A constrained optimization evolutionary algorithm based on multiobjective optimization techniques

This paper presents a novel evolutionary algorithm for constrained optimization. During the evolutionary process, our algorithm is based on multiobjective optimization techniques, i.e., an individual in the parent population may be replaced if it is dominated by a nondominated individual chosen from...

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Bibliographic Details
Published in:2005 IEEE Congress on Evolutionary Computation Vol. 2; pp. 1081 - 1087 Vol. 2
Main Authors: Yong Wang, Zixing Cai
Format: Conference Proceeding
Language:English
Published: IEEE 2005
Subjects:
Benchmark testing
Constraint optimization
Educational institutions
Evolutionary computation
Information science
Linear programming
Robustness
ISBN:0780393635, 9780780393639
ISSN:1089-778X
Online Access:Get full text
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Summary:This paper presents a novel evolutionary algorithm for constrained optimization. During the evolutionary process, our algorithm is based on multiobjective optimization techniques, i.e., an individual in the parent population may be replaced if it is dominated by a nondominated individual chosen from the offspring population. In addition, a model of population-based algorithm-generator and an infeasible solutions archiving and replacement mechanism are introduced. Furthermore, the simplex crossover is used as a recombination operator to enrich the exploration and exploitation abilities of the approach proposed. The new approach is tested on thirteen well-known benchmark functions, and the empirical evidences suggest that it is robust, efficient and generic when handling linear/nonlinear equality/inequality constraints. Compared with some other state-of-the-art algorithms, our algorithm remarkably outperforms them in terms of the best, median, mean, and worst objective function values and the standard deviations.
ISBN:0780393635
9780780393639
ISSN:1089-778X
DOI:10.1109/CEC.2005.1554811