Levy Flight search patterns in Particle Swarm Optimization
There has been a growing interest in studying of random search strategies. In many industries including manufacturing, logistics, computer etc., researchers use evolutionary algorithms to solve sophisticated optimization problems which have stationary or shifty optimal values. These problems could hardly be solved with precise mathematical methods, called non-deterministic Polynomial-time hard (NP-hard) problems. Particle swarm optimization (PSO) is one of those algorithm and attracts extra attention. In this paper, we put forward a new model to explore the step length of search process of PSO, via statistics methods. Typical two-dimensional and multi-dimensional benchmark functions are used to generate empirical data for further analysis. Levy flight search patterns finally proved to play an important role in the searching process. Then the relationship between the values of scaling parameters in power law distributions and the efficiency of PSO is discussed. More interesting results are given in discussion.
Random search strategies Particle swarm optimization Levy flight Power law distribution.
HUANG gang LONG yuanming LI jinhang LONG yuanming
State Key Laboratory of Digital Manufacturing Equipment & Technology Huazhong University of Science Department of mechanical engineering and automation, School of Mechanical Science and Engineering Hu
国际会议
2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)
上海
英文
1211-1215
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）