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A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques
Abstract
. This paper presents a critical review of the most important evolutionary-based multiobjective optimization techniques developed over the years, emphasizing the importance of analyzing their Operations Research roots as a way to motivate the development of new approaches that exploit the search capabilities of evolutionary algorithms. Each technique is briefly described mentioning its advantages and disadvantages, their degree of applicability and some of their known applications. Finally, the future trends in this discipline and some of the open areas of research are also addressed. Keywords: multiobjective optimization, multicriteria optimization, vector optimization, genetic algorithms, evolutionary algorithms, artificial intelligence. 1 Introduction Since the pioneer work of Rosenberg in the late 60s regarding the possibility of using genetic-based search to deal with multiple objectives, this new area of research (now called evolutionary multiobjective optimization) has grown c...
... Figure 5. An example of a problem with two objective functions (Coello, 1999). ...
... ............. An example of a problem with two objective functions(Coello, 1999). ................. Product scheme. ...
Due to awakening environmental awareness and corresponding tightening of environmental protocols in the industrialized world, new production challenges arise. These challenges are to meet the continuously growing worldwide demand for capital and consumer goods while considering the associated economic, environmental, and social aspects. The next generation manufacturing systems must adjust themselves rapidly and cost-effectively. The goal is to respond to changing market needs while minimizing adverse effects on the environment. Reconfigurable Manufacturing Systems (RMSs) —due to its flexibility and characteristics— can increase the system sustainability and responsiveness to satisfy the market needs. In this work, we address an environmental oriented multiobjective process plan generation problem for a sustainable reconfigurable manufacturing system (SRMS). As design objectives to minimize, we consider a sustainability-metric value, the total production time and the total production cost. The sustainability-metric value considers both liquid hazardous waste and greenhouse gas emissions (GHG). First, we model the problem as a multiobjective integer linear programming (MOILP). Second, due to its complexity, we adapt three approaches respectively a posteriori approach called augmented ε-constraint (AUGECON) and two evolutionary approaches namely, non-dominated sorting genetic algorithm II (NSGA-II) and strength pareto evolutionary algorithm II (SPEA-II) to tackle the problem. Finally, due to the lack of real world data related to our problem and to illustrate the applicability of our approaches, a simple numerical example is presented, and the numerical results are analyzed.
... Then, the goal of solving a given MOP is to find the Pareto optimal set (P * ) from the feasible region (F), defined by (2) and (3). ...
... This represents a clear advantage with respect to traditional mathematical programming techniques, which usually produce a single solution per execution. MOEAs are also more general in the sense that they require little domainspecific information and do not impose requirements on the objective functions (e.g., they don't need to be differentiable nor being defined in algebraic form [3]). ...
... Evolutionary Algorithm (EA) is then used addressing multi-objective optimization problems, simultaneously considering multiple optimization objectives and seeking a balance between them [40,41]. In previous related studies, evolutionary algorithms have been used to generate building layouts and optimize these results by setting specific objectives [41,42]. ...
Feng Shui principles have a profound impact in Asia, studies have shown that consumers often consider Feng Shui when purchasing property to arrange interior layouts. Balancing design requirements and cultural beliefs in the design process requires significant communication and calculation efforts, However, aside from repeated communication among Feng Shui experts, homeowners, and designers, there is currently a lack of efficient methods to incorporate Feng Shui into design. Therefore, this study establishes a decision model to provide layout recommendations for purchase property, design, and for existing property renovation planning. By references Feng Shui Compass School principles to assess the Feng Shui quality of dwelling interiors and considers spatial layout and area distribution rules to evaluate the feasibility of the solution. Multi-Objective Evolutionary Algorithm (MOEA) is then applied to optimize Feng Shui and design conditions in real-world case studies. The results show that the application can effectively optimize and balance Feng Shui and design conditions in a short period of time, also provides homeowners and designers with clear strategies during purchase, design, and renovation to meet the needs related to cultural beliefs.
... However, the clearing radius σ is usually unknown in many optimization problems [74], making it difficult to determine an appropriate value without prior knowledge or extensive tuning. To address this challenge, we borrow the concept of a dynamic fitness sharing method proposed by Tan et al. [75], which adaptively adjusts the clearing radius based on the characteristics of the population at each generation. ...
Autonomous Driving Systems (ADSs) rely on Deep Neural Networks, allowing vehicles to navigate complex, open environments. However, the unpredictability of these scenarios highlights the need for rigorous system-level testing to ensure safety, a task usually performed with a simulator in the loop. Though one important goal of such testing is to detect safety violations, there are many undesirable system behaviors, that may not immediately lead to violations, that testing should also be focusing on, thus detecting more subtle problems and enabling a finer-grained analysis. This paper introduces Cooperative Co-evolutionary MEtamorphic test Generator for Autonomous systems (CoCoMEGA), a novel automated testing framework aimed at advancing system-level safety assessments of ADSs. CoCoMEGA combines Metamorphic Testing (MT) with a search-based approach utilizing Cooperative Co-Evolutionary Algorithms (CCEA) to efficiently generate a diverse set of test cases. CoCoMEGA emphasizes the identification of test scenarios that present undesirable system behavior, that may eventually lead to safety violations, captured by Metamorphic Relations (MRs). When evaluated within the CARLA simulation environment on the Interfuser ADS, CoCoMEGA consistently outperforms baseline methods, demonstrating enhanced effectiveness and efficiency in generating severe, diverse MR violations and achieving broader exploration of the test space. These results underscore CoCoMEGA as a promising, more scalable solution to the inherent challenges in ADS testing with a simulator in the loop. Future research directions may include extending the approach to additional simulation platforms, applying it to other complex systems, and exploring methods for further improving testing efficiency such as surrogate modeling.
... These algorithms are used to explore a large solution space while optimizing multiple objectives. Genetic algorithms are search methods based on natural selection and genetic processes and are widely used to explore the Pareto front in multi-objective optimization problems [44]. ...
The seismic design of structures is an emerging practice in earthquake-resistant construction. Therefore, using energy-dissipation devices and optimizing these devices for various purposes are important. Evolutionary computation, nature-inspired, and meta-heuristic algorithms have been studied more in recent years for the optimization of these devices. In this study, the development of evolutionary algorithms for seismic design in the context of multi-objective optimization is examined through bibliometric analysis. In particular, evolutionary algorithms such as genetic algorithms and particle swarm optimization are used to optimize the performance of structures to meet seismic loads. While genetic algorithms are used to improve both the cost and seismic performance of the structure, particle swarm optimization is used to optimize the vibration and displacement performance of structures. In this study, a bibliometric analysis of 661 publications is performed on the Web of Science and Scopus databases and on how the research in this field has developed since 1986. The R-studio program with the biblioshiny package is used for the analyses. The increase in studies on the optimization of energy dissipation devices in recent years reveals the effectiveness of evolutionary algorithms in this field.
... solutions, which is called Pareto front. 58 An initial population is generated at the first step and then the objective functions are evaluated using obtained polynomials. To choose and decrease the number of individuals, the Pareto ranking method is applied. ...
