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An External Archive-Guided Multiobjective Particle Swarm Optimization Algorithm.

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    This study introduces an archive-guided multiobjective particle swarm optimization (AgMOPSO) algorithm. AgMOPSO enhances swarm leader selection for faster convergence and superior performance on multiobjective optimization problems.

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    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Swarm Intelligence

    Background:

    • Effective leader selection is crucial for multiobjective particle swarm optimization (MOPSO) algorithms to approximate the Pareto optimal front.
    • Existing MOPSO methods face challenges in guiding swarms effectively towards optimal solutions.

    Purpose of the Study:

    • To propose a novel external archive-guided MOPSO algorithm (AgMOPSO) for improved performance in multiobjective optimization.
    • To enhance swarm exploration and convergence speed through innovative leader selection and update mechanisms.

    Main Methods:

    • Developed AgMOPSO, selecting leaders for velocity updates exclusively from an external archive.
    • Transformed multiobjective optimization problems (MOPs) into subproblems, assigning particles for individual optimization.
    • Implemented an archive-guided velocity update method and an immune-based evolutionary strategy for archive evolution.

    Main Results:

    • AgMOPSO demonstrated superior performance on most tested MOPs compared to existing methods, based on standard performance metrics.
    • Experimental validation confirmed the effectiveness of the archive-guided velocity update and immune-based evolutionary strategy across over 30 MOPs.

    Conclusions:

    • The proposed AgMOPSO algorithm effectively improves convergence speed and solution quality for MOPs.
    • The novel leader selection and update strategies are key contributors to AgMOPSO's enhanced performance.