A new metaheuristic optimisation algorithm motivated by elephant herding behaviour

Gai Ge Wang*, Suash Deb, Xiaozhi Gao, Leandro Dos Santos Coelho

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

204 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)394-409
Number of pages16
JournalInternational Journal of Bio-Inspired Computation
Issue number6
Publication statusPublished - 2016
MoE publication typeA1 Journal article-refereed


  • Benchmark functions
  • Bio-inspired metaheuristic
  • EHO
  • Elephant herding optimisation
  • Elitism strategy
  • Evolutionary algorithms
  • Evolutionary computation
  • Global optimisation
  • Real world problems
  • Soft computing
  • Swarm intelligence


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