摘要
针对粒子群算法易陷入局部最优等问题,分析了粒子群算法的进化方程,提出了一种改进的粒子群优化算法。算法在振荡环节采用互不相同的参数取值来调节粒子群算法的全局和局部搜索能力,并通过对测试函数和机器人路径规划问题仿真模拟,与标准PSO、标准二阶PSO、二阶振荡PSO算法的实验结果进行对比分析,验证了所提出算法的有效性和可行性。
Aiming at some demerits of Particle Swarm Optimization(PSO), such as falling into local optimum, analyzing the evolutionary equation of particle swarm optimization, an improved particle swarm optimization algorithm is proposed.The algorithm uses different parameters in the oscillation, which is used to adjust the global and local search capabilities of the particle swarm algorithm. The experimental results of the test function and the robot path planning are compared and analyzed with the experimental results of standard PSO, two-order PSO and two-order oscillating PSO algorithm,which verifies the effectiveness and feasibility of the improved algorithm.
作者
蒋丽
叶润舟
梁昌勇
陆文星
JIANG Li;YE Runzhou;LIANG Changyong;LU Wenxing(School of Management, Hefei University of Technology, Hefei 230009, China;Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, China)
出处
《计算机工程与应用》
CSCD
北大核心
2019年第9期130-138,167,共10页
Computer Engineering and Applications
基金
国家自然科学基金(No.71331002
No.71601061
No.71502047
No.71771077)
教育部人文社科项目(No.17YJA630037)
中央高校基本科研业务费专项资金(No.JS2017HGXJ0044)
国家重点研发计划项目(No.2016YFC0803203)
关键词
粒子群算法
二阶振荡进化方程
收敛精度
机器人路径规划
Particle Swarm Optimization(PSO)
two-order oscillating evolutionary equation
convergence precision
robot path planning
