Name
Abstract | ||
|---|---|---|
Inspired by the mechanism of small-world phenomenon, some small-world optimization operators, mainly including the local short-range searching operator and random long-range searching operator, are constructed in this paper. And a new optimization algorithm, Small-World Optimization Algo-rithm (SWOA) is explored. Compared with the corresponding Genetic Algorithms (GAs), the simulation experiment results of some complex functions optimization indicate that SWOA can enhance the diversity of the population, avoid the prematurity and GA deceptive problem to some extent, and have the high convergence speed. SWOA is shown to be an effective strategy to solve complex tasks. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11881223_33 | ICNC (2) |
Keywords | Field | DocType |
high convergence speed,small-world phenomenon,small-world optimization algo-rithm,ga deceptive problem,small-world optimization algorithm,effective strategy,new optimization algorithm,complex function,function optimization,complex task,corresponding genetic algorithms,small-world optimization operator,simulation experiment,genetic algorithm | Derivative-free optimization,Stochastic optimization,Mathematical optimization,Computer science,Vector optimization,Test functions for optimization,Meta-optimization,Algorithm,Multi-swarm optimization,Random optimization,Metaheuristic | Conference |
Volume | ISSN | ISBN |
4222 | 0302-9743 | 3-540-45907-3 |
Citations | PageRank | References |
23 | 1.32 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haifeng Du | 1 | 421 | 30.96 |
| Xiaodong Wu | 2 | 23 | 1.32 |
| Jian Zhuang | 3 | 104 | 15.09 |