Journal of System Simulation
Abstract
Abstract: Many existing strategies for improving particle swarm optimization (PSO) fall short in assisting particles trapped in local optima and experiencing premature convergence to recover optimization performance. In response, an adaptive particle swarm optimization algorithm based on trap label and lazy ant (TLLA-APSO) is proposed. Firstly, the trap label strategy dynamically adjusts particle velocities, enabling the particle swarm to escape from local optima. Secondly, the lazy ant optimization strategy is employed to diversify particle velocity and enhance population diversity. Finally, the inertia cognition strategy introduces historical position into velocity updates, promoting path diversity and particle exploration while effectively mitigating the risk of falling into new local optimum. The convergence of the particle swarm algorithm with the incorporation of historical positions has been empirically demonstrated. Simulation results validate the efficacy of TLLA-APSO, showcasing its ability to mitigate local optima and premature convergence while achieving faster convergence speed and higher optimization accuracy compared with other algorithms.
Recommended Citation
Zhang, Wei and Jiang, Yuefeng
(2024)
"Adaptive Particle Swarm Optimization Algorithm Based on Trap Label and Lazy Ant,"
Journal of System Simulation: Vol. 36:
Iss.
7, Article 11.
DOI: 10.16182/j.issn1004731x.joss.23-0401
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss7/11
First Page
1631
Last Page
1642
CLC
TP391.9
Recommended Citation
Zhang Wei, Jiang Yuefeng. Adaptive Particle Swarm Optimization Algorithm Based on Trap Label and Lazy Ant[J]. Journal of System Simulation, 2024, 36(7): 1631-1642.
DOI
10.16182/j.issn1004731x.joss.23-0401
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