Self-learning differential evolution algorithm for dynamic polycentric problems

Xing-bao LIU; Jian-ping YIN; Chun-hua HU; Rong-yuan CHEN

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

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Self-learning differential evolution algorithm for dynamic polycentric problems

Academic communication | 更新时间：2024-06-05

- Self-learning differential evolution algorithm for dynamic polycentric problems
- Journal on Communications Vol. 36, Issue 7, Pages: 166-175(2015)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(61273232);The National Natural Science Foundation of China(41101425);The Ministry of Education Program for New Century Excellent Talents(NECT-2013-0785)
- DOI：10.11959/j.issn.1000-436x.2015154
  CLC： TP18
- Online First：2015-07，
  
  Published：25 July 2015
- 稿件说明：
移动端阅览
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：

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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