Abstract:
To update the performance of the standard optimization method, a new Particle Swarm Optimization(PSO) algorithm is proposed based on the earlier works. A non-symmetric learning factor adjusting method introduced here is to keep the balance between the global search and the local search with the great advantages of convergence property and robustness compared with standard PSO algorithm. The relationship between swarm average velocity and convergence is studied through Benchmark test functions simulation. All the merits mentioned above are demonstrated by the compound gear transmission ratio optimization in transmission systems.
Key words:
Particle Swarm Optimization(PSO) algorithm,
learning factor,
test function,
compound gear transmission ratio optimization
摘要: 分析粒子群优化算法中2个学习因子对粒子收敛性的影响,通过Benchmark标准测试函数对不同取值的学习因子进行测试,提出一种基于非对称学习因子调节策略的改进粒子群算法。在搜索初期使粒子获得更好的多样性及较强的摆脱局部极值的能力,在搜索后期加快粒子的收敛速度,提高全局寻优能力。该算法已在复合齿轮传动系统的传动比优化设计中得到了成功应用。
关键词:
粒子群优化算法,
学习因子,
测试函数,
复合齿轮传动比优化
CLC Number:
MAO Kai-Fu, BAO An-Qing, XU Chi. Particle Swarm Optimization Algorithm Based on Non-symmetric Learning Factor Adjusting[J]. Computer Engineering, 2010, 36(19): 182-184.
毛开富, 包广清, 徐驰. 基于非对称学习因子调节的粒子群优化算法[J]. 计算机工程, 2010, 36(19): 182-184.