| 惯性权重正弦调整的粒子群算法 |
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| 引用本文： | 姜长元,赵曙光,沈士根,郭力争.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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| 作者姓名： | 姜长元 赵曙光 沈士根 郭力争 |
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| 作者单位： | 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 |
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| 基金项目： | 浙江省教育厅基金（No.Y201016350）;湖州市自然科学基金（No.2010YZ05）. |
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| 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。
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| 关 键 词： | 粒子群算法 惯性权重 正弦调整 差分方程 |
Particle swarm optimization algorithm with sinusoidal changing inertia weight |
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| JIANG Changyuan
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ZHAO Shuguang
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SHEN Shigen
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GUO Lizheng.Particle swarm optimization algorithm with sinusoidal changing inertia weight[J].Computer Engineering and Applications,2012,48(8):40-42. |
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| Authors: | JIANG Changyuan ZHAO Shuguang SHEN Shigen GUO Lizheng |
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| Affiliation: | 1.College of Information Science and Technology, Donghua University, Shanghai 201600, China 2.School of Science, Huzhou Teachers College, Huzhou, Zhejiang 313000, China |
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| 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. |
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| Keywords: | particle swarm optimization algorithm inertia weight sinusoidal changing differential equation |
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