CODEQ: an effective metaheuristic for continuous global optimisation. (20th July 2010)

• Record Type: Journal Article
• Authors: Omran, Mahamed G.H.
• Is Part Of: International journal of metaheuristics. Volume 1:Number 2(2010)
• Page Start: 108
• Page End: 131
• Publication Date: 2010-07-20
• Subjects: metaheuristics -- differential evolution -- chaotic search -- quantum mechanics -- opposition-based learning -- OBL -- stochastic search -- continuous global optimisation; Heuristic algorithms -- Periodicals; 006.3105
• Languages: English
• ISSNs: 1755-2176

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.