Adaptive indicator-based evolutionary algorithm for multiobjective optimization problems
Abstract
Indicator-based evolutionary algorithm (IBEA1) is a fast and effective approach for solving multiobjective optimization problems (MOPs). In the classical IBEA1, the parameter κ is predefined to amplify or shrink the indicator differences on pairwise solutions. However, the value of κ in IBEA1 needs to be carefully calibrated based on the selected indicator (e.g., hypervolume or additive e-indicator) and the encountered MOPs. In this paper, a new version of IBEA1 (labeled as IBEA2 hereafter) is proposed to adaptively adjust parameter κ for solving various MOPs. The core idea of IBEA2 is to adapt parameter κ for the purpose of selecting the subset of offspring solutions with the maximum hypervolume into the next population. Experimental studies on 44 benchmark MOPs with 2-5 objectives in jMetal verified that IBEA2 is able to find higher hypervolumes against the four classical MOEAs, which are NSGAII, SPEA2, MOEA/D and IBEA1, in the literature.
Authors: Siwei Jiang, Liang Feng, Chen Kim Heng, Quoc Chinh Nguyen, Yew-Soon Ong, Allan Neng Sheng Zhang, Puay Siew Tan
Published in: IEEE Congress on Evolutionary Computation (CEC) (2016)