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

Abstract

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.