C-Multi: A competent multi-swarm approach for many-objective problems

One of the major research topics in the evolutionary multi-objective community is handling a large number of objectives also known as many-objective optimization problems (MaOPs). Most existing methodologies have demonstrated success for problems with two and three objectives but face significant challenges in many-objective optimization. To tackle these challenges, a hybrid multi-swarm algorithm called C-Multi was proposed in a previous work. The project of C-Multi is based on two phases; the first uses a unique particle swarm optimization (PSO) algorithm to discover different regions of the Pareto front. The second phase uses multiple swarms to specialize on a dedicate part. On each sub-swarm, an estimation of distribution algorithm (EDA) is used to focus on convergence to its allocated region. In this study, the influence of two critical components of C-Multi, the archiving method and the number of swarms, is investigated by empirical analysis. As a result of this investigation, an improved variant of C-Multi is obtained, and its performance is compared to I-Multi, a multi-swarm algorithm that has a similar approach but does not use EDAs. Empirical results fully demonstrate the superiority of our proposed method on almost all considered test instances.