The performance of an atmosphere-breathing electric propulsion (ABEP) intake has been investigated with a focus on the direct simulation Monte Carlo (DSMC) method. A numerical dataset was derived from extensive DSMC analysis of rarefied flow across various intake configurations. The intake geometry, based on a concept from the literature, comprises a cylindrical body with four annular coaxial channels and a conical convergent diffuser. By maintaining the aspect ratio of the coaxial channels, the DSMC simulations were performed by changing three key parameters: inlet area, convergent diffuser angle, and operating discharge voltage, at altitudes ranging from 140 to 200 km. The analysis of the ABEP system revealed that altitude has the most significant influence on the discharge power, while the effects of the diffuser angle and inlet area are comparatively smaller. Analysis at fixed altitudes reveals a strong influence of altitude on discharge power, while the diffuser angle and the inlet area play a minor role. The results also show that the sensitivity of the discharge power to the diffuser angle increases as the altitude approaches the highest level of 200 km. Furthermore, an evolutionary-based optimization methodology was applied, taking into account the requirements of a drag-to-thrust ratio of less than 1 and a discharge power of less than 12 kW. Optimization analysis in the full altitude range revealed that the optimal diffuser angle falls within the narrow range of 15°– 20°, corresponding to an optimal operating altitude range of 170– 178 km.
... While there are numerous types of multiobjective GAs, the aforementioned algorithms are widely recognized and have been extensively studied across different applications through comparative studies. Several survey studies [39,[51][52][53] on evolutionary multiobjective optimization have been published. Ref. [54] proposed a novel ε-dominance MOEA (EDMOEA) that employs steady-state replacement and pair-comparison selection instead of Pareto ranking. ...
Minimizing job waiting time for completing related operations is a critical objective in industries such as chemical and food production, where efficient planning and production scheduling are paramount. Addressing the complex nature of flow shop scheduling problems, which pose significant challenges in the manufacturing process due to the vast solution space, this research employs a novel multiobjective genetic algorithm called distance from ideal point in genetic algorithm (DIPGA) to identify Pareto-optimal solutions. The effectiveness of the proposed algorithm is benchmarked against other powerful methods, namely, NSGA, MOGA, NSGA-II, WBGA, PAES, GWO, PSO, and ACO, using analysis of variance (ANOVA). The results demonstrate that the new approach significantly improves decision-making by evaluating a broader range of solutions, offering faster convergence and higher efficiency for large-scale scheduling problems with numerous jobs. This innovative method provides a comprehensive listing of Pareto-optimal solutions for minimizing makespan and total waiting time, showcasing its superiority in addressing highly complex problems.
... It is particularly effective in testing curvilinear effects, offering a nuanced view that common linear assumptions fail to capture. MOGA is a robust approach for addressing conflicting objectives [15]. It evaluates various conflicting goals, prioritizing them based on significance. ...
With the progression of digital transformation in the workplace, the use of enterprise social media has become a daily routine in contemporary organizations. In the course of this transition, securing enterprise social media for both efficiency and individual well-being is pivotal as it steers digital transformation towards a sustainable future. Despite the huge benefits, the impact of enterprise social media on individuals is often seen as a double-edged sword, posing a managerial dilemma to organizations. To address this issue, our research developed a hybrid method aiming at maximizing efficiency and protecting employees’ psychological well-being with neither target being compromised. Polynomial regression with response surfaces was employed to visually elucidate the variations in work engagement and work exhaustion, thereby identifying the conditions for optimal values of work engagement. We then transformed the conflicting outcome variables into a single optimization goal. By calculating the equilibrium point and comparing various predictor limits, we determined an optimal condition to achieve both targets. Specifically, the equilibrium point is identified when employees’ psychological detachment slightly exceeds enterprise social media use. The optimal condition can be identified when two predictors are symmetrically aligned with each other. Our method demonstrates that a congruence framework of enterprise social media use is conducive to both efficiency and well-being, challenging the existing assertion that moderate usage is most favorable and questioning linear relationship assumptions. This study extends the innovative application of optimization techniques to broader managerial domains and provides practical solutions for reconciling the contradictory effects between well-being and efficiency, thereby promoting the sustainable success of enterprise social media.
... For nanofluid, the viscosity model and thermal conductivity can be defined as [19] = (1− ̅ ) ...
This study presents the mathematical model of entropy generation on MHD peristaltic wave of Nanofluid. The governing equations have been developed by the assumption of low Reynold’s number and long wavelength approximation. The analytical solution has been obtained with the help of perturbation method. The expression of temperature profile, pressure distribution and friction forces are presented graphically for some significant parameters. Further, the results of correlation and regression between the entropy generation and some other parameters have been plotted. It is very important to find the sensitivity of each parameter on entropy generation. Findings of regression analysis show that 81% of the variability of entropy generation for magnetic parameter, 99% of the variability of entropy generation for Brownian motion parameter, 40% of the variability of entropy generation for Thermophoresis parameter and 100% of the variability of entropy generation for Brinkmann is accounted for by the variable Iv. Similarly, a decrease of 2.562 in entropy generation for the various values of the independent variable Magnetic parameter, an increase of 2.029 in entropy generation for the values of Brownian motion, an increase of 6.307 in entropy generation for Thermophoresis and 68.492 in entropy generation scores for Brinkmann on every one-unit increase in Iv.
... Another example of the use of optimization algorithms may be to simultaneously increase the safety and usability of a structure while minimizing its production costs [34,35]. Multi-criteria optimization algorithms allow the determination of the most optimal solutions even in contradictory conditions [36], although it is always some kind of compromise resulting from the minimization of the objective function [37,38]. Optimization algorithms allow searching the solution space regardless of the class of the problem being considered and are successfully used to optimize issues such as composite structures [39], design of high-rise buildings [40] or adjacent buildings [41], or to find optimal locations for shock absorbers in construction system [42]. ...
The work compares the design of phononic structures using two types of optimization algorithms. Using the genetic algorithm and the simulated annealing algorithm, optimal layer distributions were obtained in which the phononic band gap phenomenon occurs. The mechanical wave propagating in the obtained structure, for the given frequency ranges, significantly reduces the transmitted energy, thanks to which the building facade or monument located behind the obtained barrier is exposed to much smaller vibrations, which significantly reduces damage related to long-term fatigue load. The mechanical wave propagation was modeled using the Transfer Matrix Method algorithm and the proprietary objective function allows for the reduction of wave transmission with the simultaneous reduction of high transmission peaks with small half-widths.
... Depending on each objective independently, VEPSO selects portions of the future generation from the older generation. However, choosing people who excel in one area without considering the other areas raises the issue of eliminating those with average performance, who might be very helpful in finding compromise solutions 43 . In 44,45 , the authors have proposed solutions for enhancements in evolutionary algorithms and optimization techniques 44 outlines a strategy for improving the effectiveness of SAEAs (Surrogate-assisted evolutionary algorithms) utilizing unevaluated solutions. ...
This work implements the recently developed nth state Markovian jumping particle swarm optimisation (PSO) algorithm with local search (NS-MJPSOloc) awareness method to address the economic/environmental dispatch (EED) problem. The proposed approach, known as the Non-dominated Sorting Multi-objective PSO with Local Best (NS-MJPSOloc), aims to enhance the performance of the PSO algorithm in multi-objective optimisation problems. This is achieved by redefining the concept of best local candidates within the search space of multi-objective optimisation. The NS-MJPSOloc algorithm uses an evolutionary factor-based mechanism to identify the optimum compromise solution, a Markov chain state jumping technique to control the Pareto-optimal set size, and a neighbourhood’s topology (such as a ring or a star) to determine its size. Economic dispatch refers to the systematic allocation of available power resources in order to fulfill all relevant limitations and effectively meet the demand for electricity at the lowest possible operating cost. As a result of heightened public consciousness regarding environmental pollution and the implementation of clean air amendments, nations worldwide have compelled utilities to adapt their operational practises in order to comply with environmental regulations. The (NS-MJPSOloc) approach has been utilised for resolving the EED problem, including cost and emission objectives that are not commensurable. The findings illustrate the efficacy of the suggested (NS-MJPSOloc) approach in producing a collection of Pareto-optimal solutions that are evenly dispersed within a single iteration. The comparison of several approaches reveals the higher performance of the suggested (NS-MJPSOloc) in terms of the diversity of the Pareto-optimal solutions achieved. In addition, a measure of solution quality based on Pareto optimality has been incorporated. The findings validate the effectiveness of the proposed (NS-MJPSOloc) approach in addressing the multi-objective EED issue and generating a trade-off solution that is both optimal and of high quality. We observed that our approach can reduce ∼6.4% of fuel costs and ∼9.1% of computational time in comparison to the classical PSO technique. Furthermore, our method can reduce ∼9.4% of the emissions measured in tons per hour as compared to the PSO approach.
