陷阱标记联合懒蚂蚁的自适应粒子群优化算法  

Adaptive Particle Swarm Optimization Algorithm Based on Trap Label and Lazy Ant

作　　者：张伟[1] 蒋岳峰 Zhang Wei;Jiang Yuefeng(College of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454003,China)

机构地区：[1]河南理工大学电气工程与自动化学院,河南焦作454003

出　　处：《系统仿真学报》2024年第7期1631-1642,共12页Journal of System Simulation

基　　金：国家自然科学基金(61703145);河南省科技攻关项目(222102210213)。

摘　　要：为解决现有粒子群改进策略无法帮助已陷入局部最优和过早收敛的粒子恢复寻优性能的问题,提出一种陷阱标记联合懒蚂蚁的自适应粒子群优化(adaptive particle swarm optimization based on trap label and lazy ant, TLLA-APSO)算法。陷阱标记策略为粒子群提供动态速度增量,使其摆脱最优解的束缚。利用懒蚂蚁寻优策略多样化粒子速度,提升种群多样性。通过惯性认知策略在速度更新中引入历史位置,增加粒子的路径多样性和提升粒子的探索性能,使粒子更有效地避免陷入新的局部最优。理论证明了引入历史位置的粒子群算法的收敛性。仿真实验结果表明,所提算法不仅能有效解决粒子群已陷入局部最优和过早收敛的问题,且与其他算法相比,具有较快的收敛速度和较高的寻优精度。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.

关 键 词：粒子群优化算法 懒蚂蚁 陷阱标记 局部最优 过早收敛 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]