Cooperative Particle Swarm Optimization Algorithm Based on Huffman Coding

doi:10.3969/j.issn.1006-2475.2015.06.018

Abstract

Abstract: Aiming at particle swarm optimization (PSO) algorithm easy to fall into local optimal problems in optimizing a population, a new particle swarm optimization on Huffman Coding (HCPSO) algorithm was put forward. Using Huffman Coding, one population will be divided into two sub populations, and each subpopulation will be optimized independently. At the same time, the two subpopulations cooperatively complete searching the global optimum solution.Through six standard test functions, the experimental results show that the algorithm can effectively avoid the population falling into local optimum, is of better optimization performance and stability, and convergence accuracy is significantly improved.

Key words: particle swarm optimization, local optimal, Huffman coding, Huffman algorithm, Huffman tree

CLC Number:

TP202.7

WANG Juan-juan. Cooperative Particle Swarm Optimization Algorithm Based on Huffman Coding[J]. Computer and Modernization, 2015, 0(6): 82-85.

References

［1］ Kennedy J, Eberhartr C. Particle swarm optimization［C］// Proceeding of IEEE International Conference on Neural Networks. Perth: IEEE Piscataway, 1995:1942-1948.
［2］ Parsopoulos K E, Vrahatis M N. Recent approaches to global optimization problems through particle swarm optimization［J］. Natural Computing, 2002,1(2-3):235-306.
［3］ Poli R, Kennedy J, Blackwell T. Particle swarm optimization an overview［J］. Swarm Intelligence, 2007,1:33-57.
［4］ Shi Y, Eberhart R. A modified particle swarm optimizer［C］// Proceedings of International Conference on Evolutionary Computation. 1998:69-73.
［5］胡广浩,毛志忠,何大阔. 基于两阶段领导的多目标粒子群优化算法［J］. 控制与决策, 2010,25(3):404-410.
［6］ Potter M A, De Jong K A. A cooperative coevolutionary approach to function optimization［C］// Proceedings of the 3rd International Conference on Parallel Problem Solving from Nature. 1994,886:249-257.
［7］ Bergh F V D, Engelbrecht A. A cooperative approach to particle swarm optimization［J］. IEEE Transactions on Evolutionary Computation, 2004,8(3):225-239.
［8］ Shi Y, Krohling R A. Coevolutionary particle swarm optimization to solve minmax problems［C］// Proceedings of the 2002 Congress on Evolutionary Computation. 2002,2:1682-1687.
［9］陶新民,徐晶,杨立标,等. 改进的多种群协同进化微粒群优化算法［J］. 控制与决策, 2009,24(9):1406-1411.
［10］刘衍民,赵庆祯,隋常玲,等. 一种基于动态邻居和变异因子的粒子群算法［J］. 控制与决策, 2010,25(7):968-974.
［11］Huffman D A. A method for the construction of minimum redundancy codes［J］. Proceedings of the IRE, 1952,40(9):1098-1101.
［12］严蔚敏,马伟民. 数据结构［M］. 北京:清华大学出版社, 1992.

[1]	MAO Ming-yang, XU Sheng-chao. Continuous Attribute Discretization Algorithm of Rough Sets for BP Neural Networks Based on Particle Swarm Optimization#br# [J]. Computer and Modernization, 2023, 0(09): 115-119.
[2]	HAN Xue. Multi Path Planning Based on Constrained Clustering and Particle Swarm Optimization [J]. Computer and Modernization, 2023, 0(08): 7-11.
[3]	PAN Si-yuan, ZHANG Wei. SOC Estimation of Lithium Battery Based on Improved LSTM [J]. Computer and Modernization, 2023, 0(08): 25-30.
[4]	LIU Fu-qi, ZHANG Da. PSO-DBN-based Hydraulic System Cooler Fault Diagnosis [J]. Computer and Modernization, 2023, 0(06): 7-14.
[5]	PAN Yu-qing, ZHANG Su-ning, FENG Ren-jun, JING Dong-sheng. Intrusion Detection Method Based on Particle Swarm Optimization Combined with LightGBM [J]. Computer and Modernization, 2023, 0(04): 123-126.
[6]	ZENG Yi-pu, DAI Yi-ru, CHEN Yu-tian. Dynamic Particle Swarm Optimization without Velocity Based on Opposition-based Learning and Elite Promotion [J]. Computer and Modernization, 2023, 0(03): 113-120.
[7]	HUANG Ling, BAI Xiao-bo. Teachers’ Scientific Research Performance Prediction Based on PSO-KPLS [J]. Computer and Modernization, 2022, 0(06): 8-12.
[8]	LIU Zhi-feng, SHU Zhi-hao, XU Yue-feng, YANG Shu-yi, SHEN Wen-long. Artificial Fish Swarm Algorithm Based on PSO Adaptive Dual Strategy [J]. Computer and Modernization, 2022, 0(05): 46-53.
[9]	ZHANG Yan-hu, YAN Li-juan, MA Zhi-fen, ZHANG Yan-jun. An improved Particle Swarm Optimization (PSO) Force Unloading Algorithm for Multi-task and Multi-resource Moving Edge Computing Environment [J]. Computer and Modernization, 2022, 0(05): 54-60.
[10]	LI Ling, CHEN Xi, SHEN Wei-jie, XIONG Han-wu, CAI Ran-ran. An Edge Caching Strategy for Intelligent Manufacturing Supervision of Electrical Equipment [J]. Computer and Modernization, 2022, 0(05): 61-67.
[11]	LIANG Zheng-you , , WANG Lu , LI Xuan-ang , YANG Feng , . A Point Cloud Registration Algorithm Combining Improved PSO Algorithm and TrICP Algorithm [J]. Computer and Modernization, 2022, 0(05): 90-95.
[12]	WANG Zhe, WANG Long-da, LIU GANG, WANG Xing-cheng, WANG Zhong-jun, BAO Lu-jie. Improved Immune Particle Swarm Optimization Algorithm for Automatic Parallel Parking Based on Cubic Spline Interpolation#br# [J]. Computer and Modernization, 2022, 0(02): 7-12.
[13]	GUO Cheng, ZHANG Wan-da, WANG Bo, WANG Jia-fu. Particle Swarm Optimization Algorithm Based on Multigroup Parallel Cooperation [J]. Computer and Modernization, 2022, 0(01): 33-40.
[14]	CHEN Xiang, ZOU Qing-nian, XIE Shao-yu, CHEN Cui-qiong. Identification of Platen Switch State Based on Transfer Learning Strategy [J]. Computer and Modernization, 2021, 0(05): 120-126.
[15]	YI Wen-zhou. Improved WSN Coverage Algorithms Based on Differential Evolution #br# and Particle Swarm Optimization [J]. Computer and Modernization, 2019, 0(08): 33-.