Iterated multi-swarm: a multi-swarm algorithm based on archiving methods

Authors:
Andre Britto;Sanaz Mostaghim;Aurora Pozo
Affiliations:
Ministry of Education of Brazil, Brasilia, Brazil;Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany;Federal University of Parana, Curitiba, Brazil
Venue:
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Year:
2013

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Abstract

Usually, Multi-Objective Evolutionary Algorithms face serious challengers in handling many objectives problems. This work presents a new Particle Swarm Optimization algorithm, called Iterated Multi-Swarm (I-Multi Swarm), which explores specific characteristics of PSO to face Many-Objective Problems. The algorithm takes advantage of a Multi-Swarm approach to combine different archiving methods aiming to improve convergence to the Pareto-optimal front and diversity of the non-dominated solutions. I-Multi Swarm is evaluated through an empirical analysis that uses a set of many-Objective problems, quality indicators and statistical tests.