解决动态多中心问题的自学习差异进化算法

刘星宝; 殷建平; 胡春华; 陈荣元

doi:10.11959/j.issn.1000-436x.2015154

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解决动态多中心问题的自学习差异进化算法

学术通信 | 更新时间：2024-06-05

- 解决动态多中心问题的自学习差异进化算法
- Self-learning differential evolution algorithm for dynamic polycentric problems
- 通信学报 2015年36卷第7期页码：166-175
- 作者机构：
  
  1. 湖南商学院计算机与信息工程学院，湖南长沙 410205
  2. 国防科学技术大学计算机学院，湖南长沙 410073
- 作者简介：
  
  [ "刘星宝（1977-），男，山东临沂人，博士，湖南商学院讲师，主要研究方向为智能计算、投资组合优化。" ]
  [ "殷建平（1963-），男，湖南益阳人，博士，国防科学技术大学教授，主要研究方向为人工智能、模式识别和信息安全。" ]
  [ "胡春华（1973-），男，湖南新化人，博士，湖南商学院教授，主要研究方向为云计算，电子商务。" ]
  [ "陈荣元[通信作者]（1976-），男，江苏泰州人，博士，湖南商学院高级研究员，主要研究方向为图形图像处理。E-mail:chenrongyuan@126.com。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61273232);国家自然科学基金资助项目(41101425);教育部新世纪优秀人才支持计划基金资助项目(NECT-2013-0785)
- DOI：10.11959/j.issn.1000-436x.2015154
  中图分类号： TP18
- 网络出版日期：2015-07，
  
  纸质出版日期：2015-07-25
- 稿件说明：
移动端阅览
刘星宝, 殷建平, 胡春华, 等. 解决动态多中心问题的自学习差异进化算法[J]. 通信学报, 2015,36(7):166-175.

Xing-bao LIU, Jian-ping YIN, Chun-hua HU, et al. Self-learning differential evolution algorithm for dynamic polycentric problems[J]. Journal on communications, 2015, 36(7): 166-175.
刘星宝, 殷建平, 胡春华, 等. 解决动态多中心问题的自学习差异进化算法[J]. 通信学报, 2015,36(7):166-175. DOI： 10.11959/j.issn.1000-436x.2015154.

Xing-bao LIU, Jian-ping YIN, Chun-hua HU, et al. Self-learning differential evolution algorithm for dynamic polycentric problems[J]. Journal on communications, 2015, 36(7): 166-175. DOI： 10.11959/j.issn.1000-436x.2015154.

摘要

为解决动态环境下的多中心优化问题，提出自学习差异进化算法。通过评估特定个体检测到环境变化，自学习算子将群体引至新的环境，并保持群体的拓扑结构不变，以继续当前的进化趋势。采用邻域搜索机制加快算法的收敛速度，引入随机个体迁入机制增加群体多样性。实验以周期动态函数为测试对象，比较自学习差异进化算法与部分智能优化算法的性能，结果表明，新算法有更快的收敛速度和更好的环境适应能力。

Abstract

A novel self-learning differential evolution algorithm is proposed to solve dynamical multi-center optimization problems.The approach of re-evaluating some specific individuals is used to monitor environmental changes.The proposed self-learning operator guides the evolutionary group to a new environment

meanwhile maintains the stable topology structure of group to maintain the current evolutionary trend.A neighborhood search mechanism and a random immigrant mechanism are adapted to make a tradeoff between algorithmic convergence and population diversity.The experiment studies on a periodic dynamic function set suits are done

and the comparisons with peer algorithms show that the self-learning differential algorithm outperforms other algorithms in term of convergence and adaptability under dynamical environment.

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references

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