Directed mating using inverted PBI function for constrained multi-objective optimization

Miyakawa, Minami; Takadama, Keiki; Satō, Hiroyuki

doi:10.1109/cec.2015.7257253

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…In order to solve CMOPs with a large number of objectives or variable dimensions, He et al [115] emphasized the role of offspring generation when reproducing promising feasible or valuable infeasible offspring solutions. Miyakawa et al [116] proposed a direct matching strategy, which can assign a unique search direction to individuals according to their positions in the objective space. Afterwards, they further proposed a method to control the selection area of promising infeasible solutions by determining the dominant region of the solutions [117].…”

Section: F Methods Of Altering the Reproduction Operatorsmentioning

confidence: 99%

A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

137

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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: F Methods Of Altering the Reproduction Operatorsmentioning

confidence: 99%

A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

137

View full text Add to dashboard Cite

show abstract

“…So far, we have focused on ways to select parents on the concept of the directed mating and proposed some variants of the directed mating [5,6]. However, there is a problem in the crossover operator combined with the directed mating, and this work focuses on it.…”

Section: Introductionmentioning

confidence: 98%

“…In our previous studies [3,5,6], we have used the conventional uniform crossover [7] with the directed mating. In this crossover, almost half genes of an offspring are copied from an infeasible secondary parent, and other genes are copied from a feasible primary parent.…”

Section: Introductionmentioning

confidence: 99%

Control of Crossed Genes Ratio for Directed Mating in Evolutionary Constrained Multi-Objective Optimization

Miyakawa

Takadama

Satō

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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As an evolutionary approach to solve constrained multi-objective optimization problems (CMOPs), an algorithm using the two-stage non-dominated sorting and the directed mating (TNSDM) has been proposed. To generate offspring, the directed mating utilizes useful infeasible solutions having better objective values than feasible solutions in the population. The directed mating achieves higher search performance than the conventional mating which avoids using infeasible solutions in several CMOPs. However, since the directed mating uses infeasible solutions, generated offspring tend to be infeasible compared with the conventional mating. To further improve the effectiveness of the directed mating by improving the feasibility of generated offspring, in this work we propose a method to control the crossed genes ratio in the directed mating. In this method, we control the amount of genes copied from infeasible parents to offspring in the directed mating. Experimental results using m-objective k-knapsack problem with 2-4 objectives show the contribution of the directed mating for the search performance is further improved by controlling crossed genes ratio.

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Evolutionary constrained multi-objective optimization: a review

Liang,

Lin,

Yue

et al. 2024

Vicinagearth

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Solving constrained multi-objective optimization problems (CMOPs) is challenging due to the simultaneous consideration of multiple conflicting objectives that need to be optimized and complex constraints that need to be satisfied. To address this class of problems, a large number of constrained multi-objective evolutionary algorithms (CMOEAs) have been designed. This paper presents a comprehensive review of state-of-the-art algorithms for solving CMOPs. First, the background knowledge and concepts of evolutionary constrained multi-objective optimization are presented. Then, some classic constraint handling technologies (CHTs) are introduced, and the advantages and limitations of each CHT are discussed. Subsequently, based on the mechanisms used by these algorithms, the CMOEAs are classified into six categories, each of which is explained in detail. Following that, the benchmark test problems used to evaluate the algorithm’s performance are reviewed. Moreover, the experimental comparison and performance analysis of different types of algorithms are carried out on different test problems with different characteristics. Finally, some of the challenges and future research directions in evolutionary constrained multi-objective optimization are discussed.

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Directed mating using inverted PBI function for constrained multi-objective optimization

Cited by 5 publications

References 21 publications

A Survey on Evolutionary Constrained Multiobjective Optimization

A Survey on Evolutionary Constrained Multiobjective Optimization

Control of Crossed Genes Ratio for Directed Mating in Evolutionary Constrained Multi-Objective Optimization

Evolutionary constrained multi-objective optimization: a review

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