Comparative Study of Recent Multimodal Evolutionary Algorithms

R. Pighetti Denis Pallez 1 Frédéric Precioso 2
1 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis
2 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe KEIA
Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : Multimodal Optimization (MMO) aims at identifying several best solutions to a problem whereas classical optimization converge oftenly to only one good solution. MMO has been an active research area in the past years and several new evolutionary algorithms have been developed to tackle multimodal problems. In this work, we compare extensively three recent evolutionary algorithms (MoBiDE, Multimodal NSGAII and MOMMOP). Each algorithm uses multiobjectivization, together with niching techniques to address scalar (single objective) MMO problems. We have fully re-implemented MoBiDE and MM-NSGAII in order to better evaluate their sensitivity to parameter changes and their strengths and weaknesses. We have carefully evaluated all algorithms on the same benchmark functions and with the same parameters settings. The influence of the intrinsic parameters for each algorithm are stressed and the algorithms are also compared to a non-multimodal evolutionary algorithm to better highlight the impact of the multimodal adaptations. Moreover, full access to the detailed results and source code is granted on our website for the ease of reproducibility.
Keywords : Statistics Sociology Sorting Optimization Context single objective MMO problems nonmultimodal evolutionary algorithm niching techniques Evolutionary computation Current measurement genetic algorithms MM-NSGAII MOMMOP MoBiDE multimodal NSGAII multimodal evolutionary algorithms multimodal optimization using a bi-objective differential evolution multiobjective optimization for multimodal optimization problems multiobjectivization
Type de document :
Communication dans un congrès
Computational Intelligence, 2015 IEEE Symposium Series on, 2015, Cape Town, South Africa. pp.837-844, 2015, Multicriteria Decision-Making. 〈10.1109/SSCI.2015.124〉
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https://hal.univ-cotedazur.fr/hal-01322764
Contributeur : Denis Pallez <>
Soumis le : vendredi 27 mai 2016 - 16:17:15
Dernière modification le : lundi 5 novembre 2018 - 15:52:10

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R. Pighetti, Denis Pallez, Frédéric Precioso. Comparative Study of Recent Multimodal Evolutionary Algorithms. Computational Intelligence, 2015 IEEE Symposium Series on, 2015, Cape Town, South Africa. pp.837-844, 2015, Multicriteria Decision-Making. 〈10.1109/SSCI.2015.124〉. 〈hal-01322764〉

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Université Nice Sophia Antipolis (UNS) Observatoire de la Côte d’Azur (OCA) Centre national de la recherche scientifique (CNRS) Institut national de la recherche en informatique et en automatique (Inria) SKEMA Business School EDHEC Business School  Centre Hospitalier Universitaire de Nice (CHU Nice) Centre National de Création Musicale (CIRM)  Ecole Nationale Supérieure d’Art Villa Arson Ecole Supérieure de Réalisation Audiovisuelle (ESRA) The Sustainable Design School (SDS) Ecole Supérieure de Danse de Cannes Rosella Hightower Conservatoire National à Rayonnement Régional de Nice (CNRR)