A cloud model based fruit fly optimization algorithm

被引:69
作者
Wu, Lianghong [1 ]
Zuo, Cili [1 ]
Zhang, Hongqiang [1 ]
机构
[1] Hunan Univ Sci & Technol, Sch Informat & Elect Engn, Xiangtan 411201, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
Fruit fly optimization algorithm; Cloud model; Randomness; Fuzziness; FINANCIAL DISTRESS MODEL; JOINT REPLENISHMENT; NEURAL-NETWORK; PERFORM;
D O I
10.1016/j.knosys.2015.09.006
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Fruit Fly Optimization Algorithm (FOA) is a new global optimization algorithm inspired by the foraging behavior of fruit fly swarm. However, similar to other swarm intelligence based algorithms, FOA also has its own disadvantages. To improve the convergence performance of FOA, a normal cloud model based FOA (CMFOA) is proposed in this paper. The randomness and fuzziness of the foraging behavior of fruit fly swarm in osphresis phase is described by the normal cloud model. Moreover, an adaptive parameter strategy for Entropy En in normal cloud model is adopted to improve the global search ability in the early stage and to improve the accuracy of solution in the last stage. 33 benchmark functions are used to test the effectiveness of the proposed method. Numerical results show that the proposed CMFOA can obtain better or competitive performance for most test functions compared with three improved FOAs in recent literatures and seven state-of-the-arts of intelligent optimization algorithm. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:603 / 617
页数:15
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