融合改进Sine混沌映射的新型粒子群优化算法

刘磊; 姜博文; 周恒扬; 浦晨玮; 钱鹏飞; 刘波

doi:10.7652/xjtuxb202308018

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融合改进Sine混沌映射的新型粒子群优化算法

专题 ICFPMCE 2023国际会议 | 更新时间：2025-05-08

- 融合改进Sine混沌映射的新型粒子群优化算法
- A Novel Particle Swarm Optimization Algorithm Incorporating Improved Sine Chaos Mapping
- 西安交通大学学报 2023年57卷第8期页码：182-193
- 作者机构：
  
  1. 江苏大学机械工程学院,江苏,镇江,212013
  2. 浙江大学流体动力基础件与机电系统全国重点实验室,杭州,310027
  3. 法赫德国王石油与矿业大学电气工程系, 31261, 沙特阿拉伯达兰)
- 作者简介：
  
  : 2023-03-30。作者简介: 刘磊(1998—),男,博士生
  钱鹏飞(通信作者),男,副教授,博士生导师。基金项目: 国家自然科学基金资助项目(52075223)
- 基金信息：
  
  : 2023-03-30。作者简介: 刘磊(1998—),男,博士生;钱鹏飞(通信作者),男,副教授,博士生导师。基金项目: 国家自然科学基金资助项目(52075223);中国博士后科学基金资助项目(2021M691308);江苏省研究生科研与实践创新计划资助项目(KYCX23_3731)。
- DOI：10.7652/xjtuxb202308018
  中图分类号： TP301
- 网络首发：2023-08-10，
  
  纸质出版：2023
- 稿件说明：
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刘磊, 姜博文, 周恒扬, 等. 融合改进Sine混沌映射的新型粒子群优化算法[J]. 西安交通大学学报, 2023,57(8):182-193.

LIU Lei, JIANG Bowen, ZHOU Hengyang, et al. A Novel Particle Swarm Optimization Algorithm Incorporating Improved Sine Chaos Mapping[J]. 2023, 57(8): 182-193.
刘磊, 姜博文, 周恒扬, 等. 融合改进Sine混沌映射的新型粒子群优化算法[J]. 西安交通大学学报, 2023,57(8):182-193. DOI： 10.7652/xjtuxb202308018.

LIU Lei, JIANG Bowen, ZHOU Hengyang, et al. A Novel Particle Swarm Optimization Algorithm Incorporating Improved Sine Chaos Mapping[J]. 2023, 57(8): 182-193. DOI： 10.7652/xjtuxb202308018.

摘要

为了应对传统粒子群算法(PSO)存在的初始位置不均匀、易达到局部最优、搜索精度不高等问题

提出了一种基于改进Sine混沌映射的新型PSO算法。采用一种改进的Sine混沌映射技术代替传统的伪随机数方法生成初始粒子种群

以丰富种群的多样性。在原始基本位置更新公式的基础上增加两种新的位置更新机制

并分别引入一个高斯变异算子

以实现算法勘探性能和开发性能之间的动态平衡

以及在迭代过程中使粒子有效跳出局部最优。在由7个单峰函数、6个多峰函数和10个固定维函数组成的基准测试函数和3个带约束经典工程优化设计问题上对所提出算法开展仿真实验

并与其他几种流行的PSO变体进行对比。仿真结果表明:与其他PSO变体相比

基于改进Sine混沌映射的新型PSO算法具有更快的收敛速度和更高的寻优精度

对于基准测试函数的寻优结果有20个排名第一

约为总测试函数的87%; 该算法在压力容器和工字梁设计优化中

综合性能排在第一位

应可用于解决一些实际工程优化问题。

Abstract

In order to address the problems of uneven initial positions

ease of reaching local optimum

and low search accuracy in traditional particle swarm algorithm(PSO)

a novel PSO algorithm based on an improved Sine chaotic mapping is proposed. The improved Sine chaotic mapping technique is used instead of the traditional pseudo-random number method for generating the initial particle population to enrich the population diversity. Two new position update mechanisms are added to the original basic position update formula. A Gaussian mutation operator is introduced to achieve a dynamic balance between the exploration and exploitation performance of the algorithm

as well as to help particles effectively jump out of the local optima during the iteration process. For three classical engineering optimization design problems with constraints

simulation experiments are performed for the proposed algorithm based on a benchmark test function consisting of seven single-peaked functions

six multi-peaked functions and ten fixed-dimensional functions. This algorithm is then compared with several other popular PSO variants. Simulation results show that the novel PSO algorithm based on improved Sine chaotic mapping has faster convergence speed and higher optimization-seeking accuracy than those of other PSO variants. For the benchmark test functions

it ranked first in 20 of them

accounting for about 87% of the total testing functions. The proposed algorithm ranked first in the overall performance of pressure vessel and I-beam design optimization

and can be used to solve some practical engineering optimization problems.

关键词

Keywords

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