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Abstract
When solving constrained multi-objective 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 multi-objective 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 multi-objective optimizers.
Original language | English |
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Pages (from-to) | 303-315 |
Number of pages | 13 |
Journal | IEEE Transactions on Evolutionary Computation |
Volume | 23 |
Issue number | 2 |
Early online date | 19 Jul 2018 |
DOIs | |
Publication status | Published - Apr 2019 |
Keywords
- Multi-objective optimization
- constraint handling
- evolutionary algorithm
- two-archive strategy
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Dive into the research topics of 'Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization'. Together they form a unique fingerprint.Projects
- 1 Finished
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Adaptive Tools for Electromagnetics and Materials Modelling to Bridge the Gap between Design and Manufacturing (AOTOMAT) - (QMUL Led ECSA1X7R EP/P005578/1)
Yao, X. (Principal Investigator)
Engineering & Physical Science Research Council
1/12/16 → 30/11/19
Project: Research Councils