Push and Pull Search for Solving Constrained Multi-objective Optimization Problems

By Zhun Fan, Wenji Li, Xinye Cai, Hui Li, Caimin Wei, Qingfu Zhang, Kalyanmoy Deb, Erik D. Goodman. Swarm and Evolutionary Computation, 2017

515 citations recorded by the source corpus.

This paper proposes a push and pull search (PPS) framework for solving constrained multi-objective optimization problems (CMOPs). To be more specific, the proposed PPS divides the search process into two different stages, including the push and pull search stages. In the push stage, a multi-objective evolutionary algorithm (MOEA) is adopted to explore the search space without considering any constraints, which can help to get across infeasible regions very fast and approach the unconstrained Pareto front. Furthermore, the landscape of CMOPs with constraints can be probed and estimated in the push stage, which can be utilized to conduct the parameters setting for constraint-handling approaches applied in the pull stage. Then, a constrained multi-objective evolutionary algorithm (CMOEA) equipped with an improved epsilon constraint-handling is applied to pull the infeasible individuals achieved in the push stage to the feasible and non-dominated regions. Compared with other CMOEAs, the proposed PPS method can more efficiently get across infeasible regions and converge to the feasible and non-dominated regions by applying push and pull search strategies at different stages. To evaluate the performance regarding convergence and diversity, a set of benchmark CMOPs is used to test the proposed PPS and compare with other five CMOEAs, including MOEA/D-CDP, MOEA/D-SR, C-MOEA/D, MOEA/D-Epsilon and MOEA/D-IEpsilon. The comprehensive experimental results demonstrate that the proposed PPS achieves significantly better or competitive performance than the other five CMOEAs on most of the benchmark set.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/34727366e7c2b3c4d55e5036679d5a62079da95c

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Citations in the corpus

A Survey on Evolutionary Constrained Multiobjective Optimization (2023, methodology): “Fan et al. [19] proposed the PPS framework and achieved good results in solving the optimization problem of robot gripper.”
A Coevolutionary Framework for Constrained Multiobjective Optimization Problems (2021, methodology): “This section veriﬁes the performance of the proposed CCMO by comparing it to the constrained NSGA-II [13], PPS [15], C-TAEA [14], and ToP [10] on the constrained DTLZ test suite [14], [25], the MW test suite [6], the LIR-CMOP test suite [44], and the DOC test suite [10].”
Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization (2021, methodology): “Five popular CMOEAs (i.e., TiGE-2 [20], NSGA-II-CDP [9], C-TAEA [10], PPS [11], and ToP [14]) are selected as baselines.”
Evolutionary Large-Scale Multi-Objective Optimization: A Survey (2021, background): “However, a number of MOEAs have been proposed to solve constrained multi-objective optimization problems with a few decision variables, most of which are based on coevolutionary frameworks or multi-stage frameworks [47, 92, 170].”
Handling Constrained Multiobjective Optimization Problems With Constraints in Both the Decision and Objective Spaces (2019, background): “Recently, a push and pull search (PPS) framework is embedded into MOEA/D for solving CMOPs [37].”