Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization

Tian, Ye; Zhang, Yajie; Su, Yansen; Zhang, Xingyi; Tan, Kay Chen; Jin, Yaochu

doi:10.1109/tcyb.2020.3021138

Cited by 107 publications

(34 citation statements)

References 48 publications

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“…Garcia et al [95] utilized the characteristics of cellular genetic algorithms (CGAs) and combined PPS technology to solve CMOPs. Tian et al [96] designed a twostage evolutionary algorithm, named CMOEA-MS, in which one stage can help the population reach the feasible region, and the other stage can make the population spread along the feasible boundary. In addition, based on the status of the population, the algorithm can adaptively switch between these two stages.…”

Section: Methods Of Transforming Cmops Into Other Problemsmentioning

confidence: 99%

“…Yang et al [67] used the MODE-SaE algorithm to deal with the steady-state bi-source compressed-air pipeline optimization problem. Tian et al [96] used the CMOEA-MS algorithm to solve the car side impact problem, vibration platform design problem, and water resource problem. Fan et al [19] proposed the PPS framework and achieved good results in solving the optimization problem of robot gripper.…”

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.

Self Cite

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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%

Section: A Engineering Design Problemsmentioning

confidence: 99%

A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

Self Cite

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“…Tian et al [17] proposed a weak coevolutionary framework in which one population solves the original CMOP while the other solves the constraints-ignored helper problem, and the two coevolved populations generate offspring on their own. Latter, Tian et al [25] proposed an adaptively switched two stage framework in which one stage handles the original CMOP while the other ignores constraints. Wang et al [18] proposed a two ranking based fitness evaluation mechanism in which CDP and Pareto dominance are both considered.…”

Section: Cmoeas Based On Constrained Dominance Principlementioning

confidence: 99%

An Ensemble Framework of Evolutionary Algorithm for Constrained Multi-Objective Optimization

Ming

Gong

2022

Symmetry

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In the real-world, symmetry or asymmetry widely exists in various problems. Some of them can be formulated as constrained multi-objective optimization problems (CMOPs). During the past few years, handling CMOPs by evolutionary algorithms has become more popular. Lots of constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been proposed. Whereas different CMOEAs may be more suitable for different CMOPs, it is difficult to choose the best one for a CMOP at hand. In this paper, we propose an ensemble framework of CMOEAs that aims to achieve better versatility on handling diverse CMOPs. In the proposed framework, the hypervolume indicator is used to evaluate the performance of CMOEAs, and a decreasing mechanism is devised to delete the poorly performed CMOEAs and to gradually determine the most suitable CMOEA. A new CMOEA, namely ECMOEA, is developed based on the framework and three state-of-the-art CMOEAs. Experimental results on five benchmarks with totally 52 instances demonstrate the effectiveness of our approach. In addition, the superiority of ECMOEA is verified through comparisons to seven state-of-the-art CMOEAs. Moreover, the effectiveness of ECMOEA on the real-world problems is also evaluated for eight instances.

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“…The CMOPs/CMaOPs can also be handled by adjusting the constraint parameters. The CMOEA-MS [23] adjusts the fitness evaluation strategies in the evolution process to adaptively balance objective optimization and constraint satisfaction. It can switch between the two stages according to the current population status, allowing the population to cross the infeasible area to the feasible area in one stage, and spread along the feasible boundary in the other stage.…”

Section: B Literature Reviewmentioning

confidence: 99%

A Constrained Multi/Many-Objective Particle Swarm Optimization Algorithm With a Two-Level Balance Scheme

Yang

Chen

et al. 2021

IEEE Access

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Constrained multi-objective optimization problems are common in practical engineering and are more difficult to handle than unconstrained problems. In general, it is necessary to find a balance between the convergence and diversity of solutions as well as the feasibility. For the constrained multi/many-objective optimization problem, a particle swarm optimization algorithm based on a two-level balance strategy is proposed. In contrast to existing views, the first level of the proposed algorithmic framework emphasizes convergence, while diversity and feasibility are considered together as the second-level scheme. An ensemble fitness ranking was used to improve the convergence of the proposed algorithm. To balance diversity and solution feasibility, the solutions are selected by combining the angles between the solutions using the constraint dominance principle. A penalty-based boundary-crossing approach is used as a utility function to calculate the fitness of the populations, which is compared with six state-of-the-art constrained multi/manyobjective evolutionary optimization algorithms on multiple constrained test suites, and the experimental results show that the proposed algorithm is highly competitive in most test problems. Furthermore, to illustrate the effect of different utility functions on the performance of the algorithm, the Chebyshev decomposition method is employed and compared with the former, and the results show that different utility functions need to be chosen to cope with problems of different characteristics.INDEX TERMS constrained multi/many-objective optimization, fitness ranking, constraint dominance principle, two-level balance scheme.

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Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization

Cited by 107 publications

References 48 publications

A Survey on Evolutionary Constrained Multiobjective Optimization

A Survey on Evolutionary Constrained Multiobjective Optimization

An Ensemble Framework of Evolutionary Algorithm for Constrained Multi-Objective Optimization

A Constrained Multi/Many-Objective Particle Swarm Optimization Algorithm With a Two-Level Balance Scheme

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