A New Swarm Intelligence Algorithm by Simulating Sheep Behaviors

doi:10.3969/j.issn.0372-2112.2018.06.004

您当前的位置：

首页 >

文章列表页 >

A New Swarm Intelligence Algorithm by Simulating Sheep Behaviors

更新时间：2025-07-08

- A New Swarm Intelligence Algorithm by Simulating Sheep Behaviors
- Acta Electronica Sinica Vol. 46, Issue 6, Pages: 1300-1305(2018)
- 作者机构：
  
  1. 辽宁大学信息学院,辽宁,沈阳,110036
  2. 东北大学计算机科学与工程学院,辽宁,沈阳,110169
  3. 东北大学信息科学与工程学院,辽宁,沈阳,110169
  4. 东北大学软件学院,辽宁,沈阳,110169
  5. 辽宁大学信息学院,辽宁,沈阳,110036
  6. 东北大学计算机科学与工程学院,辽宁,沈阳,110169
  7. 东北大学信息科学与工程学院,辽宁,沈阳,110169
  8. 东北大学软件学院,辽宁,沈阳,110169
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61572123);Distinguished Young Scholars Fund supported by National Natural Science Foundation of China (No.71325002);National Natural Science Foundation of China Key International Cooperation Research Project (No.71620107003);Ministry of Education - China Mobile Research Fund (No.MCM20160201);Doctoral Research Foundation of Liaoning Province (No.20170520323);Doctoral Research Project of Liaoning University
- DOI：10.3969/j.issn.0372-2112.2018.06.004
  CLC： O224
- Published Online：25 June 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
A New Swarm Intelligence Algorithm by Simulating Sheep Behaviors[J]. Acta Electronica Sinica, 2018, 46(6): 1300-1305.
DOI：

A New Swarm Intelligence Algorithm by Simulating Sheep Behaviors[J]. Acta Electronica Sinica, 2018, 46(6): 1300-1305. DOI： 10.3969/j.issn.0372-2112.2018.06.004.

摘要

针对当前多种群集智能算法往往包含过多策略而掩盖算法核心机理的现状，设计了一种模拟羊群行为的新型群集智能算法.该算法从群集智能算法的核心出发，通过分别模拟羊群的三类行为：头羊引领、羊群互动和牧羊犬监督，设计群集智能算法中相对应的三种策略：全局探索、局部开发和跳出局部优化.基准测试函数的实验结果表明，较之粒子群算法，该算法能获得更高质量的解，同时具有更快的收敛速度和更好的稳定性.

Abstract

The status of current various swarm intelligence algorithms contains many strategies which cover their core issues

therefore

a new swarm intelligence algorithm by simulating sheep behaviors optimization (SO) is devised.Based on the core of swarm intelligence algorithms

SO designs three kinds of strategies

namely global exploration

local exploitation and escaping from local optimization

by simulating three different kinds of corresponding sheep behaviors

namely bellwether guidance

moving to each other

and shepherd dog supervision respectively.The experimental results of benchmark test functions demonstrate that

compared with particle swarm optimization

SO gets better solution

faster convergence speed and better stability.

关键词

Keywords

references

Views

1040

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