A Multi-Algorithm Balancing Convergence and Diversity for Multi-Objective Optimization

As a population-based method, evolutionary algorithms have been extensively used to solve multi-objective optimization problems. However, most of the current multi-objective evolutionary algorithms (MOEAs) cannot strike a good balance between the closeness to the true Pareto front and the uniform distribution of non-dominated solutions. In this paper, we present a multi-algorithm, MABNI, which is based on two popular MOEAs, NSGA-II and IBEA. The proposed algorithm is inspired from the strengths and weaknesses of the two algorithms, e.g., the former can preserve extreme solutions effectively but has a worse diversity while the latter shows a better convergence and makes non-dominated solutions more evenly distributed but easily suffers losses of extreme solutions. In MABNI, modified NSGA-II and IBEA run alternatively and the update principle for the archive population is based on the distances to nearest neighbors. Furthermore, accompanied with preservation of extreme points, an improved differential evolution is employed to speed the search. The performance of MABNI is examined on ZDT-series and DTLZ-series test instances in terms of the selected performance indicators. Compared with NSGA-II and IBEA, the results indicate that MABNI can reach a better balance between convergence and diversity for the approximation of the true Pareto front and obtain more stable results.