[1]JING Kunlei,ZHAO Xiaoguo,ZHANG Xinyu,et al.Ant lion optimizer with levy variation and adaptive elite competition mechanism[J].CAAI Transactions on Intelligent Systems,2018,13(2):236-242.[doi:10.11992/tis.201706091]
Copy

Ant lion optimizer with levy variation and adaptive elite competition mechanism

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 2 Page number: 236-242 Column: 学术论文—智能系统 Public date: 2018-04-15
Title:
Ant lion optimizer with levy variation and adaptive elite competition mechanism
Author(s):
JING Kunlei1; 2;  ZHAO Xiaoguo1; 2; 3;  ZHANG Xinyu1; 2;  LIU Ding1; 2
1. National & Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration, Xi’an University of Technology, Xi’an 710048, China;
2. Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, X
Keywords:
ant lion optimizer; Levy variation; adaptive elite competition; convergence speed; parameters identification
CLC:
TP18
DOI:
10.11992/tis.201706091
Abstract:
The ant lion optimizer (ALO) reveals such deficiencies as easily relapsing into local optimum and low convergence speed. This paper proposed an improved ALO algorithm with Levy variation and adaptive elite competition mechanism. By carrying out Levy variation to poor individuals, the diversity of population and the global search ability of the algorithm can be increased. Moreover, the adaptive elite competition mechanism that many elites lead the population to search at the same time can improve the convergence speed of the algorithm. To reduce the amount of calculation, the number of the elites competing in parallel will decrease with the increase of iterations. By contrast with other improved optimization algorithms, the test results show that the improved algorithm proposed in this paper has better search precision and convergence speed. Finally, this improved algorithm is applied to identify parameters of silicon single crystal thermal field temperature model and the simulation results prove its excellent ability of parameters identification.
References:
[1] MIRJALILI S. The ant lion optimizer[J]. Advances in engineering software, 2015, 83: 80-98.
[2] SAXENA P, KOTHARI A. Ant lion optimization algorithm to control side lobe level and null depths in linear antenna arrays[J]. AEU-International journal of electronics and communications, 2016, 70(9): 1339-1349.
[3] HADIDIAN-MOGHADDAM M J, ARABI-NOWDEH S, BIGDELI M, et al. A multi-objective optimal Sizing and siting of distributed generation using ant lion optimization technique[J]. Ain shams engineering journal, 2017, doi: 10.1016/j.asej.2017.03.001.
[4] RAJU M, SAIKIA L C, SINHA N. Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller[J]. International journal of electrical power and energy systems, 2016, 80: 52-63.
[5] RAJAN A, JEEVAN K, MALAKAR T. Weighted elitism based ant lion optimizer to solve optimum VAr planning problem[J]. Applied soft computing, 2017, 55: 352-370.
[6] 赵世杰, 高雷阜, 于冬梅, 等. 带混沌侦查机制的蚁狮优化算法优化SVM参数[J]. 计算机科学与探索, 2016, 10(5): 722-731.
ZHAO Shijie, GAO Leifu, YU Dongmei, et al. Ant lion optimizer with chaotic investigation mechanism for optimizing SVM parameters[J]. Journal of frontiers of computer science and technology, 2016, 10(5): 722-731.
[7] REYNOLDS A. Liberating Lévy walk research from the shackles of optimal foraging[J]. Physics of life reviews, 2015, 14: 59-83.
[8] 何莉, 王淼, 李博. 面向单目标优化的集成粒子群算法[J]. 重庆邮电大学学报: 自然科学版, 2017, 29(4): 527-534.
HE Li, WANG Miao, LI Bo. Ensemble particle swarm optimizer for single objective optimization[J]. Journal of Chongqing university of posts and telecommunications: natural science edition, 2017, 29(4): 527-534.
[9] REYNOLDS A M. Cooperative random Lévy flight searches and the flight patterns of honeybees[J]. Physics letters A, 2006, 354(5/6): 384-388.
[10] 朱颢东, 孙振, 吴迪, 等. 基于改进蚁群算法的移动机器人路径规划[J]. 重庆邮电大学学报: 自然科学版, 2016, 28(6): 849-855.
ZHU Haodong, SUN Zhen, WU Di, et al. Path planning for mobile robot based on improved ant colony algorithm[J]. Journal of Chongqing university of posts and telecommunications: natural science edition, 2016, 28(6): 849-855.
[11] LEE C Y, YAO Xin. Evolutionary programming using mutations based on the levy probability distribution[J]. IEEE transactions on evolutionary computation, 2004, 8(1): 1-13.
[12] MANTEGNA R N. Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes[J]. Physical review E, 1994, 49(5): 4677-4683.
[13] JENSI R, JIJI G W. An enhanced particle swarm optimization with levy flight for global optimization[J]. Applied soft computing, 2016, 43: 248-261.
[14] 江建. 精英自适应混合遗传算法及其实现[J]. 计算机工程与应用, 2009, 45(27): 34-35, 101.
JIANG Jian. Elite adaptive hybrid genetic algorithm and its realization[J]. Computer engineering and applications, 2009, 45(27): 34-35, 101.
[15] 周新宇, 吴志健, 王晖, 等. 一种精英反向学习的粒子群优化算法[J]. 电子学报, 2013, 41(8): 1647-1652.
ZHOU Xinyu, WU Zhijian, WANG Hui, et al. Elite opposition-based particle swarm optimization[J]. Acta electronica sinica, 2013, 41(8): 1647-1652.
[16] 刘丁. 直拉硅单晶生长过程建模与控制[M]. 北京: 科学出版社, 2015: 1-3, 46-57.
LIU Ding. Modeling and controlling of crystal growth in the Czochralski process[M]. Beijing: Science Press, 2015: 1-3, 46-57.
[17] 刘胜, 宋佳, 李高云. PSO并行优化LSSVR非线性黑箱模型辨识[J]. 智能系统学报, 2013, 5(1): 51-56.
LIU Sheng, SONG Jia, LI Gaoyun. Modeling a complex nonlinear system with particle swarm optimization and parallel-optimized least squares support vector regression[J]. CAAI transactions on intelligent systems, 2013, 5(1): 51-56.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems