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A Niched Pareto Genetic Algorithm for Multiobjective Optimization
Abstract
Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple objectives by incorporating the concept of Pareto domination in its selection operator, and applying a niching pressure to spread its population out along the Pareto optimal tradeoff surface. We introduce the Niched Pareto GA as an algorithm for finding the Pareto optimal set. We demonstrate its ability to find and maintain a diverse "Pareto optimal population " on two artificial problems and an open problem in hydrosystems. I. Introduction Genetic algorithms (GAs) have been applied almost exclusively to single-attribute 1 problems. But a careful look at many real-world GA applications reveals that the objective functions...
... The most used is the weighted-sum method, which combines the multiple objectives of the PO problem into a single one, assigning weights to prioritize among them [6]. A second approach are Pareto-based models, which use the concept of Pareto optimality [11] to individually evaluate and rank the conflicting objectives and identify the dominant set of portfolios. ...
Portfolio selection, the process of selecting the optimal combination of securities to achieve investors objectives, is a leading problem in finance. Ever since its inception, the problem has been widely addressed theoretically but its practical applications in portfolio management have seen few changes, with new technologies like Artificial Intelligence (AI) having limited impact in practice. The present study addresses this gap by identifying six main areas of the portfolio selection process and analyzing literature trends and gaps in AI methods within these areas. Additionally, it proposes four primary research avenues where AI can enhance portfolio selection in both theory and practice: i) utilizing alternative datasets and big-data analysis, ii) implementing robust predictive portfolio optimization techniques, iii) applying Reinforcement Learning (RL) methods for dynamic portfolio optimization, and iv) leveraging AI to make portfolio optimization more applicable in real-world scenarios.
... In subsequent years, multi-objective optimization algorithms were developed specifically for addressing MOOPs. Several well-known and effective techniques based on evolution algorithms have been proposed to solve MOOPs, such as Multi-Objective Genetic Algorithm (MOGA) [7], Multi-Objective Niched Pareto Genetic Algorithm (NPGA) [8], Non-dominated Sorting Genetic Algorithm (NSGA) [9], and Multi-Objective Particle Swarm Optimization (MOPSO) [10]. Due to their population-based search technique, these algorithms may get a collection of solutions in a single run and they have demonstrated improved performance in addressing MOOPs [11]. ...
When real-world engineering challenges are examined adequately, it becomes clear that multi-objective need to be optimized. Many engineering problems have been handled utilizing the decomposition-based optimization approach according to the literature. The performance of multi-objective evolutionary algorithms is highly dependent on the balance of convergence and diversity. Diversity and convergence are not appropriately balanced in the decomposition technique, as they are in many approaches, for real-world problems. A novel Multi-Objective Artificial Algae Algorithm based on Decomposition (MOAAA/D) is proposed in the paper to solve multi-objective structural problems. MOAAA/D is the first multi-objective algorithm that uses the decomposition-based method with the artificial algae algorithm. MOAAA/D, which successfully draws a graph on 24 benchmark functions within the area of two common metrics, also produced promising results in the structural design problem to which it was applied. To facilitate the design of the "rectangular reinforced concrete column" using MOAAA/D, a solution space was derived by optimizing the rebar ratio and the concrete quantity to be employed.
... Experts preferred the second method because it computes genetic PO solutions in parallel. The Strength Pareto Evolutionary Algorithm (SPEA), Niched Pareto Genetic Algorithm (NPGA), Multi-Objective Genetic Algorithm (MOGA), and Pareto Archived Evolutionary Strategy (PAES) [33] are a few of the MOEAs that are known to imply a Pareto ranking. Other mathematical optimization strategies include the Particle Swarm Optimization Method, Fuzzy Optimization (FO), the Tabu Search Algorithm, the Simulated Annealing Process, and the Functional Analytic Optimization Strategies. ...
