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HAL Université Côte d'Azur |  |
Conference papers
Comparative Study of Recent Multimodal Evolutionary Algorithms
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
https://hal.univ-cotedazur.fr/hal-01322764
Contributor : Denis Pallez <>
Submitted on : Friday, May 27, 2016 - 4:17:15 PM
Last modification on : Monday, October 12, 2020 - 10:30:35 AM
Contributor : Denis Pallez <>
Submitted on : Friday, May 27, 2016 - 4:17:15 PM
Last modification on : Monday, October 12, 2020 - 10:30:35 AM