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<p>Particle swarm optimization (PSO) has been widely used, in which each particle selects its learning sample relying on fitness information. Intuitively, fitness-based selection strategy is beneficial to optimization. However, excessive reliance on fitness information may cause premature convergence of the whole population. To solve the defects of PSO, a lightweight evolutionary-state-driven multiple information learning particle swarm optimization algorithm (LEM-PSO) is proposed. In the new proposed LEM-PSO, firstly, a lightweight multiple information learning strategy is proposed. Then, adaptive evolutionary-state adjustment mechanism is proposed. Finally, local optimum warning operation is used to help the stagnant population to jump from local optimums. The comprehensive performance of LEM-PSO is compared with seven popular PSO variants on CEC2013, CEC2017 and two engineering problems, and the results confirm the firmness of LEM-PSO.</p>

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LEM-PSO: a lightweight evolutionary-state-driven multiple information learning particle swarm optimization algorithm

  • 期刊论文

  • 发表日期：

    2025年4月16日

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  • Xu Yang,
  • Hongru Li

摘要

Particle swarm optimization (PSO) has been widely used, in which each particle selects its learning sample relying on fitness information. Intuitively, fitness-based selection strategy is beneficial to optimization. However, excessive reliance on fitness information may cause premature convergence of the whole population. To solve the defects of PSO, a lightweight evolutionary-state-driven multiple information learning particle swarm optimization algorithm (LEM-PSO) is proposed. In the new proposed LEM-PSO, firstly, a lightweight multiple information learning strategy is proposed. Then, adaptive evolutionary-state adjustment mechanism is proposed. Finally, local optimum warning operation is used to help the stagnant population to jump from local optimums. The comprehensive performance of LEM-PSO is compared with seven popular PSO variants on CEC2013, CEC2017 and two engineering problems, and the results confirm the firmness of LEM-PSO.