A chaotic differential evolution and symmetric direction sampling for large-scale multiobjective optimization

Abstract

As large-scale multiobjective optimization problems (LSMOPs) contain many decision variables, most existing large-scale multiobjective optimization algorithms (LSMOEAs) require millions or even tens of millions of function evaluations to obtain high-quality solutions, which is unaffordable for many real-world problems. Moreover, few studies are dedicated to solving LSMOPs under limited function evaluations. This article proposes a Chaotic Differential Evolution and Symmetric Direction Sampling for large-scale multiobjective optimization (CDE-SDS). First, based on the decision variable information of nondominated solutions, a chaotic differential evolution strategy is adopted to accelerate the convergence of the population towards the Pareto front. Second, a symmetric direction sampling strategy is employed, which generates sampling solutions by constructing symmetric search directions in a large-scale decision space. Thus, the high-dimensional decision space can be explored more fully. The two strategies optimize the population alternately to balance exploitation and exploration. The performance of CDE-SDS is compared with seven state-of-the-art large-scale multiobjective evolution algorithms on an artificial test suit LSMOP with 500 to 5000 decision variables and a real-world time-varying ratio error estimation problem with 3000 decision variables. Experimental results show that the performance of CDE-SDS significantly outperforms the seven compared algorithms in terms of both diversity and convergence of solutions under limited function evaluations.