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CombiningMultiobjective Optimization with

DifferentialEvolution to Solve Constrained

OptimizationProblems

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چکیده لاتین مقاله به صورت ذیل میباشد :

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 proposedby the authors. However, its main shortcoming is that a trial-anderror processhas to be used to choose suitable parameters. To overcome the aboveshortcoming, this paper proposes an improved version of the CW method, calledCMODE, which combines multiobjective optimization with differentialevolution  to deal with constrainedoptimization problems. Like its predecessor CW, the comparison of individualsin CMODE is also based on multiobjective optimization. In CMODE, however,differential evolution serves as the search engine. In addition, a novelinfeasible solution replacement mechanism based on multiobjective optimizationis proposed, with the purpose of guiding the population toward promising solutionsand the feasible region simultaneously. The performance of CMODE is evaluatedon 24 benchmark test functions. It is shown empirically that CMODE is capableof producing highly competitive results compared with some otherstate-of-the-art approaches in the

community of constrained evolutionary optimization.