Name
Abstract | ||
|---|---|---|
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. Lévy 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. © 2011 IEEE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICNC.2011.6022225 | ICNC |
Keywords | DocType | Volume |
lévy flight,particle swarm optimization,power law distribution,random search strategies,evolutionary computation,np hard problem,statistical analysis,evolutionary algorithm,levy flight,random processes,convergence,optimization,computational complexity,benchmark testing | Conference | 2 |
Issue | Citations | PageRank |
null | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Huang | 1 | 0 | 1.01 |
| Yuanming Long | 2 | 0 | 0.34 |
| Jinhang Li | 3 | 0 | 1.01 |