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 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-theart algorithms, our algorithm remarkably outperforms them in terms of the best, median, mean, and worst objective function values and the standard deviations.