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Erschienen in:
2017 | OriginalPaper | Buchkapitel
3D-FOAdis: An Improved Fruit Fly Optimization for Function Optimization
verfasst von : Kejie Wang, Huiling Chen, Qiang Li, Junjie Zhu, Shubiao Wu, Hui Huang
Erschienen in: Advances in Swarm Intelligence
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Abstract
In fruit fly optimization algorithm (FOA), the search speed of each fruit fly is fast, but when it traps into the local optimum, it is difficult to re-find a better solution. In order to overcome this drawback, we propose an improved version of FOA, termed as 3D-FOAdis. In the proposed method, three-dimensional coordinates and the disturbance mechanism were both introduced. We firstly extends the original two-dimensional coordinates to three-dimensional coordinates, where fruit flies can fly more widely so that it is more likely to jump out of the local optimum. Then we introduce a disturbance mechanism force the FOA to find better solutions when the fruit flies fall into the local optimums. The effectiveness of 3D-FOAdis has been rigorously evaluated against the nine benchmark functions. The experimental results demonstrate that the proposed approach outperforms the other two counterparts.