As industries move towards sustainable product development, biopolymers such as polylactide are gaining significant attention owing to their self-degradability and eco-friendliness. Therefore, a multi-objective optimization problem (MOOP) formulation to obtain high-performance polylactide concerning physicochemical properties is designed through mathematical modeling and solved using the Elitist Non-dominated Sorting Genetic Algorithm (NSGA II). The current work is focused on improving the polymer growth mechanisms with stannous octoate (catalyst) and 1-dodecanol (co-catalyst) by analyzing three different case studies using optimization approach. In the first study, the Pareto front for batch L-lactide ring-opening polymerization (L-ROP) with objective functions of average molecular weight, polydispersity index, and time is obtained. Further investigations on esterification, chain propagation and the ratio of monomer–catalyst and cocatalyst–catalyst is carried out. The optimized result using certain range of initial reagent concentrations is determined and one of the suitable Pareto optimal solution for case study 1 gives Mw = 610 kDa, PDI = 1.8, time = 100 s; case study 2 is Mw = 560 kDa, λ1/λ0 = 4300, λ0 = 70; case study 3 is Mw = 500 kDa, M/C = 33,800, ROH/C = 8.5. The neighboring optimal solutions in the Pareto front have been classified into 3 groups and the corresponding process parameters for the particular outcome are tabulated. Process modeling and optimization in close vicinity with appropriate experimental data are distinct aspects of this work to apply in industrial plant level.
... The first MOGA, called Vector Evaluated Genetic Algorithm was proposed by [22]. Afterward, other major MOEAs were presented, such as the algorithm by [20]), the Niched Pareto Genetic Algorithm by [23], the Nondominated Sorting Genetic Algorithm by [24], the Fast Nondominated Sorting Genetic Algorithm (NSGA-II) by [10]. In this work, we exploited a multi-objective genetic algorithm obtained by specialized variations of the NSGA-II strategy. ...
Optimal portfolio selection—composing a set of stocks/assets that provide high yields/returns with a reasonable risk—has attracted investors and researchers for a long time. As a consequence, a variety of methods and techniques have been developed, spanning from purely mathematics ones to computational intelligence ones. In this paper, we introduce a method for optimal portfolio selection based on multi-objective evolutionary algorithms, specifically Nondominated Sorting Genetic Algorithm-II (NSGA-II), which tries to maximize the yield and minimize the risk, simultaneously. The system, named EvoFolio, has been experimented on stock datasets in a three-years time-frame and varying the configurations/specifics of NSGA-II operators. EvoFolio is an interactive genetic algorithm, i.e., users can provide their own insights and suggestions to the algorithm such that it takes into account users’ preferences for some stocks. We have performed tests with optimizations occurring quarterly and monthly. The results show how EvoFolio can significantly reduce the risk of portfolios consisting only of stocks and obtain very high performance (in terms of return). Furthermore, considering the investor’s preferences has proved to be very effective in the portfolio’s composition and made it more attractive for end-users. We argue that EvoFolio can be effectively used by investors as a support tool for portfolio formation.
... This approach allows for a comprehensive consideration of the various influencing factors and the trade-offs between conflicting objectives [47]. The identification of Pareto-optimal solutions in multi-objective optimization problems can be effectively achieved through the use of several multi-objective evolutionary algorithms, including the Strength Pareto Evolutionary Algorithm (SPEA) [48], Niched Pareto Genetic Algorithm (NPGA) [49], Non-dominated Sorting Genetic Algorithm (NSGA), Pareto Archived Evolution Strategy (PAES) [50], Neighborhood Cultivation Genetic Algorithm (NCGA) [51], and Non-dominated Sorting Genetic Algorithm II (NSGA-II) [52]. The widespread use and recognition of Non-dominated Sorting Genetic Algorithm II (NSGA-II) as a solution to multi-objective optimization problems can be evidenced by its publication in the journal, IEEE Transactions on Evolutionary Computation, in 2002. ...
Earthquakes can cause significant damage to constructed structures, leading engineers to design systems that effectively reduce damage and improve real-time vibration control. While base isolation is a commonly used passive method for seismic protection in highway structures, it has limitations such as a lack of immediate adaptation, constrained power dissipation capacity, and poor performance during earthquakes. To address the limitations of passive base isolation bearings, a hybrid control system that includes semi-active MR dampers is being introduced into isolated highway bridge structures. The aim is to enhance vibration reduction and improve overall performance. One of the major challenges in implementing this technology is developing appropriate control algorithms to handle the nonlinear behavior of semi-active devices. This paper proposes an adaptive data-driven control algorithm, informed by evolutionary game theory and a multi-objective optimization process, to optimize the distribution of voltage to semi-active MR dampers based on measurements of the damper's response to input signals. The algorithm is designed to provide optimal seismic protection. The performance of the replicator dynamics in the control system depends on three critical parameters: total population, which represents the total available resources or the sum of actuator forces; growth rate, which is the rate at which resources are distributed among control devices; and the fictitious fitness function, which regulates power consumption. Previous studies used sensitivity analysis to ascertain the best values for population size and growth rate, a time-consuming and unreliable process. This study aims to improve the performance of the system by solving a multi-objective problem. The proposed approach integrates a control algorithm with a multi-objective optimization algorithm, namely NSGA-II, to find Pareto optimal values for all parameters of the replicator dynamics. These parameters include total population, growth rate, and the fictitious function, with the aim of ensuring sustainability. By considering multiple objectives simultaneously, the proposed approach can provide a more comprehensive and effective solution for the bridge control problem. The effectiveness of this proposed approach is demonstrated through sample results Utilizing a case study centered around the Southern California Interstate 91/5 Overcrossing Highway Bridge, which is exposed to seismic activities.
