An Objective Reduction Algorithm based on Non-dominated Solution Pairs

Abstract

Evolutionary Multi-objective optimization (EMO) Algorithms have been successfully applied in many practical problems. However, their performance deteriorated seriously for the problems with many objectives, i.e. many-objective optimization problems (MaOPs). In some practical applications, there exist some redundant objectives in an MaOP. Reducing the number of objectives of the optimization problem is one of the effective ways to solve the MaOPs with redundant objectives. It can improve the search efficiency of EMO algorithms, and reduce the computational cost. In this paper, we propose a new objective reduction algorithm. A criterion based on the number of non-dominated solution paired is presented to measure the conflict degree between objectives. Furthermore, we develop a effective objective reduction algorithm using feature selection technique. We compared the proposed algorithm with the LPCA, NLMVUP-CA and $\delta$-MOSS algorithm in some benchmark problems, and the results show the effectiveness of the proposed algorithm.