Hybrid Particle Swarm Optimization Algorithm Based on Intuitionistic Fuzzy Entropy

WANG Yi; LI Xiao-meng; GENG Guo-hua; ZHOU Lin; DUAN Yan-zhong

doi:10.12263/DZXB.20201387

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Hybrid Particle Swarm Optimization Algorithm Based on Intuitionistic Fuzzy Entropy

PAPERS | 更新时间：2025-12-08

- Hybrid Particle Swarm Optimization Algorithm Based on Intuitionistic Fuzzy Entropy
- ACTA ELECTRONICA SINICA Vol. 49, Issue 12, Pages: 2381-2389(2021)
- 作者机构：
  
  西北大学信息科学与技术学院，陕西西安 710127
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20201387
  CLC： TP182;TP391
- Received：06 December 2020，
  
  Revised：2021-01-06，
  
  Published：25 December 2021
- 稿件说明：
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王毅,李晓梦,耿国华等.基于直觉模糊熵的混合粒子群优化算法[J].电子学报,2021,49(12):2381-2389.

WANG Yi,LI Xiao-meng,GENG Guo-hua,et al.Hybrid Particle Swarm Optimization Algorithm Based on Intuitionistic Fuzzy Entropy[J].ACTA ELECTRONICA SINICA,2021,49(12):2381-2389.
王毅,李晓梦,耿国华等.基于直觉模糊熵的混合粒子群优化算法[J].电子学报,2021,49(12):2381-2389. DOI： 10.12263/DZXB.20201387.

WANG Yi,LI Xiao-meng,GENG Guo-hua,et al.Hybrid Particle Swarm Optimization Algorithm Based on Intuitionistic Fuzzy Entropy[J].ACTA ELECTRONICA SINICA,2021,49(12):2381-2389. DOI： 10.12263/DZXB.20201387.

摘要

为了提升粒子群算法的全局寻优与局部精细搜索能力并加快收敛速度，提出了基于直觉模糊熵的混合粒子群优化算法.该算法采用粒子的历史最优解信息构造直觉模糊熵的自适应函数，并将熵值作为扰动因子动态调节惯性权重，同时建立自适应全局最优粒子学习策略对扰动后的粒子进行训练，在保持多样性传播的基础上选择学习对象，使粒子探索更多新区域，实现种群间的协作与并行进化.通过仿真实验，将本文算法与两种衍生算法以及其他改进粒子群算法在11个测试函数上进行比较，结果表明，本算法在求解精度、收敛速度和寻优效率上均有更好表现.

Abstract

In order to improve the global and local fine search capabilities of the particle swarm algorithm and accelerate the convergence speed

hybrid particle swarm optimization algorithm based on intuitive fuzzy entropy is proposed. The algorithm constructs an adaptive function of intuitive fuzzy entropy by using the information of the historical optimal solution of particles

and uses the entropy value as a disturbance factor to dynamically adjust the inertia weight. At the same time

it establishes an adaptive global optimal particle learning strategy to train the disturbed particles

chooses learning objects based on maintaining the diversity of propagation

enables the particles to explore more new areas

and realizes the cooperation and parallel evolution among populations. Through simulation experiments

the algorithm is compared with two derivation algorithms and other improved particle swarm optimization algorithms on 11 test functions. The results show that the algorithm performs better in solving accuracy

convergence speed and optimization efficiency.

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references

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