A Modified Particle Swarm Optimization Algorithm

doi:10.3969/j.issn.1006-2475.2015.03.002

Abstract

Abstract: In order to overcome the disadvantages of particle swarm optimization algorithm such as premature, bad searching capability, a Modified Particle Swarm Optimization (MPSO) algorithm was proposed. In this algorithm, when particles fall into the local extreme area, the particles, which gathered at the global optimal particle of the swarm, would have a mutation. Then test results of six complex benchmark functions and computer color matching model indicate that MPSO is superior to other two classic PSOs, which has high convergence precision, fast convergence speed and prevents particle premature effectively.

Key words: particle swarm optimization, inertia weight, global exploration； premature, mutation

CLC Number:

TP301

XU Sheng-bing1, XIA Wen-jie1, FENG Ji-qiang2. A Modified Particle Swarm Optimization Algorithm[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.03.002.

References

［1]Kennedy J, Eberhart R C. Parcticle swarm optimization［C]// Proceedings of IEEE International Conference on Neural Networks. Perth, 1995:1942-1948.
［2]Shi Y, Eberhart R C．A modified particle swarm optimizer［C]// Proceedings of the 1998 IEEE International Conference on Evolutionary Computation. Alaska, USA, 1998:69-73．
［3]Shi Y, Eberhart R. Fuzzy adaptive particle swarm optimization［C]// Proceedings of the 2001 Congress on Evolutionary Computation. Seoul, 2001:101-106.
［4]Eberhart R, Shi Y. Tracking and optimizing dynamic systems with particle swarm ［C]// Proceedings of the 2001 Congress on Evolutionary Computation.Seoul, 2001:94-100.
［5]Iwasaki N, Yasuda K, Ueno G． Dynamic parameter tuning of particle swarm optimization［J]． IEEE Transactions on Electrical and Electronic Engineering, 2006,1(4):353-363．
［6]陈贵敏,贾建援,韩琪．粒子群优化算法的惯性权值递减策略研究［J]．西安交通大学学报, 2006,40(1):53-56．
［7]冯婷,陆雪松,阳维,等．改进收敛条件的动态调整惯性权重PSO算法［J]．计算机工程与应用, 2009,45(3):175-177．
［8]Yasuda K, Iwasaki N． Adaptive particle swarm optimization using velocity information of swarm［C]// IEEE International Conference on Systems, Man and Cybernetics． 2004,4:3475-3481．
［9]张艳琼．改进的云自适应粒子群优化算法［J]．计算机应用研究， 2010,27(9):3250-3252.
［10]Angeline P J. Using selection to improve particle swarm optimization［C]// Proceedings of the 1998 IEEE International Conference on Evolutionary Computation. Alaska, 1998:84-89.
［11]Ratnaweera A, Halgamuge S K, Watson H C. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients［C]// IEEE Transactions on Evolutionary Computation. 2004:240-255.
［12]颜俊华,张敏,王永军．基于遗传算法的智能粒子群优化算法［J]. 西南大学学报(自然科学版), 2010,32(11):135-139.
［13]胡建,李志蜀,欧鹏,等. 粒子群优化算法中的位置矢量的评价策略［J]. 四川大学学报(工程科学版), 2009,41(1):139-145.
［14]李戎,潘玮,顾峰,等. 计算机测配色的历史与现状［J]. 北京纺织, 1999,20(2):59-61.
［15]李效周. 计算机配色系统在油墨配色上的应用［J]. 今日印刷, 2006(5):72-73.
［16]王喜昌,周丰昆,龙科慧. 三刺激值配色和全光谱配色的比较［J]. 光学精密工程, 1999,7(2):13-16.
［17]董振礼,郑宝海,轷桂芬,等. 测色与计算机配色［M]. 2版. 北京:中国纺织出版社, 2007.
［18]刘武辉. 印刷色彩管理［M]. 北京:化学工业出版社, 2011.

[1]	XIA Huang-zhi, CHEN Li-min, MAO Xue-di, . Adaptive Bald Eagle Search Algorithm with Dynamic Learning and Gaussian Mutation [J]. Computer and Modernization, 2024, 0(01): 117-126.
[2]	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.
[3]	HAN Xue. Multi Path Planning Based on Constrained Clustering and Particle Swarm Optimization [J]. Computer and Modernization, 2023, 0(08): 7-11.
[4]	PAN Si-yuan, ZHANG Wei. SOC Estimation of Lithium Battery Based on Improved LSTM [J]. Computer and Modernization, 2023, 0(08): 25-30.
[5]	LIU Fu-qi, ZHANG Da. PSO-DBN-based Hydraulic System Cooler Fault Diagnosis [J]. Computer and Modernization, 2023, 0(06): 7-14.
[6]	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.
[7]	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.
[8]	HUANG Ling, BAI Xiao-bo. Teachers’ Scientific Research Performance Prediction Based on PSO-KPLS [J]. Computer and Modernization, 2022, 0(06): 8-12.
[9]	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.
[10]	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.
[11]	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.
[12]	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.
[13]	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.
[14]	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.
[15]	LI Hong-wei. Multi-objective Shark Smell Optimization Algorithm Based on Decomposition and Vector [J]. Computer and Modernization, 2021, 0(09): 43-50.