Boosting Indicator-Based Selection Operators for Evolutionary Multiobjective Optimization Algorithms

Abstract

Various evolutionary multiobjective optimization algorithms (EMOAs) have adopted indicator-based selection operators that augment or replace dominance ranking with quality indicators. A quality indicator measures the goodness of each solution candidate. Many quality indicators have been proposed with the intention to capture different preferences in optimization. Therefore, indicator-based selection operators tend to have biased selection pressures that evolve solution candidates toward particular regions in the objective space. An open question is whether a set of existing indicator based selection operators can create a single operator that outperforms those existing ones. To address this question, this paper studies a method to aggregate (or boost) existing indicator-based selection operators. Experimental results show that a boosted selection operator outperforms exiting ones in optimality, diversity and convergence velocity. It also exhibits robustness against different characteristics in different optimization problems and yields stable performance to solve them.