Parallel SMS-EMOA for Many-Objective Optimization Problems

In the last decade, there has been a growing interest in multi-objective evolutionary algorithms that use performance indicators to guide the search. A simple and effective one is the S-Metric Selection Evolutionary Multi-Objective Algorithm (SMS-EMOA), which is based on the hypervolume indicator. Even though the maximization of the hypervolume is equivalent to achieving Pareto optimality, its computational cost increases exponentially with the number of objectives, which severely limits its applicability to many-objective optimization problems. In this paper, we present a parallel version of SMS-EMOA, where the execution time is reduced through the synchronous island model with micro-populations, and diversity is preserved by external archives that are pruned to a fixed size employing a recently created technique based on a Parallel-Coordinates graph. The proposed approach, called S-PAMICRO (PArallel MICRo Optimizer based on the S, is compared with an state-of-the-art algorithm (HypE) on the WFG test problems. Preliminary experimental results show that S-PAMICRO is a promising alternative that can solve many-objective optimization problems at an affordable computational cost.