Non-inertial opposition-based particle swarm optimization with adaptive elite mutation

Lan-lan KANG; Wen-yong DONG; Wan-juan SONG; Kang-shun LI

doi:10.11959/j.issn.1000-436x.2017165

您当前的位置：

首页 >

文章列表页 >

Non-inertial opposition-based particle swarm optimization with adaptive elite mutation

Papers | 更新时间：2024-06-05

- Non-inertial opposition-based particle swarm optimization with adaptive elite mutation
- Journal on Communications Vol. 38, Issue 8, Pages: 66-78(2017)
- 作者机构：
  
  1. 武汉大学计算机学院，湖北武汉 430072
  2. 江西理工大学应用科学学院，江西赣州 341000
  3. 华南农业大学信息学院，广东广州 510642
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61170305);The Nationa1 Natura1 Science Foundation of China(60873114);Science and Techno1ogy Research in Department of Education of Jiangxi Province(GJJ161568);Science and Techno1ogy Research in Department of Education of Jiangxi Province(GJJ151521)
- DOI：10.11959/j.issn.1000-436x.2017165
  CLC： TP181
- Online First：2017-08，
  
  Published：25 August 2017
- 稿件说明：
移动端阅览
Lan-lan KANG, Wen-yong DONG, Wan-juan SONG, et al. Non-inertial opposition-based particle swarm optimization with adaptive elite mutation[J]. Journal on Communications, 2017, 38(8): 66-78.
DOI：

Lan-lan KANG, Wen-yong DONG, Wan-juan SONG, et al. Non-inertial opposition-based particle swarm optimization with adaptive elite mutation[J]. Journal on Communications, 2017, 38(8): 66-78. DOI： 10.11959/j.issn.1000-436x.2017165.

摘要

为解决反向粒子群优化算法计算开销大、易陷入局部最优的不足，提出一种无惯性的自适应精英变异反向粒子群优化算法（NOPSO）。NOPSO算法在反向学习方法的基础上，广泛获取环境信息，提出一种无惯性的速度（NIV）更新式来引导粒子飞行轨迹，从而有效加快算法的收敛过程。同时，为避免早熟现象的发生，引入了自适应精英变异策略（AEM），该策略在扩大种群搜索范围的同时，帮助粒子跳出局部最优。NIV 与 AEM 这 2种机制的结合，有效增加了种群多样性，平衡了反向粒子群算法中探索与开发的矛盾。实验结果表明，与主流反向粒子群优化算法相比，NOPSO算法无论是在计算精度还是计算开销上均具有较强的竞争能力。

Abstract

Non-inertia1 opposition-based partic1e swarm optimization with adaptive e1ite mutation（NOPSO）was proposed to overcome the drawbacks，such as，s1ow convergence speed，fa11ing into 1oca1 optimization，of opposition-based partic1e swarm optimization.In addition to increasing the diversity of popu1ation，two mechanisms were introduced to ba1ance the contradiction between exp1oration and exp1oitation during its iterations process.The first one was non-inertia1 ve1ocity（NIV）equation，which aimed to acce1erate the process of convergence of the a1gorithm via better access to and use of environmenta1 information.The second one was adaptive e1ite mutation strategy（AEM），which aimed to avoid trap into 1oca1 optimum.Experimenta1 resu1ts show NOPSO a1gorithm has stronger competitive abi1ity compared with opposition-based partic1e swarm optimizations and its varieties in both ca1cu1ation accuracy and computation cost.

关键词

Keywords

references

EIBEN A E , SMITH J . From evolutionary computation to the evolution of things [J ] . Nature , 2015 , 521 ( 7553 ): 476 - 482 .

KENNEDY J , EBERHART R C . Particle swarm optimization [C ] // IEEE International Conference on Neural Networks . 1995 : 1942 - 1948 .

INBARANI H H , AZAR A T , JOTHI G . Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis [J ] . Computer Methods and Programs in Biomedicine , 2014 , 113 ( 1 ): 175 - 185 .

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 [J ] . International Journal of Electrical Power and Energy System , 2015 , 68 : 52 - 60 .

JUANG Y T , TUNG S L , CHIU H C . Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions [J ] . Information Sciences , 2011 , 181 : 4539 - 4549 .

MENG H , TERESA W , WEIR J D . An adaptive particle swarm optimization with multiple adaptive methods [J ] . IEEE Transactions on Evolutionary Computation（TEC） , 2013 , 17 ( 5 ): 705 - 720 .

PEHLIVANOGLU Y V . A new particle swarm optimization method enhanced with a periodic mutation strategy and neural networks [J ] . IEEE Transactions on Evolutionary Computation , 2013 , 17 ( 3 ): 436 - 452 .

WANG H , LI H , LIU Y , et al . Opposition-based particle swarm algorithm with Cauchy mutation [C ] // IEEE Congress on Evolutionary Computation . 2007 : 356 - 360 .

WANG H , WU Z J , RAHNAMAYAN S , et al . Enhancing particle swarm optimization using generalized opposition-based learning [J ] . Information Sciences , 2011 , 181 : 4699 - 4714 .

KARAFOTIAS G , HOOGENDOORN M , EIBEN A E . Parameter control in evolutionary algorithms:trends and challenges [J ] . IEEE Transactions on Evolutionary Computation , 2015 , 19 ( 2 ): 167 - 187 .

SHI Y , EBERHART R C . A modified particle swarm optimizer [C ] // The IEEE Congress on Evolutionary Computation(CEC 1998) . 1998 : 69 - 73 .

TIZHOOSH H R , . Opposition-based learning:a new scheme for machine intelligence [C ] // The IEEE International Conference of Intelligent for Modeling,Control and Automation . 2005 : 695 - 701 .

SHI Y , EBERHART R C . Empirical study of particle swarm optimization [C ] // The IEEE Congress on Evolutionary Computation(CEC) . 1999 .

董文永 , 康岚兰 , 刘宇航 , 等 . 带自适应精英扰动及惯性权重的反向粒子群优化算法 [J ] . 通信学报 , 2016 , 37 ( 12 ): 1 - 10 .

DONG W Y , KANG L L , LIU Y H , et al . An opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight [J ] . Journal on Communications , 2016 , 37 ( 12 ): 1 - 10 .

周新宇 , 吴志健 , 王晖 , 等 . 一种精英反向学习的粒子群优化算法 [J ] . 电子学报 , 2013 , 41 ( 8 ): 1647 - 1652 .

ZHOU X Y , WU Z J , WANG H , et al . Elite opposition-based particle swarm optimization [J ] . Acta Electronica Sinica , 2013 , 41 ( 8 ): 1647 - 1652 .

SHAHZAD F , BAIG A R , MASOOD S , et al . Opposition-based particle swarm optimization with velocity clamping(OVCPSO) [J ] . Advances in Computational Intell , 2009 : 339 - 348 .

Views

1974

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

AI问答

⁰