Two novel schemes of selecting the current best solutions for multiobjective differential evolution are proposed in this paper. Based on the search biases strategy suggested by Runarsson and Yao, a hybrid of multiobjective differential evolution and genetic algorithm with (N+N) framework for constrained MOPs is given. And then the hybrid algorithm adopting the two schemes respectively is compared with the constrained NSGA-II on 4 benchmark functions constructed by Deb. The experimental results show that the hybrid algorithm has better performance, especially in the distribution of non-dominated set.
['Min Zhang', 'Huantong Geng', 'Wenjian Luo', 'Linfeng Huang', 'Xufa Wang']
A hybrid of differential evolution and genetic algorithm for constrained multiobjective optimization problems