PDF(445 KB)
A New Fruit Fly Optimization Algorithm Based on Differential Evolution
Dabin ZHANG, Jia YE, Zhigang ZHOU, Yuqi LUAN
Journal of Systems Science and Information ›› 2015, Vol. 3 ›› Issue (4) : 365-373.
PDF(445 KB)
PDF(445 KB)
A New Fruit Fly Optimization Algorithm Based on Differential Evolution
In order to overcome the problem of low convergence precision and easily relapsing into local extremum in fruit fly optimization algorithm (FOA), this paper adds the idea of differential evolution to fruit fly optimization algorithm so as to optimizing and a algorithm of fruit fly optimization based on differential evolution is proposed (FOADE). Adding the operating of mutation, crossover and selection of differential evolution to FOA after each iteration, which can jump out local extremum and continue to optimize. Compared to FOA, the experimental results show that FOADE has the advantages of better global searching ability, faster convergence and more precise convergence.
fruit fly optimization algorithm / differential evolution / optimization / global optimization {{custom_keyword}} /
[1] Pan W T. A new fruit fly optimization algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 2012, 26(2): 69-74.
[2] Pan W T. Fruit fly optimization algorithm. Taipei: Tsang Hai Book Publishing Co, 2011.
[3] Han J Y, Liu C Z. Adaptive chaos fruit fly optimization algorithm. Journal of Computer Applications, 2013, 33(5): 1316-1333.
[4] Han J Y, Liu C Z. Fruit fly optimization algorithm with adaptive mutation. Application Research of Computers, 2013, 30(9): 2641-2644.
[5] Han J Y, Liu C Z. Fruit fly optimization algorithm based on bacterial chemotaxis. Journal of Computer Applications, 2013, 33(4): 964-966, 1038.
[6] Cheng H, Liu C Z. Mixed fruit fly optimization algorithm based on chaotic mapping. Computer Engineering, 2013, 39(5): 218-221.
[7] Storn R, Price K. Differential evolution — A simple and efficient adaptive scheme for global optimization over continuous spaces. International Computer Science Institute, 1995, 3.
[8] Storn R, Price K. Differential evolution — A simple and efficient adaptive scheme for global optimization over continuous spaces. Journal of Global Optimization, 1997, 11(4): 341-359.
[9] Zhang D B, Yang T R, Wen M, et al. Fish swarm algorithm based on differential evoution and its function optimization application. Computer Engineering, 2013, 39(5): 19-22.
[10] Yang Q, Cai L, Xue Y C. A survey of differential evolution algorithms. Pattern Recognition and Artificial Intelligence, 2008, 21(4): 506-513.
[11] Duan Y H, Gao Y L. A particle swarm optimization algorithm based on differential evolution. Computer Simulation, 2009, 26(6): 212-245.
[12] Yu Q, Zhao H. Neural network prediction model based on differential evolution algorithm and its application. Computer Engineering and Application, 2008, 44(14): 246-248.
[13] Lin C, Feng Q Y. New adaptive particle swarm optimization algorithm. Computer Engineering, 2008, 34(7): 181-183.
[14] Wang L G, Hong Y, Shi Q H. Global edition artificial fish swarm algorithm. Journal of System Simulation, 2009, 21(23): 7483-7486.
[15] Pang S, Yang X Y, Zhang X F. Chaotic mapping multi population quantum-behaved particleswarm optimization algorithm. Computer Engineering and Applications, 2012, 48(33): 56-62.
Supported by National Natural Science Foundation of China (70971052), Central China Normal University Scientific Research Projects (CCNU14Z02016), the Innovation Group Project of Hubei Province Natural Science Fund (2011CDA116)
PDF(445 KB)
/
| 〈 |
|
〉 |
