An evolutionary algorithm with directed weights for constrained multi-objective optimization

Peng, Chaoda; Liu, Hai Lin; Gu, Fangqing

doi:10.1016/j.asoc.2017.06.053

Cited by 91 publications

(37 citation statements)

References 18 publications

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“…Long [80] constructed a novel CHT for solving CMOPs, in which the convergence, diversity, and feasibility of the obtained solutions are used as three new objectives for the multiobjective subproblem. Taking the constraint violation degree as a new objective, Peng et al [29] designed a new CHT based on directed weights to solve CMOPs, in which two types of weights respectively distributed in feasible and infeasible regions are employed to guide the search toward the promising regions. Zhou et al [81] proposed a tri-goal evolutionary framework for solving constrained many-objective problems.…”

Section: Multi-objective Methodsmentioning

confidence: 99%

“…1, we use a taxonomy referring to the CHTs instead of the type of MOEAs. The existing CMOEAs can be divided into seven categories: 1) methods based on penalty function [24,25]; 2) methods based on the separation of objectives and constraints [26,27]; 3) multi-objective methods [28,29]; 4) methods of transforming CMOPs into other problems [19,30]; 5) hybrid methods [31,32]; 6) methods of altering the reproduction operators [33,34]; and 7) other methods.…”

Section: Summary Of Existing Cmoeasmentioning

confidence: 99%

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A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

162

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Handling constrained multi-objective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMOPs, numerous constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed in recent years, and they have achieved promising performance. However, there has been few literature on the systematic review of the related studies currently. This article provides a comprehensive survey for evolutionary constrained multi-objective optimization. We first review a large number of CMOEAs through categorization, and analyze their advantages and drawbacks in each category. Then, we summarize the benchmark test problems and investigate the performance of different constraint handling techniques and different algorithms, followed by some emerging and representative applications of CMOEAs. Finally, we discuss some new challenges and point out some directions of the future research in the field of evolutionary constrained multi-objective optimization.

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Section: Multi-objective Methodsmentioning

confidence: 99%

Section: Summary Of Existing Cmoeasmentioning

confidence: 99%

A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

162

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“…To improve the population diversity, Li et al [35] developed a method in which the worst solution is given a second chance for survival when it is associated with an isolated subregion. To utilize useful information included in infeasible solutions, Peng et al [36] introduced infeasible weights, which change with smaller constraint violation values and better objective function values, to maintain many well-diversified infeasible individuals. Ning et al [37] proposed a constrained nondominated sorting rank approach in which each solution is associated with a constrained nondomination rank in accordance with its Pareto rank and constraint rank.…”

Section: A Constraint Handling Techniquesmentioning

confidence: 99%

A Novel Evolutionary Algorithm for Dynamic Constrained Multiobjective Optimization Problems

Chen

Ding

Yang

et al. 2020

IEEE Trans. Evol. Computat.

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Abstract-To promote research on dynamic constrained multiobjective optimization, we first propose a group of generic test problems with challenging characteristics, including different modes of the true Pareto front (e.g., convexity-concavity and connectedness-disconnectedness) and the changing feasible region. Subsequently, motivated by the challenges presented by dynamism and constraints, we design a dynamic constrained multiobjective optimization algorithm with a nondominated solution selection operator, a mating selection strategy, a population selection operator, a change detection method, and a change response strategy. The designed nondominated solution selection operator can obtain a nondominated population with diversity when the environment changes. The mating selection strategy and population selection operator can adaptively handle infeasible solutions. If a change is detected, the proposed change response strategy reuses some portion of the old solutions in combination with randomly generated solutions to reinitialize the population, and a steady-state update method is designed to improve the retained previous solutions. Experimental results show that the proposed test problems can be used to clearly distinguish the performance of algorithms, and that the proposed algorithm is very competitive for solving dynamic constrained multiobjective optimization problems in comparison with state-of-the-art algorithms. Index Terms-Dynamic constrained multiobjective optimization, test problems, population selection, change response I. INTRODUCTION ANY real-world optimization problems, particularly online optimization problems or optimal control problems, involve multiple objectives and constraints that may change over time throughout the optimization [1]; such problems can be called dynamic constrained multiobjective optimization problems (DCMOPs). Without loss of generality,

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“…By calculating, in the monolayer evaporation phase (i.e. of the gene information in the offspring come from the parent based on the improved normalization method of fitness as shown in (12). Since the evaporation probability MEP only determines the renewal rate of individuals rather than other aspects such as learning objects, the representative 30dimensional SumSquares function is used to study the renewal rate of individuals.…”

Section: ) the Design Of Fitness Fitmentioning

confidence: 99%

“…According to a set of weight vectors, DAA divides the whole objective space into a number of subspaces, and at each generation, only one nondominated solution lying in a subspace is chosen to be used for updating the external archive in consideration of its diversity [11]. Peng et al proposed an evolutionary algorithm with directed weights for constrained multi-objective optimization, which uses feasible weight vectors and infeasible weight vectors distributed in feasible and infeasible domains to guide the population to the search the promising region, and dynamically adjusts infeasible weight vectors with the evolution to prefer infeasible individuals with better objective values and smaller constraint violations [12]. In 2019, Fan et al proposed the MOEA/D-ACDP algorithm for constrained multi-objective problems.…”

Section: Introductionmentioning

confidence: 99%

Constrained Multi-Objective Water Evaporation Optimization Algorithm Based on Decomposition With ε-Constraint Handling Technology

2020

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In order to solve the constrained multi-objective problem more effectively, the constrained multi-objective water evaporation optimization algorithm based on decomposition with ε-constraint handling technology (MOEA/D-WEO-εCHT) is proposed in this paper. MOEA/D-WEO-εCHT uses the MOEA/D framework to deal with multi-objective, ε-constraint handling technology to handle constraints and WEO algorithm to perform the main body evolution operation. Firstly, an external memory population in which the abandoned excellent parent can be kept is established for each subproblem, which make the abandoned parent be fully utilized by participating in the subsequent evolution. Secondly, some strategies of WEO, such as calculation and normalization method of individual fitness and calculation method of step size S, are improved to make WEO suitable for solving constrained multi-objective problems and helpful to obtain the Pareto solution set with better convergence and distribution. Thirdly, the ε(t) calculation method and retention mechanism of individuals are improved to reasonably coordinate feasible and infeasible individuals in different evolution stages. To evaluate the performance of the proposed algorithm, the CTP and CF benchmark instances and an engineering optimization problem are studied. The experimental results illustrate that MOEA/D-WEO-εCHT is more effective than other related algorithms on these benchmark instances, in terms of convergence and distribution of the obtained solution sets.

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An evolutionary algorithm with directed weights for constrained multi-objective optimization

Cited by 91 publications

References 18 publications

A Survey on Evolutionary Constrained Multiobjective Optimization

A Survey on Evolutionary Constrained Multiobjective Optimization

A Novel Evolutionary Algorithm for Dynamic Constrained Multiobjective Optimization Problems

Constrained Multi-Objective Water Evaporation Optimization Algorithm Based on Decomposition With ε-Constraint Handling Technology

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