... The principle behind constraint handling techniques (CHT) is how to combine the optimization process and the constraint satisfaction. There are two main ways to impose constraint handling: indirect constraint handling transforms constraint objectives into optimization objectives; while direct constraint handling explicitly enforces the fulfillment of constraints during the local search (Coello Coello, 1999;Liang et al., 2023). In this work, the selected CHT has been the indirect constraint handling. ...
This research addresses the crucial issue of pollution from aircraft operations, focusing on optimizing both gate allocation and runway scheduling simultaneously, a novel approach not previously explored. The study presents an innovative genetic algorithm‐based method for minimizing pollution from fuel combustion during aircraft take‐off and landing at airports. This algorithm uniquely integrates the optimization of both landing gates and take‐off/landing runways, considering the correlation between engine operation time and pollutant levels. The approach employs advanced constraint handling techniques to manage the intricate time and resource limitations inherent in airport operations. Additionally, the study conducts a thorough sensitivity analysis of the model, with a particular emphasis on the mutation factor and the type of penalty function, to fine‐tune the optimization process. This dual‐focus optimization strategy represents a significant advancement in reducing environmental impact in the aviation sector, establishing a new standard for comprehensive and efficient airport operation management.
... Process parameter optimization plays a major role in mass production process parameter selection. Previous publications [29][30][31][32] have reported on a variety of optimization methodologies for the process parametric optimization of the FFF process. Filament extrusion is done using a variety of extruders. ...
The first aim of this work is to produce a small-scale filament extruder. The produced filament should be suitable for Fused Deposition Modeling (FDM) 3D printers. The filament production is not common and only made by several manufacturers around the world. The cheapest filament extruder machine on the market is still expensive compared to the 3D printer itself. Specifically, this paper describes the design, working principle and structure of a compacted thermo-plastic extrusion machine. One of the additive manufacturing processes used for the manufacture of functional and nonfunctional prototypes is fused filament fabrication (FFF), also known as freeform filament fabrication. FFF process settings have been shown to have a considerable impact on the mechanical, thermal, surface, morphological, and tribological properties of 3D printed objects in earlier research. The second aim of this research is to investigate the FFF process parameters for printing UHMWPE / HAP + TiO 2 composite filament. Four main process parameters for the FFF process were adjusted in this study: infill %, bed temperature, extruder temperature, and outer perimeter. The ultimate tensile strength of the 3D printed UHMWPE / HAP + TiO 2 prototypes (according to ASTM 638 type IV) was investigated using a universal tensile tester. The study's findings imply that the ultimate tensile strength can be maximized with a 100% infill percentage, 60 O C bed temperature, 210 O C extruding temperature, and 5 outer perimeters. The other goal of this study is to replace the filament extrusion head of the 3D printer with a single screw extruder for printing the composite particle directly without the filament processing. Finally use the optimized parameter to print the same prototype directly and compare the results.
... Depending on each objective independently, VEPSO selects portions of the future generation from the older generation. However, choosing people who excel in one area without considering the other areas raises the issue of eliminating those with average performance, who might be very helpful in finding compromise solutions 43 . Table 1. ...
This work applies the recently developed n th state Markovian jumping particle swarm optimisation algorithm with local search (NS-MJPSOloc) awareness method to address the economic/environmental dispatch (EED) problem. The proposed approach, known as the Non-dominated Sorting Multi-objective Particle Swarm Optimisation with Local Best (NS-MJPSOloc), aims to enhance the performance of the Particle Swarm Optimisation (PSO) algorithm in multi-objective optimisation problems. This is achieved by redefining the concept of best local candidates within the search space of the multi-objective optimisation. Economic dispatch refers to the systematic allocation of available power resources in order to fulfil all relevant limitations and effectively meet the demand for electricity at the lowest possible operating cost. As a result of heightened public consciousness regarding environmental pollution and the implementation of clean air amendments, nations worldwide have compelled utilities to adapt their operational practises in order to comply with environmental regulations. The (NS-MJPSOloc) approach has been utilised for the resolution of the EED problem, including cost and emission objectives that are not commensurable. Multiple optimisation iterations of the suggested methodology have been conducted on a conventional test setup. The objective of economic dispatch is to minimise the overall expenditure on fuel, without taking into account any limitations related to emissions. The emission dispatch prioritises the reduction of emissions without taking into account economic considerations. The findings illustrate the efficacy of the suggested (NS-MJPSOloc) approach in producing a collection of Pareto-optimal solutions that are evenly dispersed within a single iteration. The comparison of several approaches reveals the higher performance of the suggested (NS-MJPSOloc) in terms of the diversity of the Pareto-optimal solutions achieved. In addition, a measure of solution quality based on Pareto optimality has been incorporated. The findings validate the effectiveness of the proposed (NS-MJPSOloc) approach in addressing the multi-objective EED issue and generating a trade-off solution that is both optimal and of high quality.
... Improvement in one of them leads to deterioration of the other objective functions. Therefore, there is not an optimal solution that optimizes all of the objective functions simultaneously, alternatively, there are a series of optimum solutions, which are known as Pareto fronts, and this feature is the main difference in the overall nature of MO problems with single-objective problems [87][88][89][90][91], and this set of solutions, which also includes single-objective solutions, illuminates the process of designing and choosing the optimal design. Therefore, MO problems are represented as finding the vector of design variables satisfying m equality and n inequality constraints, which are illustrated in Eqs. ...
Warm mix asphalt (WMA) additives have been proposed to overcome the high viscosity problem of crumb rubber modified asphalt (CRMA) binders. Various additives have been used to improve the low-temperature cracking performance of asphalt mixtures, but no study has been conducted to present the optimum additive content in CRMA binders in different loading modes. Therefore, this research aims to present the optimum content of nano calcium carbonate (NCC) to improve the fracture toughness and energy of asphalt rubber mixtures containing WMA additives (slack wax (SW) and polypropylene wax (PPW)). The semi-circular bend (SCB) fracture test was applied under pure mode I, mixed mode I-II, mixed mode II-I and pure mode II loadings at subzero temperature. Machine learning methods, including multivariate regression (MVR) and artificial neural network (ANN) models of group method of data handling (GMDH) and multilayer perceptron (MLP), were used to provide the prediction models of effective stress intensity factor (K eff) and fracture energy (G f). Finally, the multi-objective optimization of K eff and G f was performed to obtain optimum NCC content. The results indicated that in MVR model, the outputs had a small correlation with laboratory values, so that R value of MVR was 0.8406 and 0.8011 for K eff and G f , respectively. Also, it was revealed in MVR that NCC had the highest impact on K eff and G f significantly. GMDH model results showed that the relationships between predicted and laboratory values of K eff and G f are appropriately described with R value of 0.9546 and 0.9229 for K eff and G f models, respectively. In MLP model, different layer structures of the feed-forward neural network were developed to obtain the most accurate structure. It was indicated that MLP with 4-22-1 and 3-19-1 structures had a higher accuracy for K eff and G f prediction models with R value of 0.9951 and 0.9978, respectively. Finally, by the use of the best model relationships , the results of the multi-objective optimization indicated that 4.91% and 6.37% NCC were the design optimum contents resulting in a maximum of K eff and G f simultaneously for SW and PPW-modified CRMA mixtures. Moreover, the optimum NCC contents in loading modes of mode mixity (M e) of 1, 0.8, 0.4 and 0 were 3.62%, 5.12%, 4.08% and 3.42% for SW-modified CRMA mixtures and 4.35%, 6.51%, 7.19% and 2.84% for PPW-modified CRMA mixtures, respectively.
