MLPSO: Multi-Leader particle swarm optimization for multi-objective optimization problems

Zuwairie Ibrahim*, Kian Sheng Lim, Salinda Buyamin, Siti Nurzulaikha Satiman, Mohd Helmi Suib, Badaruddin Muhammad, Mohd Riduwan Ghazali, Mohd Saberi Mohamad, Junzo Watada

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)


    The particle swarm optimization (PSO) algorithm, which uses the best experience of an individual and its neighborhood to find the optimum solution, has proven useful in solving various optimization problems, including multiobjective optimization (MOO) problems. In MOO problems, existing multi-objective PSO algorithms use one or two leaders to guide the movement of every particle in a search space. This study introduces the concept of multiple leaders to guide the particles in solving MOO problems. In the proposed Multi-Leader PSO (MLPSO) algorithm, the movement of a particle is determined by all leaders that dominate that particle. This concept allows for more information sharing between particles. The performance of the MLPSO is assessed by several benchmark test problems, with their convergence and diversity values are computed. Solutions with good convergence and diversity prove the superiority of the proposed algorithm over MOPSOrand algorithm.

    Original languageEnglish
    Pages (from-to)17533-17538
    Number of pages6
    JournalARPN Journal of Engineering and Applied Sciences
    Issue number23
    Publication statusPublished - 2015


    • Convergence
    • Diversity
    • Multi-objective optimization
    • Multiple leaders
    • Particle swarm optimization

    ASJC Scopus subject areas

    • Engineering(all)


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