Differential evolution with infeasible-guiding mutation operators for constrained multi-objective optimization

Xu, Bin; Duan, Wei; Zhang, Haifeng; Li, Zeqiu

doi:10.1007/s10489-020-01733-0

Cited by 18 publications

(6 citation statements)

References 57 publications

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“…Yu et al [39] attempted to control the opportunity of an individual to generate offspring, where a feasible solution conducts genetic operations with a high probability. In the method proposed by Xu et al [40] , feasible solutions and infeasible solutions adopt different mutation strategies to make the population evolve toward the feasible regions as soon as possible. Liu et al [41] divided the whole population into multiple subpopulations, where each subpopulation adopted different crossover strategies.…”

Section: B Multiple-operator Methodsmentioning

confidence: 99%

Process Knowledge-Guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems

Zuo

Gong

Wang

et al. 2024

IEEE Trans. Evol. Computat.

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Various real-world problems can be attributed to constrained multi-objective optimization problems. Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for constrained multi-objective optimization problems. Given this, a process knowledge-guided constrained multi-objective autonomous evolutionary optimization method is proposed. Firstly, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving constrained multi-objective optimization problems.

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Section: B Multiple-operator Methodsmentioning

confidence: 99%

Process Knowledge-Guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems

Zuo

Gong

Wang

et al. 2024

IEEE Trans. Evol. Computat.

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“…Qu and Suganthan [112] proposed a diversity promotion mechanism to prevent the population from falling into the local optimum. Xu et al [33] designed a new DE variant with an infeasible-guiding mutation operator to solve CMOPs, in which the good infeasible solutions are employed to guide the population into the promising region. Wang et al [113] proposed an adaptive DE with Pareto dominance, in which the parameters are adjusted adaptively.…”

Section: F Methods Of Altering the Reproduction Operatorsmentioning

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%

“…Peng et al [29] also applied their algorithm to these problems. Xu et al [33] used IMDE to address four common engineering design problems, i.e., the design of welding beam, disk brake design problem, speed reducer design problem, and the spring design problem. Sadollah et al [126] solved six practical engineering problems by using the MOWCA algorithm.…”

Section: A Engineering Design Problemsmentioning

confidence: 99%

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A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

121

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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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“…The whole search process was divided into exploration and exploitation, and two populations evolved with and without considering the constraints. Based on the DE [17], a new DE variant named IMDE [18] was proposed, which used infeasible solutions to guide mutation operators and applied multiple combinations of mutation strategies and control parameters to enhance the search performance. Yu et al [19] proposed a dynamic selection preferenceassisted constrained multiobjective differential evolutionary algorithm (DSPCMDE).…”

Section: Literature Review 21 Constrained Multi-objective Evolutionar...mentioning

confidence: 99%

Self-Adaptive Constrained Multi-Objective Differential Evolution Algorithm Based on the State–Action–Reward–State–Action Method

Liu¹,

Cui²,

Fan³

2022

Mathematics

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The performance of constrained multi-objective differential evolution algorithms (CMOEAs) is mainly determined by constraint handling techniques (CHTs) and their generation strategies. To realize the adaptive adjustment of CHTs and generation strategies, an adaptive constrained multi-objective differential evolution algorithm based on the state–action–reward–state–action (SARSA) approach (ACMODE) is introduced in the current study. In the proposed algorithm, the suitable CHT and the appropriate generation strategy can be automatically selected via a SARSA method. The performance of the proposed algorithm is compared with four other famous CMOEAs on five test suites. Experimental results show that the overall performance of the ACMODE is the best among all competitors, and the proposed algorithm is capable of selecting an appropriate CHT and a suitable generation strategy to solve a particular type of constrained multi-objective optimization problems.

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Differential evolution with infeasible-guiding mutation operators for constrained multi-objective optimization

Cited by 18 publications

References 57 publications

Process Knowledge-Guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems

Process Knowledge-Guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems

A Survey on Evolutionary Constrained Multiobjective Optimization

Self-Adaptive Constrained Multi-Objective Differential Evolution Algorithm Based on the State–Action–Reward–State–Action Method

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