QuantumBehaved Particle Swarm Optimization Algorithm Based on Simplex Method

REN Xiao-kang; HAO Rui-zhi; SUN Zheng-xing; SHI Bian-xia

Chinese

您当前的位置：

首页 >

文章列表页 >

QuantumBehaved Particle Swarm Optimization Algorithm Based on Simplex Method

更新时间：2026-09-04

- QuantumBehaved Particle Swarm Optimization Algorithm Based on Simplex Method
- Vol. 27, Issue 1, Pages: 154-157(2010)
- 作者机构：
  
  西北师范大学机构数学与信息科学学院,甘肃,苏州,730070
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2010
- 稿件说明：
移动端阅览
任小康, 郝瑞芝, 孙正兴, 史变霞. 基于单纯形法的量子粒子群优化算法[J]. 微电子学与计算机, 2010, 27(1): 154-157.

REN Xiao-kang, HAO Rui-zhi, SUN Zheng-xing, et al. QuantumBehaved Particle Swarm Optimization Algorithm Based on Simplex Method[J]. 2010, 27(1): 154-157.
任小康, 郝瑞芝, 孙正兴, 史变霞. 基于单纯形法的量子粒子群优化算法[J]. 微电子学与计算机, 2010, 27(1): 154-157. DOI：

REN Xiao-kang, HAO Rui-zhi, SUN Zheng-xing, et al. QuantumBehaved Particle Swarm Optimization Algorithm Based on Simplex Method[J]. 2010, 27(1): 154-157. DOI：

摘要

针对粒子群优化算法容易陷入局部极值点

进化后期收敛慢和优化精度较差等缺点

提出了将单纯形搜索法与量子粒子群算法混合的改进算法

更好的平衡了全局搜索和局部搜索能力.仿真结果表明

该算法效率高、优化性能好

其性能远远优于一般的粒子群算法与量子粒子群算法.

Abstract

In allusion to particle swarm optimization algorithm (PSO) many defects such as being easy to get into local extremum

slow convergence in the end of evolution stage and low computational precision.A quantumbehaved particle swarm optimization algorithm (QPSO) with simplex method (SQPSO) is proposed

which can better balance the global searching and local searching ability.The experiment result demonstrated that SQPSO is of high efficiency

and of excellent optimum performance

It is of much better performance to PSO and QPSO.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

An Improved Glowworm Swarm Optimization Algorithm with Changing Step

Steganography model guided by attention mechanism and improved dense convolution

Design of ultra-low power real-time clock circuits

Simulation on the DC characteristics of double heterojunction p-GaN HEMT with AlN/GaN hybrid buffer layer

Aerial vehicle detection algorithm based on multi-scale fusion and small-object enhancement

Related Author

李恒

郭星

李炜

Related Institution

安徽大学计算智能与信号处理重点实验室

安徽大学计算机科学与技术学院

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