Multi-objective Optimization Based Differential Evolution Constrained Optimization Algorithm

Abstract

Based on differential evolution algorithm and the concept of multi-objective optimization, a differential evolution algorithm for constrained optimization problem based on multi-objective constraint handling is proposed in this paper. This proposed algorithm can achieve global search and local search effectively by using of the differential evolution algorithm. For the constrained optimization problems, this paper presents a new comparison mechanism based on the concept of Pareto optimal solution. The grades of Pareto optimal solutions, feasible solutions and infeasible solutions are prescribed and enable them to be selected at different probabilities during the evolutionary process, which can lead the search towards the direction of the global optimum. In addition, an infeasible solution of replace mechanism is also given. When the algorithm gets into a local optimal, we use infeasible solution, which contains useful information, to replace those redundant and repeat feasible solutions to improve the exploration capabilities of the algorithm in the search space. Compared to Evolutionary Algorithm based on Homomorphous Maps (EAHM), Artificial Immune Response Constrained Evolutionary Strategy (AIRCES), Constraint Handling Differential Evolution (CHDE) and Evolutionary Strategies based on Stochastic Ranking (ESSR), the results of the 13 Standard tests show that the proposed algorithm has certain advantages in the convergence speed and solution accuracy.