... An artificial neural network [27][28][29][30] is used to construct an approximate function to describe the relationship between the objective function and the optimization parameters. The neural network is generally composed of multiple layers. ...
As the core components of water jet propulsion system, water jet pump, directly affect the ship's propulsion performance. 3D reverse design method can effectively suppress the impeller secondary flow to get better performing hydraulic models. Aiming at fast and efficient optimization of the impellers, we parameterize the impellers and runner, and coupled with optimization algorithms and numerical calculations. It is an efficient and feasible way to coupling optimization based on multi-parameter, multi-objective and optimization algorithms, and parameterizing the impellers and guide vane geometry based on the 3D model designed by 3D reverse design method. In this paper, the waterjet propulsion pump is taken as the research object. According to the design method of the waterjet propulsion pump hydraulic model, the traditional axial flow blade design method, the modern ternary design theory of the blades, and the parametric three-dimensional reverse design method are selected to constructing an optimal design optimization strategy for the impeller of water-jet propulsion pump based on multi-parameter and multi-object genetic algorithm. Under the design conditions, the optimized pump efficiency reaches 89.28%, which is 1.24% higher than the original hydraulic model. The optimized pump head is 13.51 m, which is 0.39m more than the original hydraulic model. Under the design condition, the test measured head is 13.26m, the numerical calculation result is 1.88% higher than the test result, the test measured efficiency is 87.1%, the numerical calculation result is 2.5% higher than the test result. From the test results, the optimized impeller has higher hydraulic performance. Experiments have shown that it is feasible to optimize the design strategy.
... Due to a desire to solve the multiobjective optimization problem, multiobjective evolutionary algorithms are created, [40][41][42] which involve the Pareto front to obtain the best solution through mutual constraint. This elitist strategy that ensures a better solution is included throughout the iterative process. ...
In this study, we numerically investigated the performance of a printed circuit heat exchanger (PCHE) featuring National Advisory Committee for Aeronautics (NACA) 0020 airfoil overlap fins integrated into its straight flow channels. Numerical simulations were performed by solving Navier–Stokes and energy equations to analyze liquid water at temperatures ranging from 30 to 90°C, which is possible for use with a geothermal heating system, for example. The optimization of the PCHE airfoil fin geometry is performed using nondominated sorting genetic algorithm II (NSGA‐II) with three design features: front, back, and overlap lengths. The effectiveness and pressure drop of the PCHE are evaluated as two objective functions. The present study shows that, when a Reynolds number falls between 1088 and 5000, the overlapping airfoils exhibit lower pressure drops and higher convective heat flux relative to the separate airfoils, and an increase in overlap length always decreases the pressure drop positively. It is also found that using overlapping airfoils of front length of 0.1 mm, back length of 0.119 mm, and overlap length of 0.203 mm leads to almost the same effectiveness and a 22% decrease in pressure drop (which is generally believed to preferably maintain the high heat transfer performance and minimize pressure drop, as a favorable design), compared to a base design of the airfoils with front and back lengths of 0.2 mm and an overlap length of 0.1 mm.
... In case 1, the HKSOPSO-CP algorithm is applied to obtain the common optimal values of fuel cost C and pollution emissions E. Figure 4 depicts the Pareto optimal values and also plots the optimal solutions provided by other algorithms. Taking the graphs and tables together, it is clear that HKSOPSO-CP achieves a better cost and overall a better compromise solution compared to NPGA (Horn et al., 1994), MOPSO (Hazra and Sinha, 2011), and NSGA (Qu et al., 2016). Other algorithms, namely MOEA/D (Abido, 2009), SPEA (Basu and Basu, 2011), MBFA (Hota et al., 2010), FSBF (Panigrahi et al., 2010), and NGPSO (Zou et al., 2017), also successfully implemented a costmatching and compromise solution. ...
