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Greedy particle swarm and biogeography-based optimization algorithm

Jehad Ababneh (Department of Electronics and Communications Engineering, A’Sharqiyah University, Ibra, Oman and Jordan University of Science and Technology, Irbid, Jordan)

International Journal of Intelligent Computing and Cybernetics

ISSN: 1756-378X

Article publication date: 9 March 2015

Abstract

Purpose

The purpose of this paper is to propose an algorithm that combines the particle swarm optimization (PSO) with the biogeography-based optimization (BBO) algorithm.

Design/methodology/approach

The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both algorithms. The efficiency of the proposed algorithm is tested using some selected standard benchmark functions. The performance of the proposed algorithm is compared with that of the differential evolutionary (DE), genetic algorithm (GA), PSO, BBO, blended BBO and hybrid BBO-DE algorithms.

Findings

Experimental results indicate that the proposed algorithm outperforms the BBO, PSO, DE, GA, and the blended BBO algorithms and has comparable performance to that of the hybrid BBO-DE algorithm. However, the proposed algorithm is simpler than the BBO-DE algorithm since the PSO does not have complex operations such as mutation and crossover used in the DE algorithm.

Originality/value

The proposed algorithm is a generic algorithm that can be used to efficiently solve optimization problems similar to that solved using other popular evolutionary algorithms but with better performance.

Keywords

Acknowledgements

This work was partially supported by the deanship of research at Jordan University of Science and Technology.

Citation

Ababneh, J. (2015), "Greedy particle swarm and biogeography-based optimization algorithm", International Journal of Intelligent Computing and Cybernetics, Vol. 8 No. 1, pp. 28-49. https://doi.org/10.1108/IJICC-01-2014-0003

Publisher

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Emerald Group Publishing Limited

Copyright © 2015, Emerald Group Publishing Limited