|
|
|
| Preferred Strategy Based Self-adaptive Ant Lion Optimization Algorithm |
| LIU Jingsen1,2, HUO Yu3, LI Yu4 |
1. Institute of Intelligence Networks System, Henan University, Kaifeng 475004;
2. School of Software, Henan University, Kaifeng 475004;
3. School of Computer and Information Engineering, Henan University, Kaifeng 475004;
4. Institute of Management Science and Engineering, Henan University, Kaifeng 475004 |
|
|
|
|
Abstract Ant lion optimization (ALO) algorithm produces low convergence speed and accuracy in high dimensional solution and it is inclined to fall into local extremum. Therefore, a preferred strategy based self-adaptive ant lion optimization algorithm (PSALO) is proposed. The adaptive boundary mechanism is introduced into the process of ant walking around the ant lion to increase the activity of ant population and prevent the algorithm from falling into the local extremum. The optimal roulette strategy is added in the ant lion selection by roulette to maintain the diversity of ant lion individuals and accelerate the convergence speed of the algorithm. The dynamic proportional factor is added into the ant position update formula to improve the exploration ability of the algorithm in the early stage and the development ability in the later stage. Theoretical analysis proves that the time complexity of the proposed algorithm is same as that of ALO. Optimized simulation experiment of 16 standard test functions with different features in multiple dimensions indicates good feasibility of the proposed algorithm. The optimization precision and convergence speed are improved significantly and they are less affected by the dimension variation. The ability in high dimensional solution is better and more stable.
|
|
Received: 05 November 2019
|
|
|
| Fund:Supported by National Natural Science Foundation of China(No.71601071), Special R&D and Promotion Programs in Henan Province(No.182102310886), Graduate Education Innovation and Quality Improvement Project of Henan University(No.SYL18060145,SYL19050104) |
|
Corresponding Authors:
LI Yu, Ph.D., professor. Her research interests include inte-lligence algorithm and electronic commerce.
|
| About author:: LIU Jingsen, Ph.D., professor. His research interests include intelligence algorithm, optimization control and network information security; HUO Yu, master student. His research interests include intelligence algorithm. |
|
|
|
[1] KENNEDY J, EBERHART R C. Particle Swarm Optimization // Proc of the IEEE International Conference on Neural Networks. Washington, USA: IEEE, 1995: 1942-1948.
[2] SHI Y, EBERHART R C. A Modified Particle Swarm Optimizer // Proc of the IEEE International Conference on Evolutionary Computation. Washington, USA: IEEE, 1998: 69-73.
[3] YANG X S. A New Metaheuristic Bat-Inspired Algorithm // GONZLEZ J R, PELTA D A, CRUZ C, et al., eds. Nature Inspired Cooperative Strategies for Optimization. Berlin, Germany: Springer, 2010: 65-74.
[4] MIRJALILI S, GANDOMI A H, MIRJALILI S Z, et al. Salp Swarm Algorithm: A Bio-inspired Optimizer for Engineering Design Pro-blems. Advances in Engineering Software, 2017, 114: 163-191.
[5] MIRJALILI S. The Ant Lion Optimizer. Advances in Engineering Software, 2015, 83: 80-98.
[6] TALATAHARI S. Optimum Design of Skeletal Structures Using Ant Lion Optimizer. International Journal of Optimization in Civil Engineering, 2016, 6(1): 13-25.
[7] MEI R G S, SULAIMAN M H, MUSTAFFA Z. Ant Lion Optimizer for Optimal Reactive Power Dispatch Solution. Journal of Electrical Systems, 2015(3): 67-74.
[8] YAO P, WANG H L. Dynamic Adaptive Ant Lion Optimizer Applied to Route Planning for Unmanned Aerial Vehicle. Soft Computing, 2017, 21: 5475-5488.
[9] 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.计算机科学与探索, 2016, 10(5): 722-731.
(ZHAO S J, GAO L F, YU D M, et al. Ant Lion Optimizer with Chaotic Investigation Mechanism for Optimizing SVM Parameters. Journal of Frontiers of Computer Science and Technology, 2016, 10(5): 722-731.)
