Multi-Objective Volleyball Premier League algorithm

Reza Moghdani, Khodakaram Salimifard, Emrah Demir, Abdelkader Benyettou

    Research output: Contribution to journalArticlepeer-review

    16 Scopus citations


    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.

    Original languageBritish English
    Article number105781
    JournalKnowledge-Based Systems
    StatePublished - 21 May 2020


    • Engineering design optimization problems
    • Global optimization
    • Multi-objective evolutionary algorithm
    • Pareto solution


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