|Table of Contents|

An Adaptively Reversed Diffuse Evolutionary Algorithmin Dynamic Environments(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2020年04期
Page:
119-128
Research Field:
·智慧应急信息技术·
Publishing date:

Info

Title:
An Adaptively Reversed Diffuse Evolutionary Algorithmin Dynamic Environments
Author(s):
Cao Wenliang1; Kang Lanlan2; Wang Shi1
(1.Department of Computer Engineering,Dongguan Polytechnic,Dongguan 523808,China)(2.College of Applied Science,Jiangxi University of Science and Technology,Ganzhou 341000,China)
Keywords:
dynamic optimization; particle swarm optimization; reversed diffuse; between-swarms average Mahalanobis distance
PACS:
TP181
DOI:
10.3969/j.issn.1001-4616.2020.04.017
Abstract:
To solve the problem that traditional evolutionary optimization algorithm is difficult to effectively keep track of the optimal solution in dynamic environment,this paper proposes an adaptively reversed diffuse evolutionary algorithm(ARDEA). The new algorithm adopts the multi-population strategy to track the optimal solution and monitors the environmental changes by setting the global dynamic sentryin each subgroup. A differential particle swarm velocity update formula is introduced to guide individuals to search for the optimal points in the search space. Meanwhile,in order to ensure the diversity of the population and the search efficiency of the sub-population,a new exclusion strategy is proposed in this paper. This strategy includes two method. Firstly,it uses between-swarms average Mahalanobis distance to judge the inter-population distance. If the distance is too small between two sub-populations,hill-valley function is used to further determine whether they tracked the same peak or not. Secondly,the subpopulations with poor performance in the search overlap will be reinitialized by reverse diffusion operation(RD). The new algorithm is compared with several state-of-artdynamic optimization algorithms on moving peak problem. The results show that the ARDEA algorithm can track the optimal solution more effectively in the dynamic environment. Compared with other algorithms,the ARDEA algorithm shows strong robustness and adaptability.

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Last Update: 2020-11-15