An objective reduction algorithm based on hyperplane approximation for many-objective optimization problems
Yifan Li, Hai‐Lin Liu, Fangqing Gu · 2016
In this paper, we propose a simple but effective objective reduction algorithm (ORA) for many-objective optimization problems (MaOPs). It uses a hyperplane involving sparse non-negative coefficients to roughly approximate the conflicting structure of the Pareto front in the objective space. Then the objectives with non-zero coefficients are considered as essential objectives. In order to verify the performance of proposed algorithm, we compare the proposed algorithm with two correlation-based ORA, i.e., L-PCA and NL-MVU-PCA and a dominance structure-based ORA, i.e., PCSEA on the benchmark problem DTLZ5(I, M). The experimental results show the effectiveness of the proposed algorithm.