KGSA: A Gravitational Search Algorithm for Multimodal Optimization based on K-Means Niching Technique and a Novel Elitism Strategy

Gravitational Search Algorithm (GSA) is a metaheuristic for solving unimodal problems. In this paper, a K-means based GSA (KGSA) for multimodal optimization is proposed. This algorithm incorporates K-means and a new elitism strategy called “loop in loop” into the GSA. First in KGSA, the members of t...

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Bibliographic Details
Published in:Open mathematics (Warsaw, Poland) Poland), 2018-12, Vol.16 (1), p.1582-1606
Main Authors: Golzari, Shahram, Zardehsavar, Mohammad Nourmohammadi, Mousavi, Amin, Saybani, Mahmoud Reza, Khalili, Abdullah, Shamshirband, Shahaboddin
Format: Article
Language:English
Subjects:
Algorithms
Clusters
Elitism
Gravitational Search Algorithm (GSA)
Heuristic methods
K-means
multimodal optimization
niching methods
Optimization
Search algorithms
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Summary:Gravitational Search Algorithm (GSA) is a metaheuristic for solving unimodal problems. In this paper, a K-means based GSA (KGSA) for multimodal optimization is proposed. This algorithm incorporates K-means and a new elitism strategy called “loop in loop” into the GSA. First in KGSA, the members of the initial population are clustered by K-means. Afterwards, new population is created and divided in different niches (or clusters) to expand the search space. The “loop in loop” technique guides the members of each niche to the optimum direction according to their clusters. This means that lighter members move faster towards the optimum direction of each cluster than the heavier members. For evaluations, KGSA is benchmarked on well-known functions and is compared with some of the state-of-the-art algorithms. Experiments show that KGSA provides better results than the other algorithms in finding local and global optima of constrained and unconstrained multimodal functions.
ISSN:2391-5455
2391-5455
DOI:10.1515/math-2018-0132