Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 114-116.

• Intelligent Computing • Previous Articles     Next Articles

New Swarm Intelligent Algorithms:Lions Algorithm

ZHANG Cong-ming, LIU Li-qun,MA Li-qun   

  1. College of Electronic Information Engineering,Taiyuan Science and Technology University,Taiyuan 030024,China
  • Online:2018-06-20 Published:2018-08-03

Abstract: As the optimization object becomes nonlinear,high dimensional and multi target,the ideal result can not be obtained by using the traditional optimization method.Intelligent algorithm is a good solution to the shortcomings of the traditional optimization methods.This paper proposed a new intelligent algorithm,called lions algorithm.Lions algorithm’s request on the initial value is not high.It has faster optimization speed and strong global convergence ability.In this paper,the principle of the lions algorithm was given,the convergence performance of the algorithm and the influence of the parameters on the convergence of the algorithm were analyzed,and it was compared with artificial bee colony algorithm.Finally,the algorithm was applied to the maximum power tracking of the PV,and the practical ability of the algorithm was verified by experiment and simulation.

Key words: Convergence, Lions algorithm, Maximum power tracking, Optimal value, Optimization

CLC Number: 

  • TP301.6
[1]KENNEDY J,EBERHART R.Particle swarm optimization[C]∥ Proceedings of the 1995 IEEE International Conference on Neural Networks.Piscataway,NJ,IEEE Press,1995:1942-1948.
[2]DORIGO M,GAMBARDELLA L M.Ant colony system:a cooperative learning approach to the traveling salesman problem[J].IEEE Transactions on Evolutionary Computation,1997,1(1):53-66.
[3]KARABOGA D.An idea based on honey bee swarm for numerical optimization[R].Technical Report TR06,Erciyes University,2005.
[4]葛宇,梁静,王学平,等.改进人工蜂群算法求解多目标连续优化问题[J].计算机科学,2014,41(6):254-259.
[5]YANG X S.Firefly algorithm,stochastic test functions and design Optimization[J].Bio-Inspired Computation,2010,2(2):78-84.
[6]程美英,倪志伟 朱旭辉.萤火虫优化算法理论研究综述[J].计算机科学,2015,42(4):19-24.
[7]PUNITHA K,DEVARAJ D,SAKTHIVEL S.Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions[J].Energy,2013(62):330-340.
[8]HIREN P,VIVEK A.Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded Conditions[J].IEEE Transactions on Industrial Electronics,2008,55(4):1689-1698.
[1] LU Chen-yang, DENG Su, MA Wu-bin, WU Ya-hui, ZHOU Hao-hao. Federated Learning Based on Stratified Sampling Optimization for Heterogeneous Clients [J]. Computer Science, 2022, 49(9): 183-193.
[2] WANG Can, LIU Yong-jian, XIE Qing, MA Yan-chun. Anchor Free Object Detection Algorithm Based on Soft Label and Sample Weight Optimization [J]. Computer Science, 2022, 49(8): 157-164.
[3] CHEN Jun, HE Qing, LI Shou-yu. Archimedes Optimization Algorithm Based on Adaptive Feedback Adjustment Factor [J]. Computer Science, 2022, 49(8): 237-246.
[4] LI Qi-ye, XING Hong-jie. KPCA Based Novelty Detection Method Using Maximum Correntropy Criterion [J]. Computer Science, 2022, 49(8): 267-272.
[5] WANG Bing, WU Hong-liang, NIU Xin-zheng. Robot Path Planning Based on Improved Potential Field Method [J]. Computer Science, 2022, 49(7): 196-203.
[6] TANG Feng, FENG Xiang, YU Hui-qun. Multi-task Cooperative Optimization Algorithm Based on Adaptive Knowledge Transfer andResource Allocation [J]. Computer Science, 2022, 49(7): 254-262.
[7] ZHAO Dong-mei, WU Ya-xing, ZHANG Hong-bin. Network Security Situation Prediction Based on IPSO-BiLSTM [J]. Computer Science, 2022, 49(7): 357-362.
[8] LI Ya-ru, ZHANG Yu-lai, WANG Jia-chen. Survey on Bayesian Optimization Methods for Hyper-parameter Tuning [J]. Computer Science, 2022, 49(6A): 86-92.
[9] HUANG Guo-xing, YANG Ze-ming, LU Wei-dang, PENG Hong, WANG Jing-wen. Solve Data Envelopment Analysis Problems with Particle Filter [J]. Computer Science, 2022, 49(6A): 159-164.
[10] LU Chen-yang, DENG Su, MA Wu-bin, WU Ya-hui, ZHOU Hao-hao. Clustered Federated Learning Methods Based on DBSCAN Clustering [J]. Computer Science, 2022, 49(6A): 232-237.
[11] LU Hao-song, HU Yong-hua, WANG Shu-ying, ZHOU Xin-lian, LI Hui-xiang. Study on Hybrid Resource Heuristic Loop Unrolling Factor Selection Method Based on Vector DSP [J]. Computer Science, 2022, 49(6A): 777-783.
[12] CHEN Jun-wu, YU Hua-shan. Strategies for Improving Δ-stepping Algorithm on Scale-free Graphs [J]. Computer Science, 2022, 49(6A): 594-600.
[13] LIU Zhang-hui, ZHENG Hong-qiang, ZHANG Jian-shan, CHEN Zhe-yi. Computation Offloading and Deployment Optimization in Multi-UAV-Enabled Mobile Edge Computing Systems [J]. Computer Science, 2022, 49(6A): 619-627.
[14] FAN Xing-ze, YU Mei. Coverage Optimization of WSN Based on Improved Grey Wolf Optimizer [J]. Computer Science, 2022, 49(6A): 628-631.
[15] ZHU Xu-hui, SHEN Guo-jiao, XIA Ping-fan, NI Zhi-wei. Model Based on Spirally Evolution Glowworm Swarm Optimization and Back Propagation Neural Network and Its Application in PPP Financing Risk Prediction [J]. Computer Science, 2022, 49(6A): 667-674.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!