New Chaotic Simplified Particle Swarm Optimization Algorithm Based on Logistic Mapping

doi:10.3969/j.issn.1006-2475.2019.12.004

Abstract

Abstract: An new chaotic simplified particle swarm optimization algorithm based on logistic mapping (CIW-SPSO) is proposed to tackle the problems of basic PSO algorithm, such as easy to fall into local optimum, slow convergence, and low accuracy. The algorithm introduces chaos theory make inertia weight with chaotic search ability, and make learning factor changing with sine function optimization process, reduce the probability of algorithm falling into local optimum. Six classical test functions are used for simulation. The results show that the CIW-SPSO algorithm has faster convergence speed and higher accuracy, and can avoid local optimum and improve the algorithm optimization performance effectively.

Key words: chaos mapping, inertia weight, learning factor, simplified particle swarm algorithm

CLC Number:

TP301.6

YANG Wan-li1,2, ZHOU Xue-ting1, CHEN Meng-na1. New Chaotic Simplified Particle Swarm Optimization Algorithm Based on Logistic Mapping[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2019.12.004.

References

［1］EBERHART R C, KENNEDY J. A new optimizer using particle swarm theory［C］// Proceedings of the 6th International 〖JP4〗Symposium on Micro Machine and Human Science. 1995:39-43.
［2］EBERHART R C, SHI Y H. Particle swarm optimization: Developments, applications and resources［C］// Proceedings of the 2001 IEEE Congress on Evolutionary Computation. 2001:81-86.
［3］戴永彬,陈海涛. 一种混合引导的偏好多目标粒子群优化算法［J］. 控制工程, 2019,26(3):549-554.
［4］SUN C L, JIN Y C, CHENG R, et al. Surrogate-assisted cooperative swarm optimization of high-dimensional expensive problems［J］. IEEE Transactions on Evolutionary Computation, 2017,21(4):644-660.
［5］LIN Q Z, LIU S B, ZHU Q L, et al. Particle swarm optimization with a balanceable fitness estimation for many-objective optimization problems［J］. IEEE Transactions on Evolutionary Computation, 2018,22(1):32-46.
［6］PIZZUTI C. Evolutionary computation for community detection in networks: A review［J］. IEEE Transactions on Evolutionary Computation, 2018,22(3):464-483.
［7］JIANG M, HUANG Z Q, QIU L M, et al. Transfer learning-based dynamic multiobjective optimization algorithms［J］. IEEE Transactions on Evolutionary Computation, 2018,22(4):501-514.
［8］LIU Z Z, WANG Y, YANG S X, et al. An adaptive framework to tune the coordinate systems in nature-inspired optimization algorithms［J］. IEEE Transactions on Cybernetics, 2019,49(4):1403-1416.
［9］SHI Y H, EBERHART R C. A modified particle swarm optimizer［C］// Proceedings of the 1998 IEEE International Conference on Evolutionary Computation. 1998:69-73.
［10］SHI Y H, EBERHART R C. Empirical study of particle swarm optimization［C］// Proceedings of the 1999 IEEE Congress on Evolutionary Computation. 1999:1945-1950.
［11］SHI Y H, EBERHART R C. Fuzzy adaptive particle swarm optimization［C］// Proceedings of the 2001 IEEE Congress on Evolutionary Computation. 2001:101-106.
［12］EBERHART R C, SHI Y H. Tracking and optimizing dynamic systems with particle swarms［C］// Proceedings of the 2001 IEEE Congress on Evolutionary Computation. 2001:94-100.
［13］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.
［14］姜长元,赵曙光,沈士根,等. 惯性权重正弦调整的粒子群算法［J］. 计算机工程与应用, 2012,48(8):40-42.
［15］张顶学,关治洪,刘新芝. 一种动态改变惯性权重的自适应粒子群算法［J］. 控制与决策, 2008,23(11):1253-1257.
［16］周敏,李太勇. 粒子群优化算法中的惯性权值非线性调整策略［J］. 计算机工程, 2011,37(5):204-206.
［17］高立群,李若平,邹德旋. 全局粒子群优化算法［J］. 东北大学学报(自然科学版), 2011,32(11):1538-1541.
［18］任圆圆,刘培玉,薛素芝. 一种新的自适应动态文化粒子群优化算法［J］. 计算机应用研究, 2013,30(11):3240-3243.
［19］赵远东,方正华. 带有权重函数学习因子的粒子群算法［J］. 计算机应用, 2013,33(8):2265-2268.
［20］张治俊,罗辞勇,张帆,等. 采用振荡参数策略的粒子群优化算法［J］. 重庆大学学报, 2011,34(6):36-41.
［21］张春慨,李霄峰,邵惠鹤. 基于线性搜索的混沌优化及其在非线性约束优化问题中的应用［J］. 控制与决策, 2001,16(1):123-125.
［22］董丽凤,陈阳,巫光福. 动态学习混沌映射的粒子群算法［J］. 计算机应用研究, 2019,36(5):1319-1322.
［23］胡旺,李志蜀. 一种更简化而高效的粒子群优化算法［J］. 软件学报, 2007,18(4):861-868.
［24］雷敏,杨万里,彭晓波,等. 基于改进简化粒子群算法的含DG的配电网无功优化［J］. 华北电力大学学报(自然科学版), 2015,42(1):39-44.
［25］FENG Y, TENG G F, WANG A X, et al. Chaotic inertia weight in particle swarm optimization［C］// Proceedings of the 2nd International Conference on Innovative Computing, Information and Control. 2007:1899-1902.

[1]	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.
[2]	CAO Shuai-shuai， CHEN Xue-xin， MIAO Pu， BU Qing-kai. Image Segmentation Method Based on Optimization of PSO Algorithm #br# and K-means Clustering Algorithm [J]. Computer and Modernization, 2020, 0(01): 22-.
[3]	XU Sheng-bing1, XIA Wen-jie1, FENG Ji-qiang2. A Modified Particle Swarm Optimization Algorithm [J]. Computer and Modernization, 2015, 0(3): 5-8,14.
[4]	YE An-xin. Research on Traveling Salesman Problem Based onImproved Particle Swarm Optimization Algorithm [J]. Computer and Modernization, 2011, 1(4): 1-3.
[5]	LIU Jiao. Application of Improved Particle Swarm Optimization Algorithm in PID Controller Parameters Optimization for Main Steam Temperature System [J]. Computer and Modernization, 2009, 1(12): 29-32.