A new metaheuristic optimisation algorithm motivated by elephant herding behaviour Online publication date: Wed, 28-Dec-2016
by Gai-Ge Wang; Suash Deb; Xiao-Zhi Gao; Leandro Dos Santos Coelho
International Journal of Bio-Inspired Computation (IJBIC), Vol. 8, No. 6, 2016
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.
Online publication date: Wed, 28-Dec-2016
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