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Open AccessArticle

MAKHA—A New Hybrid Swarm Intelligence Global Optimization Algorithm

1
Department of Chemical Engineering, Faculty of Engineering, Cairo University, Giza 12613, Egypt
2
Department of Petroleum and Energy Engineering, American University in Cairo, New Cairo 11835, Egypt
3
Department of Chemical Engineering, Aguascalientes Institute of Technology, Aguascalientes 20256, Mexico
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: George Karakostas
Algorithms 2015, 8(2), 336-365; https://doi.org/10.3390/a8020336
Received: 2 April 2015 / Revised: 1 June 2015 / Accepted: 3 June 2015 / Published: 19 June 2015
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. View Full-Text
Keywords: global optimization; nature-inspired methods; monkey algorithm; krill herd algorithm; hybridization global optimization; nature-inspired methods; monkey algorithm; krill herd algorithm; hybridization
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Khalil, A.M.; Fateen, S.-E.K.; Bonilla-Petriciolet, A. MAKHA—A New Hybrid Swarm Intelligence Global Optimization Algorithm. Algorithms 2015, 8, 336-365.

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