... Due to the conflict between these objective functions, an increase in one will affect the others. There is no single optimal solution with respect to all objective functions, but rather a set of solutions known as the Pareto front [39]. ...
A numerical data set will be developed to assist in the design of Savonius wind turbines. The main objective of study is to improve Savonius turbine blade designs to increase torque coefficients, rotational speeds, and pressure coefficients. Simulating 3D models and validating them with wind tunnel data were part of the experimental design methodology. Multi-objective optimization is used to optimize turbine performance. Twist angle, aspect ratio, and overlap ratio are all important factors in determining the optimal torque and power coefficients. Data-driven objective functions were modeled using the group method of data handling (GMDH). Using an evolutionary Pareto-based optimization approach, polynomial models were used to plot Pareto fronts and TOPSIS to calculate optimal commercial points. The torque coefficient, rotational speed, and power coefficient are all improved by 13.74%, 0.071%, and 5.32%, respectively. As a result of the multi-objective optimization of the turbine, some significant characteristics of objective functions were discovered.
... Evolutionary Multi-objective Optimisation [18,25,26], has seldom been used in the automatic configuration of DNNs and/or in the optimisation of their hyperparameters. Works on the latter include the approach proposed by Kim et al. [27], where the authors used two conflicting objectives, speed and accuracy, to be optimised via an EMO process using the NSGA-II. ...
Machine learning has rapidly evolved during the last decade, achieving expert human performance on notoriously challenging problems such as image classification. This success is partly due to the re-emergence of bio-inspired modern artificial neural networks (ANNs) along with the availability of computation power, vast labelled data and ingenious human-based expert knowledge as well as optimisation approaches that can find the correct configuration (and weights) for these networks. Neuroevolution is a term used for the latter when employing evolutionary algorithms. Most of the works in neuroevolution have focused their attention in a single type of ANNs, named Convolutional Neural Networks (CNNs). Moreover, most of these works have used a single optimisation approach. This work makes a progressive step forward in neuroevolution for vehicle trajectory prediction, referred to as neurotrajectory prediction, where multiple objectives must be considered. To this end, rich ANNs composed of CNNs and Long-short Term Memory Network are adopted. Two well-known and robust Evolutionary Multi-objective Optimisation (EMO) algorithms, NSGA-II and MOEA/D are also adopted. The completely different underlying mechanism of each of these algorithms sheds light on the implications of using one over the other EMO approach in neurotrajectory prediction. In particular, the importance of considering objective scaling is highlighted, finding that MOEA/D can be more adept at focusing on specific objectives whereas, NSGA-II tends to be more invariant to objective scaling. Additionally, certain objectives are shown to be either beneficial or detrimental to finding valid models, for instance, inclusion of a distance feedback objective was considerably detrimental to finding valid models, while a lateral velocity objective was more beneficial.
... Evolutionary Multi-objective Optimisation [18,25,26], has seldom been used in the automatic configuration of DNNs and/or in the optimisation of their hyperparameters. Works on the latter include the approach proposed by Kim et al. [27], where the authors used two conflicting objectives, speed and accuracy, to be optimised via an EMO process using the NSGA-II. ...
Machine learning has rapidly evolved during the last decade, achieving expert human performance on notoriously challenging problems such as image classification. This success is partly due to the re-emergence of bio-inspired modern artificial neural networks (ANNs) along with the availability of computation power, vast labelled data and ingenious human-based expert knowledge as well as optimisation approaches that can find the correct configuration (and weights) for these networks. Neuroevolution is a term used for the latter when employing evolutionary algorithms. Most of the works in neuroevolution have focused their attention on a single type of ANNs, named Convolutional Neural Networks (CNNs). Moreover, most of these works have used a single optimisation approach. This work makes a progressive step forward in neuroevolution for vehicle trajectory prediction, referred to as neurotrajectory prediction, where multiple objectives must be considered. To this end, rich ANNs composed of CNNs and Long-short Term Memory Network are adopted. Two well-known and robust Evolutionary Multi-objective Optimisation (EMO) algorithms, named Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) are also adopted. The completely different underlying mechanism of each of these algorithms sheds light on the implications of using one over the other EMO approach in neurotrajectory prediction. In particular, the importance of considering objective scaling is highlighted, finding that MOEA/D can be more adept at focusing on specific objectives whereas, NSGA-II tends to be more invariant to objective scaling. Additionally, certain objectives are shown to be either beneficial or detrimental to finding valid models, for instance, inclusion of a distance feedback objective was considerably detrimental to finding valid models, while a lateral velocity objective was more beneficial.
... Modes of operation of PV-STATCOM (capacitive and inductive mode)[21]-[23] ...
In this paper power quality of 3-bus solar-based hybrid system has been presented (where one or more than one distribution generator unit is connected to the grid). The injection of solar power into grid-connected systems creates power quality problems such as current consistency, electrical fluctuations, and inefficient power demand. A power quality control strategy based on a real-time self-regulation method for autonomous microgrid operation has been presented. In this paper solar farm design and satisfactory performance tests such as PV-static synchronous compensator (STATCOM) to improve the power quality of grid-based systems have been presented using the MATLAB/Simulink environment. Pulse width modulator (PWM) with proportional-integral derivative (PID) controller used for frequency control, reactive var compensation is used to control voltage profile. Multi-objective genetic algorithm (MOGA) for reactive power planning (RPP) with the objective of reactive power minimization is introduced. The optimization variables are generator voltage, transformer tap changer, and various operational constraints.
... Modes of operation of PV-STATCOM (capacitive and inductive mode)[21]-[23] ...
In this paper power quality of 3-bus solar-based hybrid system has been presented (where one or more than one distribution generator unit is connected to the grid). The injection of solar power into grid-connected systems creates power quality problems such as current consistency, electrical fluctuations, and inefficient power demand. A power quality control strategy based on a real-time self-regulation method for autonomous microgrid operation has been presented. In this paper solar farm design and satisfactory performance tests such as PV-static synchronous compensator (STATCOM) to improve the power quality of grid-based systems have been presented using the MATLAB/Simulink environment. Pulse width modulator (PWM) with proportional-integral derivative (PID) controller used for frequency control, reactive var compensation is used to control voltage profile. Multi-objective genetic algorithm (MOGA) for reactive power planning (RPP) with the objective of reactive power minimization is introduced. The optimization variables are generator voltage, transformer tap changer, and various operational constraints.
... Only in exceptional cases can multi-objective optimization problems be solved analytically (Coello Coello 1999;Deb 2001). A standard method for solving such problems relies on the use of evolutionary algorithms, which constitute a main form of population-based optimization (Kochenderfer and Wheeler 2019, Ch. 9). ...
Philosophers have recently questioned the methodological status of agent-based modeling. Meanwhile, this methodology has been central to various studies of the COVID-19 pandemic. Few agent-based COVID-19 models are accessible to philosophers for inspection or experimentation. We make available a package for modeling the COVID-19 pandemic and similar pandemics and give an impression of what can be achieved with it. In particular, it is shown that by coupling an agent-based model to a standard optimizer we are able to identify strategies for implementing non-pharmacological interventions that flexibly lower or raise social activity, depending on how the outbreak develops, while balancing various desiderata that cannot be fully satisfied together. The simulation outcomes to be presented testify to the power of agent-based modeling and thereby help to push back against the recent philosophical critique of this methodology.
