Particle based on biogeography-based optimisation for global optimisation problems. (1st January 2013)

• Record Type: Journal Article
• Authors: Feng, Quanxi; Liu, Sanyang; Tang, Guoqiang; Chen, Huazhou
• Is Part Of: International journal of innovative computing and applications. Volume 5:Number 4(2013)
• Page Start: 228
• Page End: 239
• Publication Date: 2013-01-01
• Subjects: biogeography-based optimisation -- BBO -- hybrid migration operator -- evolutionary algorithm -- particle swarm optimisation -- computing; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Biogeography-based optimisation is an emerging evolutionary optimisation algorithm based on biogeography theory. This paper proposes a hybrid algorithm through hybridisation of biogeography-based optimisation and particle swarm optimisation. The hybridisation embeds the position updating equation of PSO into migration operator of BBO to accelerate convergence speed. Cauchy mutation is also integrated to enhance population diversity and implement selection operator to preserve fitter habitats for the subsequent generation. Experimental tests are conducted on 23 benchmark functions. Simulation results and comparisons show that the new algorithm is an efficient algorithm. Compared with other state-of-the-art BBO algorithms and evolutionary algorithms, the proposed algorithm performs better or at least comparably in terms of solution's quality and convergent speed. Finally, the influence of the proportional ratio in migration operator on the performance is also investigated.