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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Small-World Optimization Algorithm for Function Optimization
verfasst von : Haifeng Du, Xiaodong Wu, Jian Zhuang
Erschienen in: Advances in Natural Computation
Verlag: Springer Berlin Heidelberg
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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.