... Multi-objective functions can be solved in different ways. The classical method to solve the multi-objective function is based on the weighted sum method [125]. Using various weighting coefficients in each objective, the weighted sum approach adds all the objective functions together. ...
Fossil fuels greenhouse gases emissions for remote and island electrification and transport application have led to the study of hybrid renewable energy systems. These comprise renewable energy generation units (Solar Photovoltaics, Wind Turbines, etc.), storage units (Battery, Hydrogen Storage Tanks), and other components (compressors, inverters, pressure regulators, etc.). Component selection is key for reducing the negative effects on the system’s cost, reliability, environmental, and social impacts, and reaching its full potential. The aim of this study is to review recent advancements in the architecture sizing and energy management strategies of hybrid renewable energy systems, considering various locations and applications, and based on technical, reliability, environmental, and social factors. The employed optimization formulation frameworks based on various linear and non-linear objective functions, equality and non-equality constraints, and decision variables are also investigated. Optimization methods including tools and solvers used to solve complex hybrid renewable energy system optimization problems are also explored. The study's important results include identifying various components that could help optimize the performance of hybrid renewable energy systems. Additionally, the study highlights the potential of optimization-based energy management strategies to enhance the reliability and efficiency of these systems. Trends in the studies of the implementation of grid-integrated or off-grid hybrid renewable energy systems in remote, island, or urban locations are highlighted. It could also aid in the development of methodologies for designing sustainable energy infrastructure that is resilient and environmentally friendly. Furthermore, the study could inspire future research that addresses the identified research challenges and areas of interest.
... The main advantage of GA over other evolutionary optimisation approaches such as Particle Swarm Optimization (PSO) is its simplicity of implementation and stochastic nature, which allows it to effectively explore the global search space. PSO, on the other hand, has a propensity to converge around local optima, restricting its ability to thoroughly explore the search space [26]. ...
This research conducts an experimental investigation of the far-field acoustic properties of propellers with different pitch values. Discussions were held regarding the underlying physical principles and how pitch value affects the noise source mechanism in propellers. The classical propeller Broadband noise model was validated and utilised as an objective input for Optimisation using a genetic algorithm in order to evaluate the propeller turbulent Boundary Layer-Trailing Edge interaction Broadband noise. Optimisation were conducted on two blade section profiles in order to reduce broadband noise and boundary layer thickness while constraining the aerodynamic properties to be within 10% increase compared to the baseline propeller blade. A low noise propeller geometry profile was achieved with minimal aerodynamic deviations from the baseline propeller. Broadband noise reduction is found to be significant in the mid frequency region (100 Hz to 3 KHz), while shifting the peak of the Broadband spectrum to higher frequencies. The optimised propeller's overall sound power level was reduced by 3 [dB] as compared to the baseline. This was achieved by altering the upper and lower airfoil surfaces geometrically, reducing the Boundary Layer thickness and wall shear stresses at the Trailing Edge.
... The goal of multi-objective optimization [4] is to find the vectors of decision variables ...
HIV-1 protease cleavage site prediction of an amino acid sequence of Human Immune Deficiency Virus (HIV- 1) type 1 has been the subject of intense research for decades to increase the AUC value of the prediction without placing much attention to the accuracy metric by many researchers. Knowledge of the substrate specificity of HIV-1 protease has significant application in HIV-1 protease inhibitors development and in studying novel drug targets. Motivated by this, a multi-objective optimization (MOO) based majority voting ensemble framework combining the outputs from multiple classifiers has been proposed in the current paper to increase both the prediction accuracy and AUC values simultaneously. The optimal set of classifiers that are considered for voting purposes at the time of combining the outputs is determined automatically using the search capability of MOO. Comparatively better results have been attained using various benchmark data sets with average accuracy and AUC (area under the ROC curve) values of 0.92 and 0.96, respectively.
... The multi-objective problem MOP usually has inconsistent objectives and are shown in various calculation units. Thus, no single solution is accessible to ameliorate each objective concurrently [41]. An MOPSO algorithm is applied to unravel this multi-objective problem. ...
Mixed-model assembly is a particular set of production lines assembling a family of product models, with similar specifications. Designing paced assembly lines face two primary problems, Balancing and sequencing. The balancing quality is closely associated with the described production sequence. Although these two are problems of one assembly method but they do not take place at the same time, balancing pose a problem during the line designing, whereas sequencing becomes problematic at fluctuating demand of market. In the present research, we have presented a balancing and sequencing problem and proper times to setup the machines between tasks. Unlike a majority of published studies, this paper contains two successive tasks’ setup times in dynamic periods, in which periods also impact the flowing period. A mathematical description with a number of objective functions containing: reducing the inappropriate assembly lines sequence, reducing setup cost, and reducing the inappropriate products balance and the impact of this situation on incomplete tasks. This problem has a combinatorial nature, therefore, the exact techniques, for example combined integer linear programming cannot solve large-sized problems. Thus, the literature have presented several metaheuristic algorithms to solve the problems nearly optimal. This study uses multi-objective particle swarm optimization algorithm, a suitable approach, to create models and solutions. Various problems are designed in different sizes and compared, the decision variables sensitivity is investigated to prepare managerial intuitions. The findings propose that presented algorithm can solve the research problems more efficiently.
... To examine this, we recruited an evolutionary algorithm, which models computationally the mechanisms of variation and selection (Coello Coello, 1999). In detail, the evolutionary computations consisted of the following steps: ...
Much of what we know, we know thanks to our interactions with others. There is a variety of ways in which we learn from others. We sometimes simply adopt the viewpoints of those we regard as experts, but we also sometimes change our viewpoints in more subtle ways based on the viewpoints of people we regard as our peers. Both forms of social learning have been receiving increasing attention. However, studies investigating how best to combine them, and how to combine the two with individual forms of learning, are still few and far between. This paper looks at ways to integrate various forms of social learning with learning at an individual level within a broadly Bayesian framework. Using agent-based models, we compare the different ways in terms of accuracy of belief states as well as in terms of evolutionary viability. The outcomes of our simulations suggest that agents are best off spending most of their time engaging in social learning, reserving only a limited amount of time for individual learning.
It is usually difficult for humans to solve a real world problem. Although for million of years nature has its own ways to look into these problems and solve them. Hence, now a days when man made methods do not work in these situations, they turn to Nature for problem solution. Therefore, the so called Nature inspired algorithms/ Heuristics are developing rapidly. Generally it is difficult to ï¬nd the optimum solution of the problem by using Heuristic methods. On the other hand these methods are good in approximating the solution in justiï¬able time. One of such algorithm is known as Strawberry Algorithm (SBA). Here, we propose to investigate the effect of roots and runners in SBA.It is usually difficult for humans to solve a real world problem. Although for million of years nature has its own ways to look into these problems and solve them. Hence, now a days when man made methods do not work in these situations, they turn to Nature for problem solution. Therefore, the so called Nature inspired algorithms/ Heuristics are developing rapidly. Generally it is difficult to ï¬nd the optimum solution of the problem by using Heuristic methods. On the other hand these methods are good in approximating the solution in justiï¬able time. One of such algorithm is known as Strawberry Algorithm (SBA). Here, we propose to investigate the effect of roots and runners in SBA.
The presence of limitations such as processing and energy in many of end devices has resulted in task offloading to other sources. To address these limitations, offloading can be performed in diverse environments such as fog or cloud. This article reviews the multi-objective optimization of task offloading, which is a crucial challenge in fog computing. First, a task offloading model is presented for the simultaneous optimization of response time, energy consumption, and cost criteria, while considering availability criteria. This model utilizes a virtual controller to monitor all three layers of IoT, Fog, and Cloud. In the following, a developed version of the multi-objective crow search algorithm called the Improved Non-Dominated Sorting Crow Search Algorithm (INSCSA) is presented. An advantage of this research, compared to previous works, is the investigation of the optimality of the whole system. Evaluations demonstrate that our method has achieved superior results compared to the other methods.
