A new hybrid PSOGSA algorithm for function optimization

Seyedali Mirjalili,Siti Zaiton Mohd Hashim

doi:10.1109/iccia.2010.6141614

Seyedali Mirjalili, Siti Zaiton Mohd Hashim

Open Access

https://doi.org/10.1109/iccia.2010.6141614

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Publication Date: Dec 1, 2010
Citations: 433	License type: mit

Affiliation: University of Technology Malaysia

Abstract

In this paper, a new hybrid population-based algorithm (PSOGSA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The main idea is to integrate the ability of exploitation in PSO with the ability of exploration in GSA to synthesize both algorithms' strength. Some benchmark test functions are used to compare the hybrid algorithm with both the standard PSO and GSA algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard PSO and GSA.

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