MAKHA-A new hybrid swarm intelligence global optimization algorithm

Ahmed M.E. Khalil, Seif Eddeen K. Fateen, Adrián Bonilla-Petriciolet

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


The search for efficient and reliable bio-inspired optimization methods continues to be an active topic of research due to the wide application of the developed methods. In this study, we developed a reliable and efficient optimization method via the hybridization of two bio-inspired swarm intelligence optimization algorithms, namely, the Monkey Algorithm (MA) and the Krill Herd Algorithm (KHA). The hybridization made use of the efficient steps in each of the two original algorithms and provided a better balance between the exploration/diversification steps and the exploitation/intensification steps. The new hybrid algorithm, MAKHA, was rigorously tested with 27 benchmark problems and its results were compared with the results of the two original algorithms. MAKHA proved to be considerably more reliable and more efficient in tested problems.

Original languageEnglish
Pages (from-to)336-365
Number of pages30
Issue number2
Publication statusPublished - 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Numerical Analysis
  • Computational Theory and Mathematics
  • Computational Mathematics


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