Clustering method using Pareto Corner Search Evolutionary algorithm for objective reduction in many-objective optimization problems

Many-objective optimization problems (MaOPs) have been gained considerable attention for researcher, recently. MaOPs make a number of difficulties for multi-objective optimization evolutionary algorithms (MOEAs) when solving them. Although, there exist a number of many-objective optimization evolutionary algorithms (MaOEAs) for solving MaOPs, they still face difficulties when the number of objectives of MaOPs increases. One common method to reduce or alleviate these difficulties is to use objective dimensionality reduction (or objective reduction for briefly). Moreover, instead of searching the whole of objective space like existing MOEAs or MaOEAs, Pareto Corner Search Evolutionary (PCSEA) concentrates only on some places of objective space, so it decreases time consuming and then speeds up objective reduction. However, PCSEA-based objective reduction needs to specify a threshold to select or remove objectives, which is not straightforward to do. Based on the idea that more conflict two objectives are, more distant two objectives are; in this paper, we introduce a new objective reduction by integrating PCSEA and k-means, DBSCAN clustering algorithms for solving MaOPs which are assumed containing redundant objectives. The experimental results show that the introduced method can reducing redundant objectives better than PCSEA-based objective reduction. The results further strengthen the links between evolutionary computation and machine learning to address optimization problems. © 2019 Association for Computing Machinery.