带冲撞和制动的自适应粒子群优化算法  

Adaptive Particle Swarm Optimization Algorithm with Strategy of Collision and Braking

作　　者：李眩[1] 吴晓兵[1] 方婷婷 Li Xuan;Wu Xiaobing;Fang Tingting(Department of Economics and Trade,Tongling Vocational Technology College,Tongling,Anhui 244061,China)

机构地区：[1]铜陵职业技术学院经贸系,安徽铜陵244061

出　　处：《大理大学学报》2023年第12期22-26,共5页Journal of Dali University

基　　金：安徽省省级质量工程项目(2021xdxtz069);安徽省教育厅科学研究基金项目(2023AH052884)。

摘　　要：在粒子群优化算法惯性权重自适应调整的基础上,针对算法易陷入局部极值难以摆脱的情形,借鉴沙丁鱼受刺激加速游动避免死亡的原理,运用冲撞策略模拟外部刺激增强算法摆脱局部最优束缚的能力;为了兼顾算法的全局探索和局部精细搜索能力,引入非线性自适应调整制动算子对应调整粒子的速度,并将改进的粒子群优化算法应用于多维函数寻优。实验结果表明带冲撞和制动的自适应粒子群优化算法比标准粒子群优化算法有更好的算法效率和全局寻优能力。Based on adaptive adjustment of the inertia weight in the particle swarm optimization algorithm,this paper addressed the issue of the algorithm easily falling into local optima and being difficult to escape,inspired by the principle of sardine being stimulated to accelerate swimming to avoid death,a collision strategy is used to simulate external stimuli and enhance its ability to the algorithm's ability to break free from local optima.In order to balance the ability of global exploration and local fine search,a nonlinear adaptive adjustment braking operator is introduced to adjust the velocity of particles accordingly.The improved particle swarm optimization algorithm is applied to multi-dimensional function optimization,and the experimental results show that the adaptive particle swarm optimization algorithm with strategy of collision and braking has better algorithm efficiency and global optimization ability compared to the standard particle swarm optimization algorithm.

关 键 词：冲撞 制动 粒子群优化算法 局部极值 惯性权重 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]