上海交通大学学报

上海交通大学学报（自然版） ›› 2012, Vol. 46 ›› Issue (02): 228-232.

• 自动化技术、计算机技术 • 上一篇    下一篇

惯性权自适应调整的量子粒子群优化算法  

黄泽霞1，2，俞攸红3，黄德才1   

  1.  (1浙江工业大学 信息学院， 杭州 310023；2绍兴文理学院元培学院 信息与电子系，浙江 绍兴 312000；3浙江工业大学 理学院， 杭州 310023)
  • 收稿日期:2010-12-07 出版日期:2012-02-28 发布日期:2012-02-28
  • 基金资助:

    国家自然科学基金项目(10774131)

QuantumBehaved Particle Swarm Algorithm with Selfadapting Adjustment of Inertia Weight

 HUANG  Ze-Xia-1, 2 , YU  You-Hong-3, HUANG  De-Cai-1   

  1. (1College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023,China; 2Department of Information and Electronic, Shaoxing University YuanPei College, Shaoxing Zhejiang 312000,China; 3College of Science, Zhejiang University of Technology, Hangzhou 310023,China)
  • Received:2010-12-07 Online:2012-02-28 Published:2012-02-28

摘要： 针对量子粒子群的惯性权值β线性递减不能适应复杂的非线性优化搜索过程的问题，提出了一种惯性权自适应调整的量子粒子群优化(DCWQPSO)算法.在该算法中，引入了量子粒子群进化速度因子sd和聚集度因子jd，并将惯性因子β表示为sd，jd2个参数的函数.在每次迭代时，算法可根据当前量子粒子群进化速度因子和聚集度因子动态地调整惯性权值，从而使算法具有动态自适应性.对典型的标准函数的测试结果表明，与量子粒子群算法相比，改进后的量子粒子群优化算法的收敛速度明显提高.     

关键词: 量子粒子群, 自适应, 惯性权

Abstract: A new quantumbehaved particle swarm algorithm with selfadapting adjustment of inertia weight was presented to solve the problem that the linearly decreasing weight of the quantumbehaved particle swarm algorithm cannot adapt to the complex and nonlinear optimization process. The evolution speed factor and aggregation degree factor of the swarm are introduced in this new algorithm and the weight is formulated as a function of these two factors according to their impact on the search performance of the swarm. In each iteration process, the weight is changed dynamically based on the current evolution speed factor and aggregation degree factor, which provides the algorithm with effective dynamic adaptability. The algorithms of quantumbehaved particle swarm were tested with benchmark functions. The experiments show that the convergence speed of adaptive quantumbehaved particle swarm algorithm is significantly superior to quantumbehaved particle swarm algorithm.

Key words: quantumbehaved particle swarm, adaptability, inertia weight

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