Authors: Gai-Ge Wang; Suash Deb; Xiao-Zhi Gao; Leandro Dos Santos Coelho
Addresses: School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, Jiangsu, 221116, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6R 2V4, Canada; School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China ' Department of Computer Science and Engineering, Cambridge Institute of Technology, Ranchi 835103, Jharkhand, India ' Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, 00076 Aalto, Finland ' Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Parana (PUCPR), Curitiba, Parana, Brazil; Electrical Engineering Graduate Program (PPGEE), Department of Electrical Engineering, Polytechnic Center, Federal University of Parana (UFPR), Curitiba, Parana, Brazil
Abstract: In this paper, a new swarm-based metaheuristic algorithm, called elephant herding optimisation (EHO), is proposed for solving global optimisation tasks, which is inspired by the herding behaviour of the elephant groups. In nature, the elephants belonging to different clans live together under the leadership of a matriarch, and the male elephants will leave their family group when growing up. These two behaviours can be modelled into two following operators: clan updating operator and separating operator. In EHO, the elephants are updated using its current position and matriarch through clan updating operator, and the separating operator is then implemented. Moreover, EHO has been benchmarked by 20 standard benchmarks, and two engineering cases in comparison with BBO, DE and GA. The results clearly establish the supremacy of EHO in finding the better function values on most test problems than those three algorithms. The code can be found in the website: http://www.mathworks.com/matlabcentral/fileexchange/53486.
Keywords: elephant herding optimisation; EHO; swarm intelligence; evolutionary algorithms; evolutionary computation; bio-inspired metaheuristics; soft computing; elitism strategy; global optimisation; benchmark functions; real world problems; elephant herding behaviour; modelling.
International Journal of Bio-Inspired Computation, 2016 Vol.8 No.6, pp.394 - 409
Available online: 28 Dec 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article