Due to growing concerns over environmental protection, economic and environmentally responsible power dispatching has become a hot topic in the field of power system control. Multi-objective optimization minimizes fuel costs and pollution emissions without violating operational constraints. To solve this problem, the MOP is decomposed into individual objects via the weighted sum method, and Newton’s method is used to tackle equality constraints iteratively. To this end, a hybrid algorithm (HKSOPSO-CP) based on kernel search optimization (KSO) and particle swarm optimization (PSO) with Cauchy perturbation is proposed in this paper. An experiment with 23 CEC benchmark functions shows that HKSOPSO-CP offers better performance compared with various popular algorithms proposed in recent years. When employed to solve the IEEE standard economic emission dispatch (EED) problems with 6, 10, 40, and 110 units, the proposed HKSOPSO-CP algorithm produces results indicating a better trade-off between the objectives relating to fuel costs and emissions compared to other algorithms that have recently been reported on in the literature.
As the core component of water-jet propulsion system, water-jet pump, directly affects the ship’s propulsion performance. Three-dimensional (3D) inverse design can obtain higher hydraulic performance of pump impeller and diffuser. It is an efficient and feasible way to coupling optimization based on multi-parameter, multi-objective, and optimization algorithms, and parameterizing the impellers and diffuser geometry based on the 3D model designed by 3D inverse design method. In this paper, the water-jet propulsion pump is selected as the research object. Based on the design methodology of the hydraulic model for the water-jet pump, traditional axial flow blade design methods, modern three-dimensional blade design theory, and parametric 3D inverse design methods are employed to develop an optimization strategy for the water-jet pump impeller using a multi-parameter, multi-objective genetic algorithm. The impeller is optimized using this strategy, and the performance of the model before and after optimization is compared through simulation and experimental results. Under the design conditions, the optimized pump efficiency reaches 89.28%, which is 1.24% higher than the original hydraulic model. The optimized pump head is 13.51 m, which is 0.39 m more than the original hydraulic model. The numerical calculation result is 1.88% higher than the test result, the test measured efficiency is 87.1%, and the numerical calculation result is 2.5% higher than the test result. The experimentally measured critical cavitation margin was 7.58 m, and the numerically calculated critical cavitation margin was 7.36 m, both of which indicate a high critical cavitation margin and strong cavitation resistance.
Accurately forecasting power consumption is crucial important for efficient energy management. Machine learning (ML) models are often employed for this purpose. However, tuning their hyperparameters is a complex and time-consuming task. The article presents a novel multi-objective (MO) hybrid evolutionary-based approach, GA-SHADE-MO, for tuning ML models aimed at solving the complex problem of forecasting power consumption. The proposed algorithm simultaneously optimizes both hyperparameters and feature sets across six different ML models, ensuring enhanced accuracy and efficiency. The study focuses on predicting household power consumption at hourly and daily levels. The hybrid MO evolutionary algorithm integrates elements of genetic algorithms and self-adapted differential evolution. By incorporating MO optimization, GA-SHADE-MO balances the trade-offs between model complexity (the number of used features) and prediction accuracy, ensuring robust performance across various forecasting scenarios. Experimental numerical results show the superiority of the proposed method compared to traditional tuning techniques, and random search, showcasing significant improvements in predictive accuracy and computational efficiency. The findings suggest that the proposed GA-SHADE-MO approach offers a powerful tool for optimizing ML models in the context of energy consumption forecasting, with potential applications in other domains requiring precise predictive modeling. The study contributes to the advancement of ML optimization techniques, providing a framework that can be adapted and extended for various predictive analytics tasks.