Given the ever-growing electricity consumption and environmental anxiety with the predominant usage of conventional fuels in power plants, it is crucial to explore suitable alternatives to address these issues. Renewable energy sources (RESs) have emerged as the preferred choice for meeting energy requirements due to their minimal pollution. This study proposes a new idea to minimize operational costs and achieve the most cost-effective grid with minimum cost. Meanwhile, the transportation sector is gradually replacing conventional fossil-cars with electric ones, specifically plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles (PHEVs), which have gained significant consideration. These vehicles can join to the main grid and engage in energy exchange through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies. Additionally, the concept of microgrid (MG) is proposed to optimize the potential of PEVs through smart infrastructure. Using the V2G capability, the operating costs are reduced, providing opportunities to incorporate PEVs into the network. Therefore, effective operation of MGs becomes highly significant. This paper suggests management of a MG consisting of PEVs and RESs. The approach utilizes a stochastic programming technique called unscented transformation (UT). The problem is addressed as a single-objective stochastic optimization problem with the aim of minimizing the operation cost. The proposed approach employs the hybrid whale optimization algorithm and pattern search (HWOA–PS) to solve the stochastic problem. The obtained outcomes are compared with those of other approaches to validate its effectiveness.
This paper introduces a novel surrogate-assisted multi-objective nutcracker optimization algorithm. This algorithm is built upon the recently proposed nutcracker optimization algorithm, drawing inspiration from the behaviours observed in Clark’s nutcrackers. The algorithm is developed based on two distinct behaviours exhibited
by these birds. To comprehensively evaluate the performance of the proposed algorithm, a dual-pronged approach is adopted. On the one hand, a set of artificial test problems is employed to scrutinize the algorithm’s capabilities, while on the other hand, a set of real-world problems is considered to assess its practical efficacy. The results of the proposed algorithm are evaluated in comparison to existing baseline algorithms and state-of-the-art algorithms, using well-recognized performance metrics, both qualitatively and quantitatively. The obtained results provide convincing evidence of the performance of the proposed algorithm.
A numerical data set will be developed to assist in the design of Savonius wind turbines. The main objective of study is to improve Savonius turbine blade designs to increase torque coefficients, rotational speeds, and pressure coefficients. Simulating 3D models and validating them with wind tunnel data were part of the experimental design methodology. Multi-objective optimization is used to optimize turbine performance. Twist angle, aspect ratio, and overlap ratio are all important factors in determining the optimal torque and power coefficients. Data-driven objective functions were modeled using the group method of data handling (GMDH). Using an evolutionary Pareto-based optimization approach, polynomial models were used to plot Pareto fronts and TOPSIS to calculate optimal commercial points. The torque coefficient, rotational speed, and power coefficient are all improved by 13.74%, 0.071%, and 5.32%, respectively. As a result of the multi-objective optimization of the turbine, some significant characteristics of objective functions were discovered.
In the real world, there are many complex problems in engineering. Every problem has a level of computational complexity, starting from simple problems and reaching NP-hard problems. Np-hard problems do not have a definite answer. Therefore, hyper-heuristic algorithms try to optimize NP-hard problems. Hyper-heuristics optimize complex search problems using a combination plate of exploration and exploitation strategies. The current algorithms need more generalizability, handling specific data types, limitations to a particular search problem, and weak performance. We propose a World hyper-heuristic (World) to address the issues using a novel reinforcement learning method. World, in two steps of rewarding and selection, dynamically switches between exploration and exploitation strategies provided in an infinite pool of meta-heuristics. We evaluated the performance of our proposed method in three phases. First, we optimized the standard functions as artificial Np-hard problems. Then, we compared real engineering examples related to discrete Np-hard problems. Finally, we implemented and analyzed problems with the continuous Np-hard problems. Our extensive comparisons with the state-of-the-art algorithms demonstrate the World’s outperformance in handling varied search problems with any data type. Among all the findings, World finds the shortest path of length 4.33e + 05, far shorter than the results of the state-of-the-art work, in benchmarked data of 10,000 real cities.
This study presents a novel cooling-power-desalination combined cycle for recovering shipboard diesel exhaust heat, integrating a freezing desalination sub-cycle to regulate the ship’s cooling-load fluctuations. The combined cycle employs ammonia–water as the working fluid and efficiently utilizes excess cooling capacity to pretreat reverse osmosis desalination. By adjusting the mass flow rate of the working fluid in both the air conditioning refrigeration cycle and the freezing desalination sub-cycle, the combined cycle can dynamically meet the cooling-load demand under different working conditions and navigation areas. To analyze the cycle’s performance, a mathematical model is established for energy and exergy analysis, and key parameters including net output work, comprehensive efficiency, and heat exchanger area are optimized using the MOPSO algorithm. The results indicate that the system achieves optimal performance when the generator temperature reaches 249.95 °C, the sea water temperature is 22.29 °C, and 42% ammonia–water is used as the working fluid. Additionally, an economic analysis of frozen seawater desalination as RO seawater desalination pretreatment reveals a substantial cost reduction of 22.69%, showcasing the advantageous features of this proposed cycle. The research in this paper is helpful for waste energy recovery and sustainable development.
Energy management plays a pivotal role in enhancing the economic efficiency of power systems. However, it is noteworthy that a substantial number of microgrids (MGs) exhibit inherent unbalances that impose a range of critical issues, including voltage instability, elevated losses, power quality violations, safety concerns, and inefficiencies in energy management. Fast-acting power electronic converters present a relevant and efficacious solution for balancing such complex networks. This paper investigates the application of such converters within the realm of 3-phase unbalanced networks, wherein the proposed algorithm not only ameliorates network imbalances but also yields substantial reductions in operational costs, power losses, voltage deviations, and emissions. Demand response (DR) program has been applied to the model to enhance the system efficiency. The uncertainty about electric demand and renewable energy sources is considered in the simulation model for precise results. By implementing DR programs and penetrating distributed generators (DGs), the proposed model has been shown to reduce network losses and operation costs by 23% and 80%, respectively. Also, the total up-to-down voltage deviation of the voltage profile has been significantly reduced by 400%.
Analog circuits are increasingly used as controllers for complex fuzzy systems, prompting the need for hardware implementation of fuzzy logic solutions and, depending on the problem, changing the configuration of the fuzzy system may be required. In this paper, we propose a multi-objective optimization strategy to evolve analog circuits to simulate fuzzy membership functions, using a fuzzy system to aggregate different objectives and evaluate analog circuits. The proposed strategy provides a simple yet effective multi-objective strategy for the circuit evolution, with the benefit of the interpretability of the results. We compare our proposed approach in different analog circuit optimization using its mathematical models to generate membership functions. Compared to other optimization strategies, our approach achieves optimal results with fewer iterations.
Surrogate-Assisted Evolutionary Optimisation algorithms are a specialized brand of optimisers developed to undertake problems with computationally expensive fitness functions. These algorithms work by building a cheap approximation or model of the exact function and using it in the evaluation of solutions within the optimisation process. This use of modelling techniques within optimisation, while offers a practical reduction in function calls, brings along with it some additional questions. This paper starts with a description of the key elements of surrogate-assisted evolutionary optimisation algorithms as they are outlined throughout the literature, and then, proceeds to rearrange these elements using a novel blueprint of the field. The proposed blueprint can be used to represent any surrogate-assisted evolutionary algorithm in a way that illustrates its principles and components in a non-vague manner. In addition, a survey of the most prominent works in the field is conducted using this novel blueprint. Finally, a number of challenges and perspectives are listed before the paper is concluded.
