Title: A hybrid optimisation algorithm based on butterfly optimisation algorithm and differential evolution

Authors: Sankalap Arora; Satvir Singh

Addresses: Department of Computer Science and Engineering, I.K. Gujral Punjab Technical University, Kapurthala, Punjab, India ' Department of Electronics and Communication Engineering, Shaheed Bhagat Singh State Technical Campus, Ferozepur, Punjab, India

Abstract: Butterfly optimisation algorithm (BOA) is a newcomer in the family of nature inspired optimisation algorithms. Although it is an effective algorithm, still, like other population-based optimisation algorithms, it encounters two probable problems: 1) entrapment in local optima; 2) slow convergence speed. In order to increase the potential of the algorithm, it is hybridised with an efficient algorithm, differential evolution (DE), which accelerates the global convergence speed to the true global optimum while preserving the main feature of the basic BOA. In this paper, a novel hybrid algorithm based on BOA and DE, namely BOA/DE is proposed to solve numerical optimisation problems. The proposed algorithm has advantages of both BOA and DE which enable the algorithm to balance the tradeoff between exploration and exploitation which produces efficient results. Engineering design problem and standard benchmark functions are employed to validate the proposed algorithm and according to the simulation results, the performance of the hybrid algorithm is superior to or at least highly competitive with the standard BOA and DE.

Keywords: butterfly optimisation algorithm; BOA; differential algorithm; numerical optimisation; benchmark functions; engineering design problems.

DOI: 10.1504/IJSI.2017.087872

International Journal of Swarm Intelligence, 2017 Vol.3 No.2/3, pp.152 - 169

Received: 20 Jun 2016
Accepted: 17 Nov 2016

Published online: 06 Nov 2017 *

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