Dağınık arama algoritması, tek amaçlı optimizasyon problemlerinin çözümünde sıkça kullanılan bir yöntemdir. Ancak, çok amaçlı problemlerle başa çıkmak oldukça zorlu bir süreçtir. Bu makale, çok amaçlı optimizasyon problemleriyle başa çıkabilmek için "Dağınık Arama Algoritması" (DA) olarak adlandırılan yöntemin genişletilmesine yönelik bir öneri sunmaktadır. Önerilen yaklaşım, DA algoritmasına çok amaçlı optimizasyon algoritması olan Baskın Olmayan Sıralama Genetik Algoritması II (NSGA-II) yöntemindeki Yoğunluk Mesafesi (CD) ve Hızlı Bastırılmamış Sıralama kavramlarını ekleyerek hibrit çok amaçlı optimizasyon algoritması önermektedir. Bu önerilen algoritma, ZDT ve DTLZ test problemleri kullanılarak değerlendirilmiştir. Yapılan deneysel sonuçlar, önerilen Çok Amaçlı Dağınık Arama(ÇADA) algoritmasının 19 farklı çok amaçlı optimizasyon yöntemi ile karşılaştırıldığında, ZDT problemi için 2.40 IGD ortalama ile birinci sırada, DTLZ probleminde ise 0.0035 IGD ortalama değeri ile altıncı sırada yer aldığını göstermektedir. Bu sonuçlar, önerilen algoritmanın karşılaştırılabilir düzeyde başarılı bir performansa sahip olduğunu ortaya koymaktadır.
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.
Optimization is a process of minimizing or maximizing a given objective function under specified constraints. In multi-objective optimization (MOO), multiple conflicting functions are optimized within defined criteria. Numerous MOO techniques have been developed utilizing various meta-heuristic methods such as Evolutionary Algorithms (EAs), Genetic Algorithms (GAs), and other biologically inspired processes. In a cooperative environment, a Pareto front is generated, and an MOO technique is applied to solve for the solution set. On other hand, Multi-Criteria Decision Making (MCDM) is often used to select a single best solution from a set of provided solution candidates. The Multi-Criteria Decision Making–Pareto Front (M-PF) optimizer combines both of these techniques to find a quality set of heuristic solutions. This paper provides an improved version of the M-PF optimizer, which is called the elite Multi-Criteria Decision Making–Pareto Front (eMPF) optimizer. The eMPF method uses an evolutionary algorithm for the meta-heuristic process and then generates a Pareto front and applies MCDM to the Pareto front to rank the solutions in the set. The main objective of the new optimizer is to exploit the Pareto front while also exploring the solution area. The performance of the developed method is tested against M-PF, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), and Non-Dominated Sorting Genetic Algorithm-III (NSGA-III). The test results demonstrate the performance of the new eMPF optimizer over M-PF, NSGA-II, and NSGA-III. eMPF was not only able to exploit the search domain but also was able to find better heuristic solutions for most of the test functions used.
Productively reducing the time required to cut numerous through holes in automotive workpieces is crucial for enhancing parts manufacturing in the 3D laser cutting process. However, the conventional cutting strategy, in which the laser beam maintains a stationary posture along the hole path, lacks flexibility and fails to effectively leverage processing tolerances. In this study, we conduct a thorough analysis of the kinematics of a six-axis redundant laser cutting machine and resolve through a decoupling method with singularity management. We propose an innovative conic posture cutting strategy for 3D laser hole-cutting with thin materials. This approach adopts the geometry of a cone as the posture while cutting the hole path. In order to obtain the optimal vertex of the cone while minimizing the taper error generated by the conic posture and kinetic energy consumption of the actuators during motion, we formulate a multi-objective optimization problem and solve it using a genetic algorithm. Furthermore, we enhance the optimization by adopting a time minimization approach. Through the implementation of a B-pillar workpiece cutting experiment, we have successfully validated the credibility of our proposed cutting strategy, thereby demonstrating an enhancement of time on 26 hole-cutting paths.
A detailed numerical simulation of Colored Self-Compacting Concrete (CSCC) was conducted in this research. Emphasis was placed on an innovative calibration methodology tailored for ten unique CSCC mix designs. Through the incorporation of multi-objective optimization, MATLAB's Genetic Algorithm (GA) was seamlessly integrated with PFC3D, a prominent Discrete Element Modeling (DEM) software package. This integration facilitates the exchange of micro-parameter values, where MATLAB’s GA optimizes these parameters, which are then input into PFC3D to simulate the behavior of CSCC mix designs. The calibration process is fully automated through a MATLAB script, complemented by a fish script in PFC, allowing for an efficient and precise calibration mechanism that automatically terminates based on predefined criteria. Central to this approach is the Uniaxial Compressive Strength (UCS) test, which forms the foundation of the calibration process. A distinguishing aspect of this study was the incorporation of pigment effects, reflecting the cohesive behavior of cementitious components, into the micro-parameters influencing the cohesion coefficient within DEM. This innovative approach ensured significant alignment between simulations and observed macro properties, as evidenced by fitness values consistently exceeding 0.94. This investigation not only expanded the understanding of CSCC dynamics but also contributed significantly to the discourse on advanced concrete simulation methodologies, underscoring the importance of multi-objective optimization in such studies.
