Journal
APPLIED SOFT COMPUTING
Volume 101, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2020.107042
Keywords
Multi-objective evolutionary algorithm; Steady-state constrained multi-objective; optimization; Co-evolution
Categories
Funding
- National Natural Science Foundation of China [61773106]
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Many MOEAs are developed to solve CMOPs, but they encounter low efficiency for steady-state CMOPs. This paper proposes a multi-objective evolutionary algorithm named FACE, which maintains the known feasible solution in the second population and evolves together with the main population. Performance comparisons show the efficiency and scalability of FACE for steady-state CMOPs.
Many multi-objective evolutionary algorithms (MOEAs) are developed to solve constrained multiobjective optimization problems (CMOPs). However, they encounter low efficiency for steady-state CMOPs which are to optimize a known feasible solution named the current steady-state operation point. This paper proposes a multi-objective evolutionary algorithm (MOEA), termed FACE, for tackling the steady-state CMOPs. In FACE, the known feasible solution is maintained in the second population and evolves together with the main population. The main population is evolved by global search without handling constraints to accelerate the convergence. And the second population is evolved by local search to hold and achieve more feasible solutions. Two kinds of performance comparison between FACE and five representative MOEAs are made: the first using two cases of steady-state multisource compressed-air pipeline optimization problems to evaluate the performance of FACE on real life applications, and the second using a set of bench-mark test problems of CMOPs assigned with a known feasible solution to further assess the scalability of FACE. The results demonstrate the efficiency and the scalability of FACE with a potential for steady-state CMOPs. (C) 2020 Elsevier B.V. All rights reserved.
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