期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 23, 期 2, 页码 303-315出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2018.2855411
关键词
Constraint handling; evolutionary algorithm (EA); decomposition-based technique; multiobjective optimization; two-archive strategy
资金
- Royal Society [IEC/NSFC/170243]
- Ministry of Science and Technology of China [2017YFC0804003]
- Science and Technology Innovation Committee Foundation of Shenzhen [ZDSYS201703031748284]
- Shenzhen Peacock Plan [KQTD2016112514355531]
- EPSRC [EP/J017515/1, EP/P005578/1]
- Royal Society Industry Fellowship [IF160108]
- EPSRC [EP/J017515/1, EP/P005578/1] Funding Source: UKRI
- UKRI [MR/S017062/1] Funding Source: UKRI
When solving constrained multiobjective optimization problems, an important issue is how to balance convergence, diversity, and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, a two-archive evolutionary algorithm, for constrained multiobjective optimization. It maintains two collaborative archives simultaneously: one, denoted as the convergence-oriented archive (CA), is the driving force to push the population toward the Pareto front; the other one, denoted as the diversity-oriented archive (DA), mainly tends to maintain the population diversity. In particular, to complement the behavior of the CA and provide as much diversified information as possible, the DA aims at exploring areas under-exploited by the CA including the infeasible regions. To leverage the complementary effects of both archives, we develop a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status. Comprehensive experiments on a series of benchmark problems and a real-world case study fully demonstrate the competitiveness of our proposed algorithm, in comparison to five state-of-the-art constrained evolutionary multiobjective optimizers.
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