Name
Abstract | ||
|---|---|---|
The random drift particle swarm optimization (RDPSO) algorithm, inspired by the free electron model in metal conductors placed in an external electric field, is presented, systematically analyzed and empirically studied in this paper. The free electron model considers that electrons have both a thermal and a drift motion in a conductor that is placed in an external electric field. The motivation of the RDPSO algorithm is described first, and the velocity equation of the particle is designed by simulating the thermal motion as well as the drift motion of the electrons, both of which lead the electrons to a location with minimum potential energy in the external electric field. Then, a comprehensive analysis of the algorithm is made, in order to provide a deep insight into how the RDPSO algorithm works. It involves a theoretical analysis and the simulation of the stochastic dynamical behavior of a single particle in the RDPSO algorithm. The search behavior of the algorithm itself is also investigated in detail, by analyzing the interaction between the particles. Some variants of the RDPSO algorithm are proposed by incorporating different random velocity components with different neighborhood topologies. Finally, empirical studies on the RDPSO algorithm are performed by using a set of benchmark functions from the CEC2005 benchmark suite. Based on the theoretical analysis of the particle's behavior, two methods of controlling the algorithmic parameters are employed, followed by an experimental analysis on how to select the parameter values, in order to obtain a good overall performance of the RDPSO algorithm and its variants in real-world applications. A further performance comparison between the RDPSO algorithms and other variants of PSO is made to prove the efficiency of the RDPSO algorithms. |
Year | Venue | Field |
|---|---|---|
2013 | CoRR | Particle swarm optimization,Thermal,Electric field,Computer science,Electrical conductor,Network topology,Potential energy,Artificial intelligence,Particle,Machine learning,Electron |
DocType | Volume | Citations |
Journal | abs/1306.2863 | 3 |
PageRank | References | Authors |
0.43 | 24 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Sun | 1 | 1060 | 79.09 |
| Xiaojun Wu | 2 | 230 | 11.79 |
| Vasile Palade | 3 | 1353 | 114.44 |
| Wei Fang | 4 | 339 | 19.89 |
| Yuhui Shi | 5 | 4397 | 435.39 |