计算机应用 ›› 2016, Vol. 36 ›› Issue (3): 681-686.DOI: 10.11772/j.issn.1001-9081.2016.03.681

• 人工智能 • 上一篇    下一篇

基于多策略协同作用的粒子群优化算法

李俊1,2, 汪冲1,2, 李波1,2, 方国康1,2   

  1. 1. 武汉科技大学 计算机科学与技术学院, 武汉 430065;
    2. 智能信息处理与实时工业系统湖北省重点实验室(武汉科技大学), 武汉 430065
  • 收稿日期:2015-07-29 修回日期:2015-09-24 出版日期:2016-03-10 发布日期:2016-03-17
  • 通讯作者: 李俊
  • 作者简介:李俊(1978-),男,湖北黄石人,副教授,博士,主要研究方向:智能计算;汪冲(1992-),男,湖北孝感人,硕士研究生,主要研究方向:智能计算;李波(1975-),男,湖北武汉人,副教授,博士,主要研究方向:智能计算、机器学习;方国康(1994-),男,湖北恩施人,主要研究方向:智能计算。
  • 基金资助:
    国家自然科学基金资助项目(61273303)。

Particle swarm optimization algorithm based on multi-strategy synergy

LI Jun1,2, WANG Chong1,2, LI Bo1,2, FANG Guokang1,2   

  1. 1. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan Hubei 430065, China;
    2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System (Wuhan University of Science and Technology), Wuhan Hubei 430065, China
  • Received:2015-07-29 Revised:2015-09-24 Online:2016-03-10 Published:2016-03-17
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61273303).

摘要: 针对粒子群优化(PSO)算法容易早熟收敛、在进化后期收敛精度低的缺点,提出了一种基于多策略协同作用的粒子群优化(MSPSO)算法。首先,设定一个概率阈值为0.3,在粒子迭代过程中,如果随机生成的概率值小于阈值,则采用对当前种群中的最优个体进行反向学习并生成其反向解,以提高算法的收敛速度和收敛精度;否则,算法执行对粒子的位置进行高斯变异策略,以增强种群的多样性;其次,提出一种将柯西分布的比例参数进行线性递减的柯西变异策略,能够产生更好的解引导粒子向最优解空间运动;最后,在8个标准测试函数上进行仿真测试,MSPSO算法在Rosenbrock、Schwefel's P2.22、Rotated Ackley、Quadric Noise、Ackley函数上收敛的平均值分别为1.68E+01、2.36E-283、8.88E-16、2.78E-05、8.88E-16,在Sphere、Griewank和Rastrigin函数上收敛达到最优解0,优于高斯扰动粒子群优化(GDPSO)算法、基于柯西变异的反向学习粒子群优化(GOPSO)算法。结果表明,所提出的算法收敛精度高,能避免粒子陷入局部最优。

关键词: 粒子群优化算法, 反向学习, 高斯变异, 柯西变异, 线性递减

Abstract: Aiming at the shortage that Particle Swarm Optimization (PSO) algorithm is easy to fall into local optima and has low precision at later evolution process, a modified Multi-Strategies synergy PSO (MSPSO) algorithm was proposed. Firstly, a probability threshold value of 0.3 was set. In every iteration, if the randomly generated probability value was less than the threshold, the algorithm with opposition-based learning for the best individual was adopted to generate their opposite solutions, which improved the convergence speed and precision of PSO; otherwise, Gaussian mutation strategy was adopted for the particle position to enhance the diversity of population. Secondly, a Cauchy mutation strategy for linearly decreasing cauchy distribution scale parameter decreased was proposed, to generate better solution to guide the particle to approximate the optimum space. Finally, the simulation experiments were conducted on eight benchmark functions. MSPSO algorithm has the convergence mean value of 1.68E+01, 2.36E-283, 8.88E-16, 2.78E-05, 8.88E-16, respectively in Rosenbrock, Schwefel's P2.22, Rotated Ackley, Quadric Noise and Ackley, and can converge to the optimal solution of 0 in Sphere, Griewank and Rastrigin, which is better than GDPSO (PSO based on Gaussian Disturbance) and GOPSO (PSO based on global best Cauchy mutation and Opposition-based learning). The results show that proposed algorithm has higher convergence accuracy and can effectively avoid being trapped in local optimal solution.

Key words: Particle Swarm Optimization (PSO) algorithm, opposition-based learning, Gaussian mutation, Cauchy mutation, linear decreasing

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