Combining Multiobjective Optimization With Differential Evolution to Solve Constrained Optimization Problems

During the past decade, solving constrained optimization problems with evolutionary algorithms has received considerable attention among researchers and practitioners. Cai and Wang's method (abbreviated as CW method) is a recent constrained optimization evolutionary algorithm proposed by the au...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2012-02, Vol.16 (1), p.117-134
Main Authors: Wang, Yong, Cai, Zixing
Format: Article
Language:English
Subjects:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Artificial intelligence
Benchmark testing
Computer science
control theory
systems
Constrained optimization problems
constraint-handling technique
Decision theory. Utility theory
differential evolution
Diversity reception
Evolutionary computation
Exact sciences and technology
Mathematical programming
multiobjective optimization
Operational research and scientific management
Operational research. Management science
Optimization
Pareto optimization
Proposals
Studies
Theoretical computing
Vectors
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Summary:During the past decade, solving constrained optimization problems with evolutionary algorithms has received considerable attention among researchers and practitioners. Cai and Wang's method (abbreviated as CW method) is a recent constrained optimization evolutionary algorithm proposed by the authors. However, its main shortcoming is that a trial-and-error process has to be used to choose suitable parameters. To overcome the above shortcoming, this paper proposes an improved version of the CW method, called CMODE, which combines multiobjective optimization with differential evolution to deal with constrained optimization problems. Like its predecessor CW, the comparison of individuals in CMODE is also based on multiobjective optimization. In CMODE, however, differential evolution serves as the search engine. In addition, a novel infeasible solution replacement mechanism based on multiobjective optimization is proposed, with the purpose of guiding the population toward promising solutions and the feasible region simultaneously. The performance of CMODE is evaluated on 24 benchmark test functions. It is shown empirically that CMODE is capable of producing highly competitive results compared with some other state-of-the-art approaches in the community of constrained evolutionary optimization.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2010.2093582