Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective Optimization


The average ranking (AR) method has been shown highly effective to provide sufficient selection pressure searching towards Pareto optimal set in many-objective optimization. However, as lack of diversity maintenance mechanism, the obtained final set may only concentrate in a subregion of Pareto front. In this paper, we propose a diversity maintenance strategy for AR to balance convergence and diversity during evolution process. We employ grid to define an adaptive neighborhood for each individual, whose size varies with the number of objectives. Moreover, a layering selection scheme integrates it and AR, to pick out well-converged individuals and prohibit or postpone the archive of adjacent individuals. From an extensive comparative study with original AR, and two other diversity maintenance methods, the proposed method shows a good balance among convergence, uniformity and spread.