Hybrid hierarchical autonomous learning quantum particle swarm optimization

WANG Wenfeng; XU Deng; FU Jing; HAN Longzhe; FANG Zonghua; DONG Jianhua; ZHANG Xiang

doi:10.13471/j.cnki.acta.snus.2020.09.07.2020A047

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Hybrid hierarchical autonomous learning quantum particle swarm optimization

Research articles | 更新时间：2023-11-01

- Hybrid hierarchical autonomous learning quantum particle swarm optimization
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 60, Issue 6, Pages: 161-168(2021)
- 作者机构：
  
  1.南昌工程学院江西省水信息协同感知与智能处理重点实验室，江西南昌330099
  2.南昌工程学院信息工程学院，江西南昌 330099
- 作者简介：
- 基金信息：
- DOI：10.13471/j.cnki.acta.snus.2020.09.07.2020A047
  CLC： TP393
- Published：25 November 2021，
  
  Published Online：06 January 2021，
  
  Received：07 September 2020，
  
  Accepted：08 December 2020
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王文丰,徐灯,傅晶等.混合分层自主学习量子粒子群优化算法[J].中山大学学报(自然科学版),2021,60(06):161-168.

WANG Wenfeng,XU Deng,FU Jing,et al.Hybrid hierarchical autonomous learning quantum particle swarm optimization[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2021,60(06):161-168.
王文丰,徐灯,傅晶等.混合分层自主学习量子粒子群优化算法[J].中山大学学报(自然科学版),2021,60(06):161-168. DOI： 10.13471/j.cnki.acta.snus.2020.09.07.2020A047.

WANG Wenfeng,XU Deng,FU Jing,et al.Hybrid hierarchical autonomous learning quantum particle swarm optimization[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2021,60(06):161-168. DOI： 10.13471/j.cnki.acta.snus.2020.09.07.2020A047.

摘要

针对粒子群优化算法容易陷入局部最优、收敛精度不高以及收敛速度较慢的问题，本文提出一种混合分层自主学习量子粒子群优化算法HHQPSO。首先，根据粒子适应度值和迭代次数将种群动态划分为三个阶层：上、下两层粒子分布较少，分别采用局部学习模型和全局学习模型，以增强粒子多样性；中层粒子分布较多，采用混合自适应量子学习模型。其次，在混合量子模型中提出改进差分策略以更新粒子的随机位置，并引入Levy飞行策略以提高算法的收敛精度和收敛速度。最后，分别在9个典型测试函数上对6种改进粒子群算法进行仿真对比实验。实验结果表明，HHQPSO算法在收敛精度、速度和稳定性上均有着较为明显的优势，特别适用于多峰函数寻优。

Abstract

In order to solve the problem that particle swarm optimization is easy to be trapped in local optimum， low convergence precision and slow convergence speed， a hybrid hierarchical autonomous learning quantum particle swarm optimization （HHQPSO） is proposed. Firstly， the population is dynamically divided into three layers according to the fitness value of particles and the number of iterations. Since the particles of upper and lower layers are less distributed， local learning model and global learning model are respectively used to increase the diversity of particles. While the particles of middle layer are more distributed， the hybrid adaptive quantum learning model is adopted. Secondly， an improved differential strategy is proposed to update the random positions of particles in the hybrid quantum model， and the Levy flight strategy is introduced to improve the convergence accuracy and speed of the algorithm. Finally， six improved particle swarm optimization algorithms are compared on 9 typical test functions. Experimental results show that the HHQPSO algorithm has obvious advantages in convergence accuracy， speed and stability， especially suitable for multi-peak search function optimization.

关键词

量子粒子群差分策略Levy飞行策略自主学习

Keywords

quantum particle swarmdifferential strategyLevy flight strategyautonomous learning

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