[10] GERGEL V, GRISHAGIN V, GERGEL A. Adaptive Nested Optimization Scheme for Multidimensional Global Search. Journal of Global Optimization, 2016, 66: 35-51.
[11] EMARY E, ZAWBAA H M, HASSANIEN A E. Binary Ant Lion Approaches for Feature Selection. Neurocomputing, 2016, 213: 54-56.
[12] 张振兴,杨任农,房育寰,等.自适应Tent混沌搜索的蚁狮优化算法.哈尔滨工业大学学报, 2018, 50(5): 152-159.
(ZHANG Z X, YANG R N, FANG Y H, et al. Ant Lion Optimization Algorithm Based on Self-adaptive Tent Chaos Search. Journal of Harbin Institute of Technology, 2018, 50(5): 152-159.)
[13] 景坤雷,赵小国,张新雨,等.具有Lévy变异和精英自适应竞争机制的蚁狮优化算法.智能系统学报, 2018, 13(2): 236-242.
(JING K L, ZHAO X G, ZHANG X Y, et al. Ant Lion Optimizer with Lévy Variation and Adaptive Elite Competition Mechanism. CAAI Transactions on Intelligent Systems, 2018, 13(2): 236-242.)
[14] 徐钦帅,何 庆,魏康园.改进蚁狮算法的无线传感器网络覆盖优化.传感技术学报, 2019, 32(2): 266-275.
(XU Q S, HE Q, WEI K Y. Modified Ant Lion Optimizer Based Coverage Optimization of Wireless Sensor Network. Chinese Journal of Sensors and Actuators, 2019, 32(2): 266-275.)
[15] KIIIC H, YUZGEC U. Improved Antlion Optimization Algorithm via Tournament Selection and Its Application to Parallel Machine Scheduling. Computers and Industrial Engineering, 2019, 132: 166-186.
[16] EMARY E, ZAWBAA H M. Feature Selection via Lévy Antlion Optimization. Pattern Analysis and Applications, 2019, 22: 857-876.
[17] RAJAN A, JEEVAN K, MALAKAR T. Weighted Elitism Based Ant Lion Optimizer to Solve Optimum VAr Planning Problem. Applied Soft Computing, 2017, 55: 352-370.
[18] 黄长强,赵克新.基于改进蚁狮算法的无人机三维航迹规划.电子信息学报, 2018, 40(7): 1532-1538.
(HUANG C Q, ZHAO K X. Three Dimensional Path Planning of UAV with Improved Ant Lion Optimizer. Journal of Electronics and Information Technology, 2018, 40(7): 1532-1538.)
[19] 张永韡,汪 镭,吴启迪.动态适应布谷鸟搜索算法.控制与决策, 2014, 29(4): 617-622.
(ZHANG Y W, WANG L, WU Q D. Dynamic Adaptation Cuckoo Search Algorithm. Control and Decision, 2014, 29(4): 617-622.)
[20] 周 欢,李 煜.具有动态惯性权重的布谷鸟搜索算法.智能系统学报, 2015, 10(4): 645-651.
(ZHOU H, LI Y. Cuckoo Search Algorithm with Dynamic Inertia Weight. CAAI Transactions on Intelligent Systems, 2015, 10(4): 645- 651.)
[21] 王秋梦,王 艳,纪志成.基于改进樽海鞘群算法的PMSM多参数辨识.系统仿真学报, 2018, 30(11): 4284-4291, 4297.
(WANG Q M, WANG Y, JI Z C. Permanent Magnet Synchronous Motor Multi-parameter Identification Based on Improved Salp Swarm Algorithm. Journal of System Simulation, 2018, 30(11): 4284-4291, 4297.)
[22] GHASEMI M, AKBARI E, RAHIMNEJAD A, et al. Phasor Particle Swarm Optimization: A Simple and Efficient Variant of PSO. Soft Computing, 2019, 23(19): 9701-9718. |
|
|
|
2020, Vol. 33 
