Test Suites and Performance of Algorithms in Large-Scale Multiobjective Evolutionary Optimization

Haoran Gu, Handing Wang, Jingjing Ma

Abstract: In recent years, the research on large-scale mul-tiobjective optimization has attracted much attention. Many competitive large-scale multiobjective evolutionary algorithms have been proposed. Usually, their performance is evaluated on the widely used large-scale multiobjective test suite (i.e. LSMOP test suite). Those algorithms often exhibit a strong convergence capability on the instances of LSMOP test suite. The purpose of this study is to show our concern that the development of algorithms may be over specialized for the LSMOP test suite. We first explain some issues in the original LSMOP test suite. Then, we propose a general LSMOP test suite (termed GLSMOP), in which the Pareto set has a more general structure in the decision space. Experimental results on two test suites suggest that the performance of some large-scale multiobjective evolutionary algorithms will be deteriorated rapidly by the change of Pareto set. It also reveals the good performance of MOEAID-DE on large-scale multiobjective optimization problems.