finishing stages. The machining parameters are determined by minimizing the unit production cost, subject to various practical machining constraints. The results indicate that the proposed ant colony framework is effective compared to other techniques carried out by different researchers.
In a Computer-Aided Process Planning (CAPP) system, one of the important steps is the selection of machining parameters which yield optimum results. In this paper, a face-milling operation has been considered. The machining parameters such as number of passes, depth of cut in each pass, speed and feed are obtained using a genetic algorithm, to yield minimum total production cost while considering technological constraints such as allowable speed and feed, dimensional accuracy, surface finish, tool wear and machine tool capabilities.
This paper outlines the development of an optimization strategy to determine the optimum cutting parameters for multipass milling operations like plain milling and face milling. The developed strategy is based on the “maximum production rate” criterion and incorporates eight technological constraints. The optimum number of passes is determined via dynamic programming, and the optimal values of the cutting conditions are found based on the objective function developed for the typified criterion by using a non-linear programming technique called “geometric programming”. This paper also underlies the importance of using optimization strategies rather than handbook recommendations as well as pointing out the superiority of the multipass over the single-pass optimization approach.
This paper presents an approach to select the optimal machining parameters for multi-pass milling. It is based on two recent approaches, genetic algorithm (GA) and simulated annealing (SA), which have been applied to many difficult combinatorial optimization problems with certain strengths and weaknesses. In this paper, a hybrid of GA and SA (GSA) is presented to use the strengths of GA and SA and overcome their weaknesses. In order to improve, the performance of GSA further, the parallel genetic simulated annealing (PGSA) has been developed and used to optimize the cutting parameters for multi-pass milling process. For comparison, conventional parallel GA (PGA) is also chosen as another optimization method. An application example that has been solved previously using the geometric programming (GP) and dynamic programming (DP) method is presented. From the given results, PGSA is shown to be more suitable and efficient for optimizing the cutting parameters for milling operation than GP+DP and PGA.
A genetic algorithm (GA) based optimization procedure has been developed to optimize grinding conditions, viz. wheel speed, workpiece speed, depth of dressing and lead of dressing, using multi-objective function model with a weighted approach for surface grinding process. The procedure evaluates the production cost and production rate for the optimum grinding condition, subjected to constraints such as thermal damage, wheel wear parameters, machine tool stiffness and surface finish. New GA procedure is illustrated with an example and optimum results such as production cost, surface finish, metal removal rate are compared with quadratic programming techniques.
The paper proposes a new optimization technique based on Tribes for determination of the cutting parameters in multi-pass milling operations such as plain milling and face milling by simultaneously considering multi-pass rough machining and finish machining. The optimum milling parameters are determined by minimizing the maximumproductionrate criterion subject to several practical technological constraints. The cutting model formulated is a nonlinear, constrained programming problem. Experimental results show that the proposed Tribes-based approach is both effective and efficient.
A new efficient optimization method, called ‘Teaching–Learning-Based Optimization (TLBO)’, is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the ‘Teacher Phase’ and the second part consists of the ‘Learner Phase’. ‘Teacher Phase’ means learning from the teacher and ‘Learner Phase’ means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems.
An efficient optimization method called ‘Teaching–Learning-Based Optimization (TLBO)’ is proposed in this paper for large scale non-linear optimization problems for finding the global solutions. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods.
Optimization and robustness for crashworthiness
- L Gu
- R J Yang
- C H Cho
- M Makowski
- M Faruque
- Y Li
L. Gu, R.J. Yang, C.H. Cho, M. Makowski, M. Faruque, Y. Li, Optimization and
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A novel hybrid immune algorithm for optimization of machining parameters in milling operations
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A.R. Yildiz, A novel hybrid immune algorithm for optimization of machining
parameters in milling operations, Robot. Comput.-Integr. Manuf. 25 (2) (2009)
261-270.
- M Hashish
M. Hashish, A model for abrasive water jet (AWJ) machining, Trans. ASME: J.
Eng. Mater. Technol. 111 (1989) 154-162.