Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

A newly hybrid nature inspired algorithm called HPSOGWO is presented with the combination of Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). The main idea is to improve the ability of exploitation in Particle Swarm Optimization with the ability of exploration in Grey Wolf Optimizer...

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Bibliographic Details
Published inJournal of Applied Mathematics Vol. 2017; no. 2017; pp. 1 - 15-005
Main Authors Singh, Narinder, Singh, S. B.
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Limiteds 2017
Hindawi Publishing Corporation
Hindawi
John Wiley & Sons, Inc
Wiley
Subjects
Algorithms
Civil engineering
Convergence
Convergence (Mathematics)
Exploitation
Genetic algorithms
International conferences
Mathematical optimization
Mathematical research
Optimization techniques
Particle swarm optimization
Quality
Wolves
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Summary:A newly hybrid nature inspired algorithm called HPSOGWO is presented with the combination of Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). The main idea is to improve the ability of exploitation in Particle Swarm Optimization with the ability of exploration in Grey Wolf Optimizer to produce both variants’ strength. Some unimodal, multimodal, and fixed-dimension multimodal test functions are used to check the solution quality and performance of HPSOGWO variant. The numerical and statistical solutions show that the hybrid variant outperforms significantly the PSO and GWO variants in terms of solution quality, solution stability, convergence speed, and ability to find the global optimum.
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ISSN:1110-757X
1687-0042
DOI:10.1155/2017/2030489