Particle swarm optimization algorithm based on entropy model

被引：1
|
作者：
Sun Q. [1 ]
Gao L. [2 ]
Liu T. [3 ]
Yao J. [3 ]
Wang H. [1 ]
机构：
[1] School of Science Information and Technology, Northwest University, Xi'an
[2] College of Computer Science, Xi'an Polytechnic University, Xi'an
[3] College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an
来源：
Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2019年 / 49卷 / 06期
关键词：
Convergence speed; Information entropy model; Invalid iteration; Particle swarm optimization algorithm; Solution accuracy;
D O I：
10.3969/j.issn.1001-0505.2019.06.010
中图分类号：
学科分类号：
摘要：
To solve the problem that the particle swarm optimization (PSO)algorithm is prone to premature convergence and have a large number of invalid iterations when solving high-dimensional optimization problems, a particle swarm optimization algorithm based on the entropy model (EPSO) is proposed. The information entropy model is introduced to analyze the aggregation characteristics precisely in the process of particle swarm search, and the particle swarm search process is divided into three stages for optimization. In the first stage, the difference of the particle iterative entropy is used to adjust the inertia weight. In the second stage, according to the change of the particle swarm entropy, the inertia weight is reset. In the third stage, the invalid iteration of particle swarm is reduced by truncation strategy. The experimental results show that in five standard test functions such as Sphere, Rosenbrock, Ackley, Griewank, Rastrigin,the solution accuracy and the convergence speed of the EPSO algorithm are higher than those of the traditional PSO algorithm, the classical PSO algorithm, the adaptive inertial weight PSO algorithm and the new adaptive inertia weight PSO algorithm. It also reduces a large number of invalid iterations. The effectiveness of the EPSO algorithm is proved. © 2019, Editorial Department of Journal of Southeast University. All right reserved.
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页码：1088 / 1093
页数：5
相关论文
共 15 条
  • [1] Eberhart R., Kennedy J., A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39-43, (1995)
  • [2] Shi Y., Eberhart R., A modified particle swarm optimizer, 1998 IEEE International Conference on Evolutionary Computation Proceedings, pp. 69-73, (1998)
  • [3] Nickabadi A., Ebadzadeh M.M., Safabakhsh R., A novel particle swarm optimization algorithm with adaptive inertia weight, Applied Soft Computing, 11, 4, pp. 3658-3670, (2011)
  • [4] Li X.J., Xu J., Zhu E.Z., Et al., A novel computation method for adaptive inertia weight of task scheduling algorithm, Journal of Computer Research and Development, 53, 9, pp. 1990-1999, (2016)
  • [5] Taherkhani M., Safabakhsh R., A novel stability-based adaptive inertia weight for particle swarm optimization, Applied Soft Computing, 38, pp. 281-295, (2016)
  • [6] Tian D.P., Shi Z.Z., MPSO: Modified particle swarm optimization and its applications, Swarm and Evolutionary Computation, 41, pp. 49-68, (2018)
  • [7] Dong H.B., Li D.J., Zhang X.P., Particle swarm optimization algorithm with dynamically adjusting inertia weight, Computer Science, 45, 2, (2018)
  • [8] Tang K.Z., Feng J.W., Li F., Et al., Multi-strategy adaptive particle swarm optimization algorithm, Journal of Nanjing University of Science and Technology, 41, 3, (2017)
  • [9] Liang J., Qin A., Suganthan P.N., Et al., Comprehensive learning particle swarm optimizer for global optimisation of multimodal functions, IEEE Transactions on Evolutionary Computation, 10, 3, pp. 281-295, (2006)
  • [10] Niu B., Huang H.L., Tan L.J., Et al., Symbiosis-based alternative learning multi-swarm particle swarm optimization, ACM Transactions on Computational Biology and Bioinformatics, 14, 1, pp. 4-14, (2017)
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