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

Wen-yong DONG; Lan-lan KANG; Yu-hang LIU; Kang-shun LI

doi:10.11959/j.issn.1000-436x.2016224

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

Academic paper | 更新时间：2024-06-05

- Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight
- Journal on Communications Vol. 37, Issue 12, Pages: 1-10(2016)
- 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 National Natural Science Foundation of China(61672024)
- DOI：10.11959/j.issn.1000-436x.2016224
  CLC： TP181
- Online First：2013-12，
  
  Published：25 December 2016
- 稿件说明：
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Wen-yong DONG, Lan-lan KANG, Yu-hang LIU, et al. Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight[J]. Journal on Communications, 2016, 37(12): 1-10.
DOI：

Wen-yong DONG, Lan-lan KANG, Yu-hang LIU, et al. Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight[J]. Journal on Communications, 2016, 37(12): 1-10. DOI： 10.11959/j.issn.1000-436x.2016224.

摘要

针对反向粒子群优化算法存在的易陷入局部最优、计算开销大等问题，提出了一种带自适应精英粒子变异及非线性惯性权重的反向粒子群优化算法（OPSO-AEM＆NIW），来克服该算法的不足。OPSO-AEM＆NIW算法在一般性反向学习方法的基础上，利用粒子适应度比重等信息，引入了非线性的自适应惯性权重（NIW）调整各个粒子的活跃程度，继而加速算法的收敛过程。为避免粒子陷入局部最优解而导致搜索停滞现象的发生，提出了自适应精英变异策略（AEM）来增大搜索范围，结合精英粒子的反向搜索能力，达到跳出局部最优解的目的。上述2种机制的结合，可以有效克服反向粒子群算法的探索与开发的矛盾。实验结果表明，与主流反向粒子群优化算法相比，OPSO-AEM＆NIW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

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 be-tween 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 parti-cle 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 optimiza-tions and its varieties in both calculation accuracy and computation cost.

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references

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