A Clustering-Based Evolutionary Algorithm for Many-Objective Optimization Problems

This paper suggests a novel clustering-based evolutionary algorithm for many-objective optimization problems. Its main idea is to classify the population into a number of clusters, which is expected to solve the difficulty of balancing convergence and diversity in high-dimensional objective space. The individuals showing high similarities on the vector angles are gathered into the same cluster, such that the population's distribution can be well portrayed by the clusters. To efficiently find these clusters, partitional clustering is first used to classify the union population into in main clusters based on the m axis vectors (m is the number of objectives), and then hierarchical clustering is further run on these in main clusters to get N final clusters (N is the population size and N > m). At last, in environmental selection, one individual from each of N clusters closest to the axis vectors is selected to maintain diversity, while one individual from each of the other clusters is preferred by a simple convergence indicator to ensure convergence. When tackling some well-known test problems with 5-15 objectives, extensive experiments validate the superiority of our algorithm over six competitive many-objective EAs, especially on problems with incomplete and irregular Pareto-optimal fronts.