CAJ | 학술논문

为了避免普通粒子群算法（PSO）可能出现的局部收敛及精度不高现象，围绕影响PSO算法性能的两个重要参数w和 pgd ，提出了一种面向全局优化的参数自适应变异PSO改进算法。算法定义了粒子熵集概念，可以精确反映粒子群数据的全局聚集特性，由粒子群各维数据的熵值大小决定是否对各维数据的惯性权重w进行回归变异，对全局变量 p gd 进行随机变异，并采取引入变异次数因子等方法来避免寻优发散。仿真研究表明该算法比常用算法在寻优精度、摆脱局部陷阱、稳定性等方面均有明显提高，在求解复杂多峰问题上有着良好的表现。
위료피면보통입자군산법（PSO）가능출현적국부수렴급정도불고현상，위요영향PSO산법성능적량개중요삼수w화 pgd ，제출료일충면향전국우화적삼수자괄응변이PSO개진산법。산법정의료입자적집개념，가이정학반영입자군수거적전국취집특성，유입자군각유수거적적치대소결정시부대각유수거적관성권중w진행회귀변이，대전국변량 p gd 진행수궤변이，병채취인입변이차수인자등방법래피면심우발산。방진연구표명해산법비상용산법재심우정도、파탈국부함정、은정성등방면균유명현제고，재구해복잡다봉문제상유착량호적표현。
A new Particle Swarm Optimization(PSO)algorithm with global optimization of parameters for adaptive muta-tion is proposed around two key parameters w and pgd which all affect PSO algorithm performance to avoid the possible problems about local convergence and low precision. The concept of particle entropy set is defined which can accurately reflect the PSO data global aggregation behavior. The regression variance for inertia weight w of swarm dimensional data and the random variance for global variable pgd are determined by the particle entropy of every dimension data, and the method of using mutation frequency factor is used to avoid divergence in the algorithm. Simulation results show that com-pared with the conventional algorithm there are great advantages in optimization precision, getting rid of local traps, stabil-ity, etc, and good performance in solving complex multimodal problems with this algorithm.