A Constrained Multi-Objective Evolutionary Algorithm Based on Boundary Search and Archive

Liu, Hailin; Peng, Chaoda; Gu, Fangqing; Wen, Junhai

doi:10.1142/s0218001416590023

Cited by 20 publications

(19 citation statements)

References 19 publications

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“…Then each subproblem is optimized by a coevolution strategy. Later, they proposed a constraint handing scheme based on boundary search and archive [85], in which a CMOP is decomposed into several subproblems, and each subproblem has its own archive. In addition, the boundary search strategy is constructed to improve the efficiency of the algorithm.…”

Section: Methods Of Transforming Cmops Into Other Problemsmentioning

confidence: 99%

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: Methods Of Transforming Cmops Into Other Problemsmentioning

confidence: 99%

A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

162

View full text Add to dashboard Cite

show abstract

“…Each UCMOPs and IM-CMOPs has two objectives. Nine algorithms (M2M-IEpsilon, CCMO [19], CMOEA-MS [21], cDPEA [22], ToP [20], MOEA/D-CDP [14], MOEA/D-DW [30], CM2M2 [29], and CM2M [28]) were operated 30 times independently using the two test instances. The experimental parameters for each algorithm are as follows:…”

Section: Experimental Settingsmentioning

confidence: 99%

“…Specifically, this method decomposes a population into a set of subpopulations, thereby maintaining the diversity of the population. In CMOPs, infeasible regions may make unconstrained PFs partially feasible, which may waste search effort on unpromising regions by using CM2M [28]. To effectively allocate search effort, CM2M2 [29] was proposed.…”

Section: Introductionmentioning

confidence: 99%

A Decomposition-based Evolutionary Multi-objective Optimization Method for Solving Constrained Optimization Problems with Imbalanced Objectives or Constraints

Fan

Wang

et al. 2022

Preprint

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In recent years, many effective constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed and successfully applied to address constrained multi-objective optimization problems (CMOPs). Nevertheless, few CMOEAs have fully explored CMOPs with imbalanced objectives or constraints. In this study, we propose a hybrid algorithm called M2M-IEpsilon to handle CMOPs with such characteristics, which combines an improved epsilon constraint-handling method (IEpsilon) with a multi-objective to multi-objective (M2M) decomposition strategy. The M2M decomposition mechanism divides a population into a set of sub-populations, which strengthens the diversity of the population. The IEpsilon constraint-handling method enables individuals with small constraint violation values to survive to the next generation, thus leading to a search for promising regions. In addition, a series of imbalanced CMOPs, named IM-CMOPs, is designed to verify the performance of the proposed M2M-IEpsilon algorithm. The comprehensive experimental results indicate that the proposed method can solve such imbalanced CMOPs well and perform significantly better than the other eight new or classical CMOEAs. Finally, we used the proposed M2M-IEpsilon to optimize the wellbore trajectory problem, and it achieved good results compared with previously developed algorithms.

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“…We choose eight test instances CTP1 ∼ CTP8 from the biobjective CTP-series (Deb, Pratap, & Meyarivan, 2001;Liu, Peng, Gu, & Wen, 2016), which are shown as follows:…”

Section: Test Instances and Performance Indexmentioning

confidence: 99%

“…Additionally, we use the popular performance metric Hypervolume (HV) (Liu et al, 2016) as the performance index. HV metric can demonstrate both the convergence and diversity of Pareto non-dominated solutions in a sense (see in Figure 4).…”

Section: Test Instances and Performance Indexmentioning

confidence: 99%

An improved constrained multi-objective optimization evolutionary algorithm for carbon fibre drawing process

Shen

2019

Systems Science & Control Engineering

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In this paper, an improved εCMOEA/D-DE is proposed to improve the performance of CMOP algorithm and achieve parameter optimization in carbon fibre drawing process. In order to avoid overusing the infeasible solutions, two repair operators are introduced in the population evolution model. More specially, during the evolutionary process, when the constraint violation of infeasible solution exceeds a tolerance threshold, the proposed two repair operators are used to find a better solution to repair the infeasible solution. On the other hand, in order to enhance the convergence rate of the Differential Evolution (DE), a modified DE is proposed. Then, an εCMOEA/D-mDE-RO is proposed by incorporating these two improved strategies into the εCMOEA/D-DE. Subsequently, the performance of the proposed εCMOEA/D-mDE-RO is evaluated on the constrained test problem series and the experiment shows that the proposed algorithm outperforms the existing εCMOEA/D-DE. Finally, in order to further illustrate the application potential, the proposed algorithm is successfully applied in optimizing carbon fibre drawing process and the optimal draw ratio vector is obtained. ARTICLE HISTORY

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A Constrained Multi-Objective Evolutionary Algorithm Based on Boundary Search and Archive

Cited by 20 publications

References 19 publications

A Survey on Evolutionary Constrained Multiobjective Optimization

A Survey on Evolutionary Constrained Multiobjective Optimization

A Decomposition-based Evolutionary Multi-objective Optimization Method for Solving Constrained Optimization Problems with Imbalanced Objectives or Constraints

An improved constrained multi-objective optimization evolutionary algorithm for carbon fibre drawing process

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