constrained multimodal multi-objective optimization problems (CMMOPs) commonly arise in practical problems in which multiple Pareto optimal sets (POSs) correspond to one Pareto optimal front (POF). The existence of co...
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constrained multimodal multi-objective optimization problems (CMMOPs) commonly arise in practical problems in which multiple Pareto optimal sets (POSs) correspond to one Pareto optimal front (POF). The existence of constraints and multimodal characteristics makes it challenging to design effective algorithms that promote diversity in the decision space and convergence in the objective space. Therefore, this paper proposes a novel constrained multimodal multi-objective evolutionary algorithm, namely CM-MOEA, to address CMMOPs. In CM-MOEA, an adaptive epsilon-constrained method is designed to utilize promising infeasible solutions, promoting exploration in the search space. Then, a diversity-based offspring generation method is performed to select diverse solutions for mutation, searching for more equivalent POSs. Furthermore, the two-level environmental selection strategy that combines local and global environmental selection is developed to guarantee diversity and convergence of solutions. Finally, we design an archive update strategy that stores well-distributed excellent solutions, which more effectively approach the true POF. The proposed CM-MOEA is compared with several state-of-the-art algorithms on 17 test problems. The experimental results demonstrate that the proposed CM-MOEA has significant advantages in solving CMMOPs.
Solving multimodalmulti-objective optimization problems (MMOPs) has received increasing attention. How-ever, recent studies only consider unconstrained MMOPs. Given the fact that there are usually constraints in real...
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Solving multimodalmulti-objective optimization problems (MMOPs) has received increasing attention. How-ever, recent studies only consider unconstrained MMOPs. Given the fact that there are usually constraints in real-world optimization problems, in this work, we propose a test problem construction approach for constrained multimodal multi-objective optimization. Based on the approach, a test suite, containing 14 instances with diverse features and difficulties, is created. Meanwhile, a new evolutionary framework is tailored for this kind of problem. We test the proposed framework in the experiments and compare it to state-of-the-art multimodalmulti-objective optimization algorithms on the proposed test suite. The results reveal that the proposed test suite is challenging and it can motivate researchers to develop new algorithms. In addition, the superiority of our proposed framework demonstrates its effectiveness in handling constrained MMOPs.
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