Binary mean-variance mapping optimization algorithm (BMVMO)

Ali Hakem Al-Saeedi and OÄŸuz Altun
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Ali Hakem Al-Saeedi: Computer Engineering Department, Yildiz Technical University, Istanbul, Turkey
OÄŸuz Altun: Computer Engineering Department, Yildiz Technical University, Istanbul, Turkey

Journal of Applied and Physical Sciences, 2016, vol. 2, issue 2, 42-47

Abstract: Mean-Variance Mapping Optimization (MVMO) is the newest class of the modern meta-heuristic algorithms. The original version of this algorithm is suitable for continuous search problems, so can’t apply it directly to discrete search problems. In this paper, the binary version of the MVMO (BMVMO) algorithm proposed. The proposed Binary Mean-Variance Mapping Optimization algorithm compare with well-known binary meta-heuristic optimization algorithms such, Binary genetic Algorithm, Binary Particles Swarm Optimization, and Binary Bat Algorithm over fifteen benchmark functions conducted to draw a conclusion. The numeric experiments result proves that BMVMO is better performance.

Keywords: Mean-Variance Mapping; Optimization; Binary Meta-Heuristic; Optimization; Discrete evolutionary; Algorithms (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:apb:japsss:2016:p:42-47

DOI: 10.20474/japs-2.2.3

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