Examining the Performance of Evolutionary Many-Objective Optimization Algorithms on a Real-World Application

Abstract

Recently research into many-objective optimization has attracted much attention. One of the main topics of the research is to develop evolutionary many-objective optimization (EMAO) algorithms that can solve optimization problems with more than three objectives. EMAO algorithms generally differ from evolutionary multi-objective optimization (EMO) algorithms as EMO algorithms are known to work mainly for optimization problems with two or three objectives. Thus far the performance of EMO algorithms has been validated using both benchmark test problems and real-world applications. Although the performance of EMAO algorithms has also been shown using benchmark test problems, their performance on real-world applications rarely appears in the literature. in this paper we examine the performance of state-of-the-art EMAO algorithms by applying them to a real-world application, namely a hybrid car controller optimization problem with six objectives. It is demonstrated that EMAO algorithms work well for this optimization problem.