Evolutionary multimodal optimization: Difference between revisions

Revision as of 11:45, 8 December 2015

Revision as of 11:50, 8 December 2015

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The field of [[Evolutionary algorithm]]s encompasses [[genetic algorithm]]s (GAs), [[evolution strategy]] (ES), [[differential evolution]] (DE), [[particle swarm optimization]] (PSO), and other methods. Attempts have been made to solve multi-modal optimization in all these realms and most, if not all the various methods implement niching in some form or the other.

== Multimodal optimization using GAsGenetic algorithms/Evolution strategies ==

Petrwoski’sDe clearingJong's crowding method, Goldberg’s sharing function approach, Petrowski’s clearing method, restricted mating, maintaining multiple subpopulations are some of the popular approaches that have been proposed by the GA Communitycommunity. The first two methods are veryespecially well studied, andhowever, respectedthey indo thenot GAperform communityexplicit separation into solutions belonging to different basins of attraction.

Recently, an evolutionary [[multiobjective optimization]] (EMO) approach was proposed,<ref>Deb, K., Saha, A. (2010) "Finding Multiple Solutions for Multimodal Optimization Problems Using a Multi-Objective Evolutionary Approach" (GECCO 2010, In press)</ref> in which a suitable second objective is added to the originally single objective multimodal optimization problem, so that the multiple solutions form a '' weak pareto-optimal'' front. Hence, the multimodal optimization problem can be solved for its multiple solutions using an EMO algorithm. Improving upon their work,<ref>Saha, A., Deb, K. (2010) "A Bi-criterion Approach to Multimodal Optimization: Self-adaptive Approach " (Lecture Notes in Computer Science, 2010, Volume 6457/2010, 95–104)</ref> the same authors have made their algorithm self-adaptive, thus eliminating the need for pre-specifying the parameters.