Many real-world optimization problems, particularly engineering ones, involve constraints that make finding a feasible solution challenging. Numerous researchers have investigated this challenge for constrained single- and multi-objective optimization problems. In particular, this work extends the boundary updating (BU) method proposed by Gandomi and Deb (2020) for the constrained optimization problem. BU is an implicit constraint handling technique that aims to cut the infeasible search space over iterations to find the feasible region faster. In doing so, the search space is twisted, which can make the optimization problem more challenging. In response, two switching mechanisms are implemented that transform the landscape along with the variables to the original problem when the feasible region is found. To achieve this objective, two thresholds, representing distinct switching methods, are taken into account. In the first approach, the optimization process transitions to a state without utilizing the BU approach when constraint violations reach zero. In the second method, the optimization process shifts to a BU method-free optimization phase when there is no further change observed in the objective space. To validate, benchmarks and engineering problems are considered to be solved with well-known evolutionary single- and multi-objective optimization algorithms. Herein, the proposed method is benchmarked using with and without BU approaches over the whole search process. The results show that the proposed method can significantly boost the solutions in both convergence speed and finding better solutions for constrained optimization problems.
The conceptual fusion of smart city and sustainability indicators has inspired the emergence of the smart sustainable city (SSC). Given the early stage of development in this field, most SSC studies have been primarily theoretical. Notably, existing empirical studies have overlooked the crucial aspect of feature engineering in the context of SSC, despite its significance in advancing SSC initiatives. This paper introduces an approach advocating for feature subset selection to maximize prediction accuracy and minimize computational time across diverse SSC indicators encompassing socio-cultural, economic, environmental, and governance categories. The study systematically collected multiple datasets on SSC indicators, covering various themes within the SSC framework. Employing six carefully chosen multiple-objective evolutionary feature selection algorithms, the research selected feature subsets. These subsets were then utilized in modeling algorithms to predict SSC indicators. The proposal enhanced prediction accuracy for life expectancy, online shopping intentions, energy consumption, air quality, water quality, and traffic flow for a smart and sustainable city by minimizing the subset features. The findings underscore the efficacy of feature subset selection in generating minimal features, thereby enhancing both prediction accuracy and computational efficiency in the realm of SSC indicators. For researchers aiming to develop sustainable systems for real-time data monitoring within SSC, the identified subset features offer a valuable resource, negating the necessity for extensive dataset collection. The provided SSC datasets are anticipated to serve as a catalyst, inspiring researchers to embark on empirical studies that explore SSC development from diverse perspectives, ultimately contributing to a more profound understanding of the SSC dynamics.
de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimization process, dnDD is naturally categorized as a many-objective optimization problem (ManyOOP), where more than three objectives must be simultaneously optimized. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimization methodologies. Herein, we cover the application of multi- and many-objective optimization methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyze how molecular properties used in the optimization process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimization for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field.
The field which is now known as evolutionary multi-objective optimization (EMOO) started in the mid-1980s and since then, it has experienced a significant growth. In this chapter, we discuss some of the (several) research topics that remain open and which, from the author’s perspective, are worth exploring in the next few years.
One innovation separating the total socioenvironmental system (TSES) analytical framework from other coupled human–nature systems is its focus on examining causal relationships between ecological/environmental, socioeconomic, and global climate changes. This chapter advocates the concept of latent variables or hypothetical constructs to investigate causal relationships between ecological, socioeconomic, and climate changes. The adoption of hypothetical constructs enables researchers to examine interactions between a large number of ecological, socioeconomic, and climate change variables and their complex socioenvironmental causal structures. The capability to model multiple sets of variables in multiple dimensions (causal structures) is an innovative attempt to develop big models for examining big data in the context of socioenvironmental sustainability studies. The other TSES innovation is to explore spatial and temporal lagged (legacy) effects of the causal relationships between various socioenvironmental factors. This chapter addresses historical or legacy effects from individual variables to hypothetical system structures. Therefore, this chapter illustrates that the TSES analytical framework can handle complex causal structures through latent variables and their temporal lagged effects (i.e., legacy effects).
