Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight

被引：0
|
作者：
Dong W.-Y. [1 ]
Kang L.-L. [1 ,2 ]
Liu Y.-H. [1 ]
Li K.-S. [3 ]
机构：
[1] Computer School, Wuhan University, Wuhan
[2] Faculty of Applied Science, Jiangxi University of Science and Technology, Ganzhou
[3] College of Information, South China Agricultural University, Guangzhou
来源：
Tongxin Xuebao/Journal on Communications | 2016年 / 37卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Adaptive elite mutation; Generalized opposition-based learning; Nonlinear inertia weight; Particle swarm optimization;
D O I：
10.11959/j.issn.1000-436x.2016224
中图分类号：
学科分类号：
摘要：
An opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight (OPSO-AEM&NIW) was proposed to overcome the drawbacks, such as falling into local optimization, slow convergence speed of opposition-based particle swarm optimization. Two strategies were introduced to balance the contradiction between exploration and exploitation during its iterations process. The first one was nonlinear adaptive inertia weight (NIW), which aim to accelerate the process of convergence of the algorithm by adjusting the active degree of each particle using relative information such as particle fitness proportion. The second one was adaptive elite mutation strategy (AEM), which aim to avoid algorithm trap into local optimum by trigging particle's activity. Experimental results show OPSO-AEM&NIW algorithm has stronger competitive ability compared with opposition-based particle swarm optimizations and its varieties in both calculation accuracy and computation cost. © 2016, Editorial Board of Journal on Communications. All right reserved.
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页码：1 / 10
页数：9
相关论文
共 19 条
  • [1] Kennedy J., Eberhart R.C., Particle swarm optimization, IEEE International Conference on Neural Networks, pp. 1942-1948, (1995)
  • [2] Shi X.B., Zhang Y., Zhao S., Et al., Discrete multi-objective optimization of particle swarm optimizer algorithm for multi-agents collaborative planning, Journal on Communications, 37, 6, pp. 29-37, (2016)
  • [3] Inbarani H.H., Azar A.T., Jothi G., Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis, Computer Methods and Programs in Biomedicine, 113, 1, pp. 175-185, (2014)
  • [4] Zad B.B., Hasanvand H., Lobry J., Et al., Optimal reactive power control of DGs for voltage regulation of MV distribution systems using sensitivity analysis method and PSO algorithm, International Journal of Electrical Power and Energy System, 68, pp. 52-60, (2015)
  • [5] Shi Y., Eberhart R.C., A modified particle swarm optimizer, The IEEE Congress on Evolutionary Computation (CEC 1998), pp. 69-73, (1998)
  • [6] Tizhoosh H.R., Opposition-based learning: a new scheme for machine intelligence, The IEEE International Conference of Intelligent for Modeling, Control and Automation, pp. 695-701, (2005)
  • [7] Wang H., Li H., Liu Y., Et al., Opposition-based particle swarm algorithm with Cauchy mutation, The IEEE Congress on Evolutionary Computation, pp. 356-360, (2007)
  • [8] Wang H., Wu Z.J., Rahnamayan S., Et al., Enhancing particle swarm optimization using generalized opposition-based learning, Information Sciences, 181, pp. 4699-4714, (2011)
  • [9] Zhou X.Y., Wu Z.J., Wang H., Et al., Elite opposition-based particle swarm optimization, Acta Electronica Sinica, 41, 8, pp. 1647-1652, (2013)
  • [10] Shahzad F., Baig A.R., Masood S., Et al., Opposition-based particle swarm optimization with velocity clamping (OVCPSO), Advances in Computational Intell, pp. 339-348, (2009)
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