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مقاله IEEE با عنوان
Combining
Multiobjective Optimization with
Differential
Evolution to Solve Constrained
Optimization
Problems
را پیاده سازی
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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 proposed
by the authors. However, its main shortcoming is that a trial-anderror 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.