A Constrained Optimization Evolutionary Algorithm Based on Multiobjective Optimization Techniques

Abstract—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 indivi-dual 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 infea-sible 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 outper-forms them in terms of the best, median, mean, and worst objective function values and the standard deviations