Title: A new group search optimiser integrating multiply strategies

Authors: Chengwang Xie; Wenjing Chen; Weiwei Yu

Addresses: School of Software, East China Jiaotong University, Nanchang, 330013, China ' School of Software, East China Jiaotong University, Nanchang, 330013, China ' School of Software, East China Jiaotong University, Nanchang, 330013, China

Abstract: Group search optimiser (GSO) is a recently developed heuristic inspired by biological group search resources behaviour. However, it still has some defects such as slow convergence speed and poor accuracy of solution. In order to improve the performance of GSO in solving complex optimisation problems, an opposition-based learning (OBL) and a differential evolution (DE) are integrated into GSO to form a hybrid GSO. In this paper, the strategy of OBL is used to enlarge the search region to facilitate jumping out of the local optimal trap, and the approach of DE is utilised to enhance local search and then improve the accuracy of solution. Comparison experiments based on 13 benchmark test functions have demonstrated that our hybrid GSO performed advantages over the other peer optimisers.

Keywords: group search optimiser; GSO; opposition-based learning; OBL; differential evolution; hybrid group search optimiser.

DOI: 10.1504/IJCSE.2017.085973

International Journal of Computational Science and Engineering, 2017 Vol.15 No.1/2, pp.12 - 20

Received: 31 Oct 2015
Accepted: 12 Jan 2016
Published online: 21 Aug 2017 *

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