Please use this identifier to cite or link to this item: https://doi.org/10.1109/TEVC.2012.2231685
Title: Multimodal optimization using a biobjective differential evolution algorithm enhanced with mean distance-based selection
Authors: Basak, A.
Das, S.
Tan, K.C. 
Keywords: Crowding
Differential evolution (DE)
Multimodal optimization
Multiobjective optimization
Niching
Nondominated sorting
Issue Date: 2013
Citation: Basak, A., Das, S., Tan, K.C. (2013). Multimodal optimization using a biobjective differential evolution algorithm enhanced with mean distance-based selection. IEEE Transactions on Evolutionary Computation 17 (5) : 666-685. ScholarBank@NUS Repository. https://doi.org/10.1109/TEVC.2012.2231685
Abstract: In contrast to the numerous research works that integrate a niching scheme with an existing single-objective evolutionary algorithm to perform multimodal optimization, a few approaches have recently been taken to recast multimodal optimization as a multiobjective optimization problem to be solved by modified multiobjective evolutionary algorithms. Following this promising avenue of research, we propose a novel biobjective formulation of the multimodal optimization problem and use differential evolution (DE) with nondominated sorting followed by hypervolume measure-based sorting to finally detect a set of solutions corresponding to multiple global and local optima of the function under test. Unlike the two earlier multiobjective approaches (biobjective multipopulation genetic algorithm and niching-based nondominated sorting genetic algorithm II), the proposed multimodal optimization with biobjective DE (MOBiDE) algorithm does not require the actual or estimated gradient of the multimodal function to form its second objective. Performance of MOBiDE is compared with eight state-of-the-art single-objective niching algorithms and two recently developed biobjective niching algorithms using a test suite of 14 basic and 15 composite multimodal problems. Experimental results supported by nonparametric statistical tests suggest that MOBiDE is able to provide better and more consistent performance over the existing well-known multimodal algorithms for majority of the test problems without incurring any serious computational burden. © 1997-2012 IEEE.
Source Title: IEEE Transactions on Evolutionary Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/56715
ISSN: 1089778X
DOI: 10.1109/TEVC.2012.2231685
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

54
checked on Sep 20, 2018

WEB OF SCIENCETM
Citations

48
checked on Sep 10, 2018

Page view(s)

21
checked on Jun 30, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.