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计算机工程 ›› 2011, Vol. 37 ›› Issue (5): 204-206. doi: 10.3969/j.issn.1000-3428.2011.05.069

• 人工智能及识别技术 • 上一篇    下一篇

粒子群优化算法中的惯性权值非线性调整策略

周 敏1,李太勇2   

  1. (1. 中国民用航空飞行学院计算机学院,四川 广汉 618307;2. 西南财经大学经济信息工程学院,成都 610074)
  • 出版日期:2011-03-05 发布日期:2012-10-31
  • 作者简介:周 敏(1978-),女,讲师、硕士,主研方向:人工智能,知识工程;李太勇,讲师、博士
  • 基金资助:
    国家自然科学基金资助项目(60879023)

Nonlinear Adjustment Strategy of Inertia Weight in Particle Swarm Optimization Algorithm

ZHOU Min1, LI Tai-yong2   

  1. (1. School of Computer, Civil Aviation Flight University of China, Guanghan 618307, China; 2. School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 610074, China)
  • Online:2011-03-05 Published:2012-10-31

摘要: 为提升粒子群优化算法的性能,提出基于正弦曲线、正切曲线和对数曲线的非线性惯性权值调整策略。采用镜像策略对越界粒子进行处理,利用标准测试函数测试这些策略对算法的影响。实验结果表明,对于连续函数优化问题,正弦曲线和对数曲线策略优于传统的线性调整策略,而传统的线性调整策略又优于正切曲线策略。

关键词: 粒子群优化算法, 惯性权值, 非线性策略, 函数优化

Abstract: To improve the performance of Particle Swarm Optimization(PSO) algorithm, nonlinear strategy based on sinusoid, tangential and logarithmic curve is proposed in this paper. At the same time, a strategy on processing the particles out of range is proposed. Several classical benchmark functions are used to evaluate the strategies. Experimental results show that for continuous optimization problem, the proposed sinusoid and logarithmic curve strategies gain advantages over the classical linear strategy, while the linear strategy outperforms the tangential curve strategy.

Key words: Particle Swarm Optimization(PSO) algorithm, inertia weight, nonlinear strategy, function optimization

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