Comparative performance evaluation of island particle swarm algorithm applied to solve constrained and unconstrained optimization problems

Abstract

Distributed evolutionary computation has been efficiently used, in last decades, to solve complex optimization problems. Island model (IM) is considered as a distributed population paradigm employed by evolutionary algorithms to preserve the diversification and, thus, to improve the local search. In this article, we study different island model techniques integrated in to particle swarm optimization (PSO) algorithm in order to overcome its drawbacks: premature convergence and lack of diversity. The first IMPSO approach consists in using the migration process in a static way to enhance the police migration strategy. On the other hand, the second approach, called dynamic-IMPSO, consists in integrating a learning strategy in case of migration. The last version called constrained-IMPSO utilizes a stochastic technique to ensure good communication between the sub-swarms. To evaluate and verify the effectiveness of the proposed algorithms, several standard constrained and unconstrained benchmark functions are used. The obtained results confirm that these algorithms are more efficient in solving low-dimensional problems (CEC’05), large-scale optimization problems (CEC’13) and constrained problems (CEC’06), compared to other well-known evolutionary algorithms.