A multiobjective differential evolution algorithm for constrained optimization

Recently, using multiobjective optimization concepts to solve the constrained optimization problems (COPs) has attracted much attention. In this paper, a novel multiobjective differential evolution algorithm, which combines several features of previous evolutionary algorithms (EAs) in a unique manner, is proposed to COPs. Our approach uses the orthogonal design method to generate the initial population; also the crossover operator based on the orthogonal design method is employed to enhance the local search ability. In order to handle the constraints, a novel constraint-handling method based on Pareto dominance concept is proposed. An archive is adopted to store the nondominated solutions and a relaxed form of Pareto dominance, called ε-dominance, is used to update the archive. Moreover, to utilize the archive solution to guide the search, a hybrid selection mechanism is proposed. Experiments have been conducted on 13 benchmark COPs. And the results prove the efficiency of our approach. Compared with five state-of-the-art EAs, our approach provides very good results, which are highly competitive with those generated by the compared EAs in constrained evolutionary optimization. Furthermore, the computational cost of our approach is relatively low.