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Journal of Applied Mathematics
Volume 2017 (2017), Article ID 2030489, 15 pages
Research Article

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

Department of Mathematics, Punjabi University, Patiala, Punjab 147002, India

Correspondence should be addressed to Narinder Singh; moc.liamy@airoghgnisredniran

Received 9 June 2017; Revised 29 August 2017; Accepted 30 August 2017; Published 16 November 2017

Academic Editor: N. Shahzad

Copyright © 2017 Narinder Singh and S. B. Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.