Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84801
Title: Niching particle swarm optimization with local search for multi-modal optimization
Authors: Qu, B. Y.
Liang, J. J.
Suganthan, P. N.
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Qu, 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.
Series/Report no.: Information sciences
Abstract: Multimodal 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.
URI: https://hdl.handle.net/10356/84801
http://hdl.handle.net/10220/13559
DOI: 10.1016/j.ins.2012.02.011
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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