The energy performance of a building has a significant impact on the environment as well as the comfort of its occupants. Given that window systems and control elements such as shading devices play a critical role in a building's energy consumption and comfort, this article presents an investigation into the optimization of windows in four different orientations, along the four cardinal directions, for a typical office room in Tehran. The objective is to reduce cooling, heating, and lighting energy consumption while improving occupant comfort simultaneously, using the NSGA‐II algorithm as a multi‐objective optimization method. To this end, first, an investigation into the effects of various parameters on total building energy consumption and occupant comfort is conducted. Subsequently, each parameter is evaluated in detail concerning its impact on each façade. As the final step, for each façade, the optimal solutions and the most undesirable scenarios are determined. The results, in which the possibility of glare is also considered, can be used by architects and designers for making informed design decisions. The results show that, in all orientations, the number of slats and their distance from the wall are the most effective parameters of shading configurations. Although the eastern and western windows are larger in this study, visual discomfort can be controlled using suitable shadings to achieve acceptable levels of visual comfort. Furthermore, north façades provide the least amount of thermal comfort and consume the least amount of energy while experiencing glare for longer periods.
In recent years, multi-objective optimization techniques have gained popularity as a powerful approach to the solution of complex engineering problems that involve multiple competing objectives. This popularity can be attributed to the fact that multi-objective optimization techniques can solve complex engineering problems. Engineers are able to think about many goals at the same time using this method, which enables them to discover the best solution that fits all of their requirements. Yet, there are obstacles connected with setting objectives, dealing with high computational costs, comprehending complicated Pareto fronts, coping with uncertainty, and having limited accessibility to data from the real world. The purpose of this paper is to present a literature review of multi-objective optimization techniques for the solution of complex engineering problems. The review covers a variety of techniques that are available for multi-objective optimization as well as their applications in a variety of engineering fields. It also draws attention to the difficulties involved with their application and the necessity of giving serious consideration to these difficulties while attempting to solve actual engineering problems that occur in the real world. The research comes to the conclusion that multi-objective optimization is an effective method that can assist engineers in solving complicated engineering issues by concurrently taking into consideration numerous competing objectives.
HIV-1 protease cleavage site prediction of an amino acid sequence of Human Immune Deficiency Virus (HIV-1) type 1 has been the subject of intense research for decades to increase the AUC value of the prediction without placing much attention to the accuracy metric by many researchers. Knowledge of the substrate specificity of HIV-1 protease has significant application in HIV-1 protease inhibitors development and in studying novel drug targets. Motivated by this, a multi-objective optimization (MOO)-based majority voting ensemble framework combining the outputs from multiple classifiers has been proposed in the current paper to increase both the prediction accuracy and AUC values simultaneously. The optimal set of classifiers that are considered for voting purposes at the time of combining the outputs is determined automatically using the search capability of MOO. Comparatively better results have been attained using various benchmark data sets with average accuracy and AUC (area under the ROC curve) values of 0.92 and 0.96, respectively.
Mobile robots are increasingly populating homes, hospitals, shopping malls, factory floors, and other human environments. Human society has social norms that people mutually accept; obeying these norms is an essential signal that someone is participating socially with respect to the rest of the population. For robots to be socially compatible with humans, it is crucial for robots to obey these social norms. In prior work, we demonstrated a Socially-Aware Navigation (SAN) planner, based on Pareto Concavity Elimination Transformation (PaCcET), in a hallway scenario, optimizing two objectives so the robot does not invade the personal space of people. This article extends our PaCcET-based SAN planner to multiple scenarios with more than two objectives. We modified the Robot Operating System’s (ROS) navigation stack to include PaCcET in the local planning task. We show that our approach can accommodate multiple Human-Robot Interaction (HRI) scenarios. Using the proposed approach, we achieved successful HRI in multiple scenarios such as hallway interactions, an art gallery, waiting in a queue, and interacting with a group. We implemented our method on a simulated PR2 robot in a 2D simulator (Stage) and a pioneer-3DX mobile robot in the real-world to validate all the scenarios. A comprehensive set of experiments shows that our approach can handle multiple interaction scenarios on both holonomic and non-holonomic robots; hence, it can be a viable option for a Unified Socially-Aware Navigation (USAN).
An oppositional-based learning approach with a real coded chemical reaction algorithm (ORCCRO) has been considered in this manuscript. The ORCCRO algorithm has been used here to find the near-global optimum solution for multi-objective economic-emission load dispatch problem with having nonlinear constraints. Emission extract from thermal power plants like NOx, inequality constraints like power generation operating limit and equality constraints like power balance consideration are considered here. ORCCRO follows the process to reach a stable state in lower energy with different molecular chemical collision. Oppositional-based learning mechanism has also hybrid here with RCCRO to find out more effective solution. Three different test systems are considered for simulation studies revealed that the ORCCRO method is much superior in comparison with other effective algorithms. The results obtained in these three test systems prove the robustness and the efficiency of ORCCRO.
The third evolutionary I adaptive computing conference organised by the Plymouth Engineering Design Centre (PEDC) at the University of Plymouth again explores the utility of various adaptive search algorithms and complementary computational intelligence techniques within the engineering design and manufacturing domains. The intention is to investigate strategies and techniques that are of benefit not only as component I system optimisers but also as exploratory design tools capable of supporting the differing requirements of conceptual, embodiment and detailed design whilst taking into account the many manufacturing criteria influencing design direction. Interest in the integration of adaptive computing technologies with engineering has been rapidly increasing in recent years as practical examples illustrating their potential relating to system performance and design process efficiency have become more apparent. This is in addition to the realisation of significant commercial benefits from the application of evolutionary planning and scheduling strategies. The development of this conference series from annual PEDC one day workshops to the biennial 'Adaptive Computing in Engineering Design and Control' conference and this year's event reflects this growth in both academic and industrial interest. The name change to include manufacture relates to a desire to increase cover of integrated product development aspects, facility layout and scheduling in addition to process I machine control.
In optimization, multiple objectives and constraints cannot be handled independently of the underlying optimizer. Requirements such as continuity and differentiability of the cost surface and yet another conflicting element to the decision process. While "better" solutions should be rated higher than "worse" ones, the resulting cost landscape must also comply with such requirements.
Evolutionary algorithms (EA'S), which have found application in many areas not amenable to optimization by other methods, possess many characteristics desirable in a multiobjective optimizer, most notably at the concerted handling of multiple candidate solutions. However, EA's are essentially unconstrained search techniques which require the assignment of a scalar measure of quality or fitness to such candidate solutions.
After reviewing current evolutionary approaches to multiobjective constrained optimization, the paper proposes that fitness assignment be interpreted as, or at least related to, a multicriterion decision process. A suitable decision making framework based on goals and priorities is subsequently formulated in terms of a relational operator, characterized and shown to encompass a number of simpler decision strategies. Finally, the ranking of an arbitrary number of candidates is considered. The effect of preference changes on the cost surface seen by an EA is illustrated graphically for a simple problem. The paper concludes with the formulation of a multiobjective genetic algorithm based on the proposed decision strategy. Niche formation techniques are used to promote diversity among preferable candidates and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape.
The evolutionary approach to multiple function optimization formulated in the first part of the paper (1) is applied to the optimization of the low-pressure spool speed governor of a Pegasus turbine engine. This study illustrates how a technique such as the mUltiobjective Genetic Algorithm can be applied and exemplifies how design requirements can be defined as the algorithm runs.
