Title: Improved biogeography-based optimisation

Authors: Raju Pal; Mukesh Saraswat

Addresses: Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India ' Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

Abstract: Biogeography-based optimisation (BBO) is one of the popular evolutionary algorithms, inspired by the theory of island biogeography. It has been successfully applied in various real world optimisation problems such as image segmentation, data clustering, combinatorial problems and many more. BBO finds the optimal solution by using two of its main operators namely: migration and mutation. However, sometimes it traps into local optimum and converges slowly due to poor population diversity generated by mutation operator. Moreover, single feature migration property of BBO gives poor performance for non-separable functions. Therefore, this paper introduces a new variant of BBO known as improved BBO (IBBO) by enhancing its migration and mutation operators. The proposed variant successfully improves the population diversity and convergence behaviour of BBO as well as shows better solutions for non-separable functions. The performance of proposed variant has also been compared and analysed with other existing algorithms over 20 benchmark functions.

Keywords: evolutionary algorithm; biogeography-based optimisation; BBO; migration operator; mutation operator.

DOI: 10.1504/IJAIP.2022.121027

International Journal of Advanced Intelligence Paradigms, 2022 Vol.21 No.1/2, pp.18 - 40

Received: 18 Nov 2016
Accepted: 12 Jun 2017
Published online: 23 Feb 2022 *

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