BIBEA: Boosted Indicator Based Evolutionary Algorithm for Multiobjective Optimization

Various evolutionary multiobjective optimization algorithms (EMOAs) have replaced or augmented the notion of dominance with quality indicators and leveraged them in selection operators. Recent studies show that indicator-based EMOAs outperform traditional dominance-based EMOAs. Many quality indicators have been proposed with the intention to capture different preferences in optimization. Therefore, indicatorbased selection operators tend to have biased selection pressures that evolve solution candidates toward particular regions in the objective space. This paper investigates a boosting method that prioritizes and aggregates existing quality indicators to create a single indicator that outperforms those existing ones. The proposed boosting method is carried out with a training problem in which Pareto-optimal solutions are known. It can work with a simple training problem, and a boosted indicator can effectively operate in parent selection and environmental selection in order to solve harder problems. Experimental results show that a boosted selection operator outperforms exiting ones in optimality and diversity. It also exhibits robustness against different characteristics in different optimization problems and yields stable performance to solve them. Experimental results also demonstrate that the proposed boosted indicator based evolutionary algorithm (BIBEA) outperforms a well-known traditional EMOA and existing indicator-based evolutionary algorithms.