A new metaheuristic optimisation algorithm motivated by elephant herding behaviour. (2016)

• Record Type: Journal Article
• Authors: Wang, Gai-Ge; Deb, Suash; Gao, Xiao-Zhi; Coelho, Leandro Dos Santos
• Is Part Of: International journal of bio-inspired computation. Volume 8:Number 6(2016)
• Page Start: 394
• Page End: 409
• Publication Date: 2016
• Subjects: 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; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

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.