dc.contributor.authorQu, B. Y.
dc.contributor.authorLiang, J. J.
dc.contributor.authorSuganthan, P. N.
dc.date.accessioned2013-09-20T02:02:11Z
dc.date.available2013-09-20T02:02:11Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationQu, B. Y., Liang, J. J., & Suganthan, P. N. (2012). Niching particle swarm optimization with local search for multi-modal optimization. Information sciences, 197, 131-143.
dc.identifier.urihttp://hdl.handle.net/10220/13559
dc.description.abstractMultimodal optimization is still one of the most challenging tasks for evolutionary computation. In recent years, many evolutionary multi-modal optimization algorithms have been developed. All these algorithms must tackle two issues in order to successfully solve a multi-modal problem: how to identify multiple global/local optima and how to maintain the identified optima till the end of the search. For most of the multi-modal optimization algorithms, the fine-local search capabilities are not effective. If the required accuracy is high, these algorithms fail to find the desired optima even after converging near them. To overcome this problem, this paper integrates a novel local search technique with some existing PSO based multimodal optimization algorithms to enhance their local search ability. The algorithms are tested on 14 commonly used multi-modal optimization problems and the experimental results suggest that the proposed technique not only increases the probability of finding both global and local optima but also reduces the average number of function evaluations.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesInformation sciencesen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering
dc.titleNiching particle swarm optimization with local search for multi-modal optimizationen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.ins.2012.02.011


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