Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization

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...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 23; no. 2; pp. 303 - 315
Main Authors: Li, Ke, Chen, Renzhi, Fu, Guangtao, Yao, Xin
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Archives & records
Constraint handling
Convergence
decomposition-based technique
evolutionary algorithm (EA)
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Linear programming
Mathematical programming
multiobjective optimization
Multiple objective analysis
Optimization
Parents
Sociology
Sorting
Statistics
two-archive strategy
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary: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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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2018.2855411