This latent variable-based TSES framework is called the cross-lagged panel model (CLPM) for urban sustainability (US). This chapter introduces the literature and basic concepts concerning CLPM-US. It illustrates CLPM-US through a case study of urban sustainability of the 278 prefecture cities in China. The examinations include the formation of causal structures of urban sustainability, the exploration of temporal legacy effects of urban sustainability and their underlying socioenvironmental structures, the computation of urban sustainability scores and rankings, and the relationships between the sustainability rankings and the factor loadings of their underlying causal structures. Furthermore, this chapter provides an alternative approach, the multi-objective optimization problems (MOOPs) algorithm, to assess the relationships between the sustainability rankings and the loadings of their underlying structures.
The total system approach based on the society and nature coevolution theory is vital for examining urban sustainability. The urban sustainability measurements involve all sectors of human society and the environment. Introducing causal constructs through latent variables in CLPM is a workable solution to describe the interactions between these causal structures that determine urban sustainability status. Moreover, urban sustainability is an evolving concept involving history, representing the current, and predicting the future. Lagged and cross-lagged effects of an urban system and its underlying causal structures are critical traits of urban system evolutionary dynamics. Furthermore, the measurement of urban sustainability is not a single composite score but involves a large set of trade-offs between sub-aims of urban sustainability.
A number of data structures have been proposed for the storage and efficient update of unbounded sets of mutually non-dominating solutions. The recent ND-Tree has proved an effective data structure across a range of update environments, and may reasonably be considered the state-of-the-art. However, although it is efficient as a passive store of non-dominated solutions — which may be extracted at the end of an optimisation — its design is ill-suited to being an active source of parent solutions to directly exploit during a optimisation run. We introduce a number of modifications to the construction and maintenance of the ND-Tree to facilitate its use as an active archive (source of parents) during optimisation, and compare and contrast the run-time performance changes these cause (and discuss their drivers). Illustrations are provided with data sequences from a tunable generator and also a simple evolution strategy — but we emphasise such data structures are optimisation algorithm agnostic, and may be effectively integrated across the range of evolutionary (and non-evolutionary) optimisers.KeywordsData structuresReal-time statisticsReal-time analysisComputational efficiency
The Complete Coverage Path Planning (CCPP) is a key technology in the field of agricultural robots, and has great significance for improving the efficiency and quality of tillage, fertilization, harvesting, and other agricultural robot operations, as well as reducing the operation energy consumption. The traditional boustrophedon- or heuristic-search-algorithm-based CCPP methods, when coping with the field with irregular boundaries, obstacles, and other complex environments, still face many problems and challenges, such as large repeated work areas, multiple turns or U-turns, low operation efficiency, and prone to local optimum. In order to solve the above problems, an improved-genetic-algorithm-based CCPP method was proposed in this paper, the proposed method innovatively extends the traditional genetic algorithm’s chromosomes and single-point mutation into chromosome pairs and multi-point mutation, and proposed a multi-objective equilibrium fitness function. The simulation and experimental results on simple regular fields showed that the proposed improved-genetic-algorithm-based CCPP method achieved the comparable performance with the traditional boustrophedon-based CCPP method. However, on the complex irregular fields, the proposed CCPP method reduces 38.54% of repeated operation area and 35.00% of number of U-turns, and can save 7.82% of energy consumption on average. This proved that the proposed CCPP method has a strong adaptive capacity to the environment, and has practical application value in improving the efficiency and quality of agricultural machinery operations, and reducing the energy consumption.
Objective: Finding a subset of features is an issue that has been widely used in a variety of fields such as machine learning and statistical pattern recognition. Since increasing the number of features increases the computational cost of a system, it seems necessary to develop and implement systems with minimum features and acceptable efficiency. Methods: Considering objective, it's developmental research and in terms of two Meta-heuristic algorithms, namely genetic algorithm (GA) and multi-objective non-dominated sorting genetic algorithm (NSGA II). The multi-objective method compared to the single-objective method has reduced the number of features to 50% in all instances; it doesn't make much difference in classification accuracy. The proposed method is applied on six datasets of credit data, and the results were analyzed using two common classifiers namely, support vector machine (SVM) and K-nearest neighbors (KNN). Comparing two classifiers applied on datasets, K-nearest neighbors (KNN) compared to the support vector machine (SVM) has shown relatively better performance in increasing the classification accuracy and reducing the number of attributes.
