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Most Valuable Player Algorithm: a novel optimization algorithm inspired from sport

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  • H. R. E. H. Bouchekara

    (University of Freres Mentouri Constantine)

Abstract

In this paper a new metaheuristic called the Most Valuable Player Algorithm (MVPA) is proposed for solving optimization problems. The developed algorithm is inspired from sport where players form teams, then these players compete collectively (in teams) in order to win the championship and they compete also individually in order to win the MVP trophy. The performances of MVPA are evaluated on a set of 100 mathematical test functions. The obtained results are compared with the ones obtained using 13 well-known optimization algorithms. These results demonstrate that, the MVPA is a very competitive optimization algorithm, it converges rapidly (with smaller number of functions evaluations) and more successfully (with higher overall success percentage) than the compared algorithms. Therefore, further developments and applications of MVPA would be worth investigating in future studies.

Suggested Citation

  • H. R. E. H. Bouchekara, 2020. "Most Valuable Player Algorithm: a novel optimization algorithm inspired from sport," Operational Research, Springer, vol. 20(1), pages 139-195, March.
    • Handle: RePEc:spr:operea:v:20:y:2020:i:1:d:10.1007_s12351-017-0320-y
    DOI: 10.1007/s12351-017-0320-y
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    References listed on IDEAS

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    1. Sudhanshu K Mishra, 2013. "Global Optimization of Some Difficult Benchmark Functions by Host-Parasite Coevolutionary Algorithm," Economics Bulletin, AccessEcon, vol. 33(1), pages 1-18.
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    Cited by:

    1. Qiang Yang & Yuanpeng Zhu & Xudong Gao & Dongdong Xu & Zhenyu Lu, 2022. "Elite Directed Particle Swarm Optimization with Historical Information for High-Dimensional Problems," Mathematics, MDPI, vol. 10(9), pages 1-29, April.

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