Several objective functions and associated goals express design concerns in direct form, i.e., as the designer would state them. While such a designer-orientated formulation is very attractive, its practical usefulness depends heavily on the ability to search and optimize cost surfaces in such a class much broader than usual, as already provided to a large extent by the Genetic Algorithm (GA).
The two instances of the problem studied, demonstrate the need for preference articulation in cases where many and highly competing objects lead to a non dominated set too large for a finite population to sample effectively. Further, it is sown that only a very small portion of the non-dominated set is of practical relevance, which further substantiates the need to supply preference information to the GA. The paper concludes with a discussion of the results.
1 GAs: What Are They?.- 2 GAs: How Do They Work?.- 3 GAs: Why Do They Work?.- 4 GAs: Selected Topics.- 5 Binary or Float?.- 6 Fine Local Tuning.- 7 Handling Constraints.- 8 Evolution Strategies and Other Methods.- 9 The Transportation Problem.- 10 The Traveling Salesman Problem.- 11 Evolution Programs for Various Discrete Problems.- 12 Machine Learning.- 13 Evolutionary Programming and Genetic Programming.- 14 A Hierarchy of Evolution Programs.- 15 Evolution Programs and Heuristics.- 16 Conclusions.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- References.
The genetic algorithm (GA), a new search technique, is applied to a
multiple objective groundwater pollution containment problem. This
problem involves finding the set of optimal solutions on the trade-off
curve between the reliability and cost of a hydraulic containment
system. The decision variables are how many wells to install, where to
install them, and how much to pump from each. The GA is an optimization
technique patterned after the biological processes of natural selection
and evolution. A GA operates on a population of decision variable sets.
Through the application of three specialized genetic operators:
selection, crossover, and mutation, a GA population "evolves" toward an
optimal solution. In the paper, simple GAs and GAs that can solve
multiple objective problems are described. Two variations of a multiple
objective GA are formulated: a vector-evaluated GA (VEGA) and a Pareto
GA. For the zero-fixed cost situation, the Pareto GA is shown to be
superior to the VEGA and is shown to produce a trade-off curve similar
to that obtained via another optimization technique, mixed integer
chance constrained programming (MICCP). The effect on the VEGA and
Pareto GA of parameter variation is shown. The Pareto GA is shown to be
capable of incorporating the fixed costs associated with installing a
system of wells. Results for several levels of fixed cost are presented.
A comparison of computer resources required by the GAs and the MICCP
method is given. Future research plans are discussed, including the
incorporation of the objective of pump-out time into the model and the
development of parallelized GAs.
The proposed Genetic Algorithms (GAs) are incorporated with problem-specific knowledge and the decision maker's degree of optimism, and the criteria space approach for bicriteria linear program conductive to find out the nondominated extreme points in the criteria space.
Tile Bicriteria Linear Transportation Problem (BLTP) is a special structure of the multiobjective transportation problem since the feasible region can be depicted in two dimensions in criteria space. In this paper, we present an Evolution Program to solve the bicriteria transportation problem In this method, an improved selection strategy is included and a particular technique called Extinction and Immigration is used for crossover operator when the same chromosomes are chosen to mate. A set of efficient solutions or an approximation of this set can be found by this approach.
This paper presents a method for optimizing the design of reinforced concrete beams subject to a specified set of constraints. A new model of optimization is proposed, leading to more realistic and practical designs. As there are an infinite number of possible beam dimensions and reinforcement ratios that yield the same moment of resistance, it becomes difficult to achieve the least-cost design by conventional iterative methods. We present a method based upon a search technique using genetic algorithms. Several applications show how our system provides more realistic designs than other methods based on mathematical programming techniques. Also, we show our results of experimenting with several representation schemes for the genetic algorithm, and the methodology that we used to adjust its parameters — i.e. population size, crossover and mutation rates and maximum number of generations—so that it produces a reasonable answer in a short period of time. A prototype of this system is currently being tested at our school, to see its potential use as a tool for real-world applications.
The subject of this article is optimization of the construction of multishafted machine tool gear trains, consisting of double shaft gear transmissions with geometric progression of the output shaft speeds. The complexity of the problem and the large number of significant variables concerned, has led to the application of logic programmes, as this is the only known way to formulate economically based methods, providing that Digital Computers are employed. The following formulation of the logic principle has been developed.
In this work, a multiobjective genetic algorithm is applied in the identification of polynomial models for a real non-linear system. The approach is shown to allow multiple performance, complexity, and validity criteria to influence the selection of candidate model structures, leading to the production of various preferable alternatives. The various models produced by the algorithm enable a better informed selection of the final identified model.
For set covering problems, genetic algorithms with two types of crossover operators are investigated in conjunction with three penalty function and two multiobjective formulations. A Pareto multiobjective formulation and greedy crossover are suggested to work well. On the other hand, for traveling salesman problems, the results appear to be discouraging; genetic algorithm performance hardly exceeds that of a simple swapping rule. These results suggest that genetic algorithms have their place in optimization of constrained problems. However, lack of, or insufficient use of fundamental building blocks seems to keep the tested genetic algorithm variants from being competitive with specialized search algorithms on ordering problems.
In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands that the user have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms (GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a number of solutions simultaneously. Although a vector evaluated GA (VEGA) has been implemented by Schaffer and has been tried to solve a number of multiobjective problems, the algorithm seems to have bias toward some regions. In this paper, we investigate Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously. The proof-of-principle results obtained on three problems used by Schaffer and others suggest that the proposed method can be extended to higher dimensional and more difficult multiobjective problems. A number of suggestions for extension and application of the algorithm are also discussed.
This monograph contains 6 chapters. Topics discussed include formulation of multicriterion optimization problems, multicriterion mathematical programming, function scalarization methods, min-max approach-based methods, and network multicriterion optimization. Examples presented include investment distribution, electric discharge machining, and gearbox design. Three appendixes contain FORTRAN Programs for random search methods, interactive multicriterion optimization, are network multicriterion optimization. Refs.
This paper investigates the problem of using a genetic algorithm to converge on a small, user-defined subset of acceptable solutions to multiobjective problems, in the Pareto-optimal (P-O) range. The paper initially explores exactly why separate objectives can cause problems in a genetic algorithm (GA). A technique to guide the GA to converge on the subset of acceptable solutions is then introduced.
The paper then describes the application of six multiobjective techniques (three established methods and three new, or less commonly used methods) to four test functions. The previously unpublished distribution of solutions produced in the P-O range(s) by each method is described. The distribution of solutions and the ability of each method to guide the GA to converge on a small, user-defined subset of P-O solutions is then assessed, with the conclusion that two of the new multiobjective ranking methods are most useful.
This work proposes a quantitative, non-parametric interpre- tation of statistical performance of stochastic multiobjective optimizers, including, but not limited to, genetic algorithms. It is shown that, accord- ing to this interpretation, typical performance can be defined in terms analogous to the notion of median for ordinal data, as can other measures analogous to other quantiles. Non-parametric statistical test procedures are then shown to be useful in deciding the relative performance of different multiobjective optimizers on a given problem. Illustrative experimental results are provided to support the discussion.
In many structural design tasks the designer's goal is to minimize and/or maximize several functions simultaneously. This situation is formulated as a multicriterion optimization problem. Since optimization tasks in structural design are often modelled by means of non-linear programming, a multicriterion approach ot this programming is discussed in the paper. The problem is formulated as follows: find a vector of design variables which satisfies constraints and optimizes a vector function which represents several noncomparable criteria.
Existing and potential applications of multi-optimization techniques to structural design are reviewed. Two approaches are available to formulate a multiobjective structural design problem. The first approach starts with a classical design, say minimize weight subject to cost, reliability, risk and other constraints; and then some of the quantities included in the constraints, in particular cost and reliability, are used to define additional objectives.