Title: CODEQ: an effective metaheuristic for continuous global optimisation
Authors: Mahamed G.H. Omran
Addresses: Department of Computer Science, Gulf University for Science and Technology, P.O. Box 7207, Hawally, 32093, Kuwait
Abstract: CODEQ is a new, parameter-free meta-heuristic algorithm that is hybrid of concepts from chaotic search, opposition-based learning, differential evolution (DE) and quantum mechanics. The performance of the proposed approach is investigated and compared with other well-known population-based optimisation approaches when applied to solve 19 benchmark functions. The conducted experiments show that CODEQ presents excellent results with almost no parameter tuning. Moreover, the performance of CODEQ when applied to high-dimensional problems is investigated with excellent results. Finally, the application of CODEQ to constrained and real-world engineering problems is investigated with encouraging results.
Keywords: metaheuristics; differential evolution; chaotic search; quantum mechanics; opposition-based learning; OBL; stochastic search; continuous global optimisation.
DOI: 10.1504/IJMHEUR.2010.034202
International Journal of Metaheuristics, 2010 Vol.1 No.2, pp.108 - 131
Published online: 19 Jul 2010 *
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