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Multi-objective volleyball premier league algorithm
Knowledge-Based Systems ( IF 5.101 ) Pub Date : 2020-03-25 , DOI: 10.1016/j.knosys.2020.105781
Reza Moghdani; Khodakaram Salimifard; Emrah Demir; Abdelkader Benyettou

This paper proposes a novel optimization algorithm called the Multi-Objective Volleyball Premier League (MOVPL) algorithm for solving global optimization problems with multiple objective functions. The algorithm is inspired by the teams competing in a volleyball premier league. The strong point of this study lies in extending the multi-objective version of the Volleyball Premier League algorithm (VPL), which is recently used in such scientific researches, with incorporating the well-known approaches including archive set and leader selection strategy to obtain optimal solutions for a given problem with multiple contradicted objectives. To analyze the performance of the algorithm, ten multi-objective benchmark problems with complex objectives are solved and compared with two well-known multi-objective algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D). Computational experiments highlight that the MOVPL outperforms the two state-of-the-art algorithms on multi-objective benchmark problems. In addition, the MOVPL algorithm has provided promising results on well-known engineering design optimization problems.
更新日期:2020-03-26

 

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