Particle swarm optimization algorithm with sinusoidal changing inertia weight

Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (8): 40-42.

• 研究、探讨 • Previous Articles Next Articles

Particle swarm optimization algorithm with sinusoidal changing inertia weight

JIANG Changyuan1，2, ZHAO Shuguang1, SHEN Shigen1, GUO Lizheng1

1.College of Information Science and Technology, Donghua University, Shanghai 201600, China
2.School of Science, Huzhou Teachers College, Huzhou, Zhejiang 313000, China

Received:1900-01-01 Revised:1900-01-01 Online:2012-03-11 Published:2012-03-11

惯性权重正弦调整的粒子群算法

姜长元1，2，赵曙光1，沈士根1，郭力争1

1.东华大学信息学院，上海 201600
2.湖州师范学院理学院，浙江湖州 313000

Abstract

Abstract: Based on analyzing inertia weight of the standard Particle Swarm Optimization（PSO） algorithm, a new PSO algorithm with sinusoidal changing inertia weight（S-PSO） is presented. Convergence condition of PSO is obtained through solving and analyzing the differential equation. By the experiments of four Benchmark function, the results show the performance of S-PSO is improved more clearly than the standard PSO and random inertia weight PSO. Theoretical analysis and simulation experiments show that the S-PSO is efficient and feasible.

Key words: particle swarm optimization algorithm, inertia weight, sinusoidal changing, differential equation

摘要： 通过对标准粒子群算法中惯性权重的分析，提出了一种惯性权重正弦调整的粒子群算法。运用差分方程对粒子速度变化过程和位置变化过程进行分析，得到了粒子群算法的收敛条件。通过对4个典型的函数的测试，实验结果表明该方法在收敛速度和全局收敛性方面都比标准粒子群算法和随机惯性权重粒子群算法有明显改进。理论分析和仿真实验验证了新算法的正确性和有效性。

关键词: 粒子群算法, 惯性权重, 正弦调整, 差分方程

JIANG Changyuan1，2, ZHAO Shuguang1, SHEN Shigen1, GUO Lizheng1. Particle swarm optimization algorithm with sinusoidal changing inertia weight[J]. Computer Engineering and Applications, 2012, 48(8): 40-42.

姜长元1，2，赵曙光1，沈士根1，郭力争1. 惯性权重正弦调整的粒子群算法[J]. 计算机工程与应用, 2012, 48(8): 40-42.

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Particle swarm optimization algorithm with sinusoidal changing inertia weight

惯性权重正弦调整的粒子群算法

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