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A Novel Sigmoid-Function-Based Adaptive Weighted Particle Swarm Optimizer.

Weibo Liu, Zidong Wang, Yuan Yuan

    IEEE Transactions on Cybernetics
    |July 23, 2019
    PubMed
    Summary

    A new adaptive weighting PSO algorithm uses a sigmoid function to improve convergence speed. This novel approach enhances particle swarm optimization performance over existing methods.

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    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Swarm Intelligence

    Background:

    • Particle Swarm Optimization (PSO) is a widely used metaheuristic algorithm.
    • Standard PSO can suffer from premature convergence and slow exploration.
    • Adaptive strategies are crucial for enhancing PSO's efficiency.

    Purpose of the Study:

    • To introduce a novel Particle Swarm Optimization (PSO) algorithm with an adaptive weighting strategy.
    • To enhance the convergence rate and search capability of PSO.
    • To leverage sigmoid functions for adaptive adjustment of acceleration coefficients.

    Main Methods:

    • Developed a sigmoid-function-based weighting strategy to adaptively adjust acceleration coefficients.
    • The strategy considers particle distances to personal best and global best positions.
    • Evaluated the Adaptive Weighting PSO (AWPSO) algorithm on eight benchmark functions (unimodal and multimodal).

    Main Results:

    • The AWPSO algorithm demonstrated a significantly improved convergence rate.
    • Experimental results showed superior performance compared to several popular PSO variants.
    • The adaptive strategy effectively balances exploration and exploitation.

    Conclusions:

    • The proposed AWPSO algorithm offers a substantial improvement in convergence speed for PSO.
    • The sigmoid-based adaptive weighting strategy is effective in enhancing optimization performance.
    • AWPSO presents a promising alternative for complex optimization problems.