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2017 Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance
Narinder Singh, S. B. Singh
J. Appl. Math. 2017: 1-15 (2017). DOI: 10.1155/2017/2030489

Abstract

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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Narinder Singh. S. B. Singh. "Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance." J. Appl. Math. 2017 1 - 15, 2017. https://doi.org/10.1155/2017/2030489

Information

Received: 9 June 2017; Revised: 29 August 2017; Accepted: 30 August 2017; Published: 2017
First available in Project Euclid: 14 December 2017

zbMATH: 07037463
MathSciNet: MR3729687
Digital Object Identifier: 10.1155/2017/2030489

Rights: Copyright © 2017 Hindawi

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