Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (2): 352-365.doi: 10.21629/JSEE.2019.02.14

• Systems Engineering • Previous Articles     Next Articles

Antlion optimizer algorithm based on chaos search and its application

Zhenxing ZHANG1(), Rennong YANG1(), Huanyu LI1,*(), Yuhuan FANG2(), Zhenyu HUANG1(), Ying ZHANG1()   

  1. 1 Air Traffic Control and Navigation College, Air Force Engineering University, Xi'an 710038, China
    2 Unit 95939 of PLA, Cangzhou 061736, China
  • Received:2017-12-04 Online:2019-04-01 Published:2019-04-28
  • Contact: Huanyu LI E-mail:2207621676@qq.com;786918169@qq.com;lihuanyu1984@163.com;846655874@qq.com;65486346@qq.com;zhangying198807@126.com
  • About author:ZHANG Zhenxing was born in 1993. He received his M.S. degree from Airforce Engineering University. He is a Ph.D. candidate in Airforce Engineering University. His research interests are artificial intelligence and deep learning algorithm. E-mail:2207621676@qq.com|YANG Rennong was born in 1969. He received his M.S. degree from Airforce Engineering University. He is a professor in Airforce Engineering University. His research interests are artificial intelligence and deep learning algorithm. E-mail:786918169@qq.com|LI Huanyu was born in 1984. He received his Ph.D. degree from Airforce Engineering University. He is a lecturer in Airforce Engineering University. His research interests are artificial intelligence and deep learning algorithm. E-mail:lihuanyu1984@163.com|FANG Yuhuan was born in 1994. He received her M.S. degree from Airforce Engineering University. She is an assistant engineer in Unit 95939 of PLA. Her research interests are artificial intelligence and deep learning algorithm. E-mail:846655874@qq.com|HUANG Zhenyu was born in 1989. He received his M.S. degree from Airforce Engineering University. He is a lecturer in Airforce Engineering University. His research interests are artificial intelligence and deep learning algorithm. E-mail:65486346@qq.com|ZHANG Ying was born in 1988. He received her Ph.D. degree from Airforce Engineering University. She is a lecturer in Airforce Engineering University. Her research interests are artificial intelligence and deep learning algorithm. E-mail:zhangying198807@126.com
  • Supported by:
    the National Natural Science Foundation of China(61503409);the Aeronautical Science Foundation of China(20155196022);the Natural Science Foundation of Shanxi Province(2016JQ6050);This work was supported by the National Natural Science Foundation of China (61503409), the Aeronautical Science Foundation of China (20155196022) and the Natural Science Foundation of Shanxi Province (2016JQ6050)

Abstract:

Aiming at the problems of premature convergence and easily falling into local optimums of the antlion optimization algorithm, a chaos antlion optimization algorithm based on the chaos search is proposed. Firstly, in the algorithm, the population is initialized by using the tent chaotic mapping, and the self-adaptive dynamic adjustment of chaotic search scopes is proposed in order to improve the overall fitness and the optimization efficiency of the population. Then, the tournament strategy is used to select antlions. Finally, the logistic chaos operator is used to optimize the random walk of ants, which forms a global and local parallel search mode with the antlionos foraging behavior. The performance algorithm is tested through 13 complex high-dimensional benchmark functions and three dimensional path planning problems. The experimental results of six complex high-dimensional benchmark functions show that the presented algorithm has a better convergence speed and precision than the standard antlion algorithm and other optimization algorithms, and is suitable for the optimization of complex high dimensional functions. The trajectory planning experimental results show that compared with the antlion optimizer (ALO) algorithm, grey wolf optimizer (GWO), particle swarm optimization (PSO) and artificial bee colony (ABC) algorithm, it has advantages in speed and accuracy to obtain a specific path, and it is of great value in actual problems.

Key words: antlion optimizer (ALO) algorithm, chaos theory, tent mapping, tournament selection strategy, logistic mapping, path planning