Objective Space Partitioning Using Conflict Information for Many-Objective Optimization

Here, we present a partition strategy to generate objective subspaces based on the analysis of the conflict information obtained from the Pareto front approximation found by an underlying multi-objective evolutionary algorithm. By grouping objectives in terms of the conflict among them, we aim to separate the multi-objective optimization into several subproblems in such a way that each of them contains the information to preserve as much as possible the structure of the original problem. The ranking and parent selection is independently performed in each subspace. Our experimental results show that the proposed conflict-based partition strategy outperforms NSGA-II in all the test problems considered in this study. In problems in which the degree of conflict among the objectives is significantly different, the conflict-based strategy achieves its best performance.