Fully Informed Swarm Optimization Algorithms: Basic Concepts, Variants and Experimental Evaluation
Szymon Łukasik, Piotr Andrzej Kowalski
DOI: http://dx.doi.org/10.15439/2014F377
Citation: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 2, pages 155–161 (2014)
Abstract. Particle swarm optimization constitutes currently one of the most important nature-inspired metaheuristics, used successfully for both combinatorial and continuous problems. Its popularity has stimulated the emergence of various variants of swarm-inspired techniques, based in part on the concept of pairwise communication of numerous swarm members solving optimization problem in hand. This paper overviews some examples of such techniques, namely Fully Informed Particle Swarm Optimization (FIPSO), Firefly Algorithm (FA) and Glowworm Swarm Optimization (GSO). It underlines similarities and differences among them and studies their practical features. Performance of those algorithms is also evaluated over a set of benchmark instances. Finally, some concluding remarks regarding the choice of suitable problem-oriented optimization technique along with areas of possible improvements are given as well.