The multi-objective optimization problem is difficult to solve with conventional optimization methods and algorithms because there are conflicts among several optimization objectives and functions. Through the efforts of researchers and experts from different fields for the last 30 years, the research and application of multi-objective evolutionary algorithms (MOEA) have made excellent progress in solving such problems. MOEA has become one of the primary used methods and technologies in the realm of multi-objective optimization. It is also a hotspot in the evolutionary computation research community. This survey provides a comprehensive investigation of MOEA algorithms that have emerged in recent decades and summarizes and classifies the classical MOEAs by evolutionary mechanism from the viewpoint of the search strategy. This paper divides them into three categories considering the search strategy of MOEA, i.e., decomposition-based MOEA algorithms, dominant relation-based MOEA algorithms, and evaluation index-based MOEA algorithms. This paper selects the relevant representative algorithms for a detailed summary and analysis. As a prospective research direction, we propose to combine the chaotic evolution algorithm with these representative search strategies for improving the search capability of multi-objective optimization algorithms. The capability of the new multi-objective evolutionary algorithm has been discussed, which further proposes the future research direction of MOEA. It also lays a foundation for the application and development of MOEA with these prospective works in the future.
Production optimization is of significance for carbonate reservoirs, directly affecting the sustainability and profitability of reservoir development. Traditional physics-based numerical simulations suffer from insufficient calculation accuracy and excessive time consumption when performing production optimization. We establish an ensemble proxy-model-assisted optimization framework combining the Bayesian random forest (BRF) with the particle swarm optimization algorithm (PSO). The BRF method is implemented to construct a proxy model of the injection–production system that can accurately predict the dynamic parameters of producers based on injection data and production measures. With the help of proxy model, PSO is applied to search the optimal injection pattern integrating Pareto front analysis. After experimental testing, the proxy model not only boasts higher prediction accuracy compared to deep learning, but it also requires 8 times less time for training. In addition, the injection mode adjusted by the PSO algorithm can effectively reduce the gas–oil ratio and increase the oil production by more than 10% for carbonate reservoirs. The proposed proxy-model-assisted optimization protocol brings new perspectives on the multi-objective optimization problems in the petroleum industry, which can provide more options for the project decision-makers to balance the oil production and the gas–oil ratio considering physical and operational constraints.
The control of matte grade determines the production cost of the copper smelting process. In this paper, an optimal matte-grade control model is established to derive the optimal matte grade with the objective of minimizing the cost in the whole process of copper smelting. This paper also uses the prediction capability of the BP (Backpropagation) neural network to establish a BP neural network prediction model for the matte grade, considering various factors affecting matte grade (including the input copper concentrate amount and its composition content, air drumming amount, oxygen drumming amount, melting agent amount, and other process parameters). In addition, the paper also uses the optimal matte grade to optimize the dosing, air supply/oxygen supply, and oxygen supply for the ISA and other furnaces. When using BP networks only, it is a nonconvex problem with gradient descent, which tends to fall into local minima and has some bias in the prediction results. This problem can be solved by optimizing its weights and thresholds through GA (Genetic Algorithm) to find the optimal solution. The analysis results show that the average absolute error of the simulation of the BP neural network prediction model for ice copper grade after GA optimization is 0.51%, which is better than the average absolute error of 1.17% of the simulation of the single BP neural network model.
The thesis mainly discuss about developing a indicator based MOES named as ISDE+ and its application to the appliance scheduling problem considering two conflicting objectives such as COST and User satisfaction in detail. The Novely lies in developing the IDSE+ algorithm and User satisfaction modelling from the energy disaggregation approach using the publicly available smart meter data.
Given a large number of applications and complex processing procedures, how to efficiently shift and schedule tax officers to provide good services to taxpayers is now receiving more attention from tax authorities. The availability of historical application data makes it possible for tax managers to shift and schedule staff with data support, but it is unclear how to properly leverage the historical data. To investigate the problem, this study adopts a user-centered design approach. We first collect user requirements by conducting interviews with tax managers and characterize their requirements of shifting and scheduling into time series prediction and resource scheduling problems. Then, we propose Tax-Scheduler, an interactive visualization system with a time-series prediction algorithm and genetic algorithm to support staff shifting and scheduling in the tax scenarios. To evaluate the effectiveness of the system and understand how non-technical tax managers react to the system with advanced algorithms and visualizations, we conduct user interviews with tax managers and distill several implications for future system design.
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