Improved Differential Evolution Algorithm Based on Time-Space Joint Denoising

In the optimization process of solving engineering problems,the evaluation of individual fitness may be affected by environmental noise,so as to affect the reasonable survival of the fittest operation on the population,and result in a decline in algorithm performance.In order to combat the impact of...

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Published inJi suan ji ke xue Vol. 51; no. 9; pp. 299 - 309
Main Author WANG Bin, ZHANG Xinyu, JIN Haiyan
Format Journal Article
LanguageChinese
Published Editorial office of Computer Science 01.09.2024
Subjects
differential evolution|distribution estimation|noise|resampling|hybrid evolution|information utilization
Online AccessGet full text
ISSN1002-137X
DOI10.11896/jsjkx.230600074

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Abstract In the optimization process of solving engineering problems,the evaluation of individual fitness may be affected by environmental noise,so as to affect the reasonable survival of the fittest operation on the population,and result in a decline in algorithm performance.In order to combat the impact of noise environment,an improved differential evolution algorithm(SEDADE) based on joint temporal and spatial denoising is proposed.The population is divided into two subpopulations according to fitness ranking,and the subpopulations composed of poorly evaluated individuals are evolved using a distribution estimation algorithm(EDA).Gaussian distribution is used to model the solution space,using the randomness of multiple individual noises in the solution space to offset the noise impact.Differential evolution algorithm(DE) is used to evolve subpopulations with better evaluated individual composition,and a time-based stagnation resampling mechanism is introduced to denoise to improve convergence accuracy.The EDA infor
AbstractList In the optimization process of solving engineering problems,the evaluation of individual fitness may be affected by environmental noise,so as to affect the reasonable survival of the fittest operation on the population,and result in a decline in algorithm performance.In order to combat the impact of noise environment,an improved differential evolution algorithm(SEDADE) based on joint temporal and spatial denoising is proposed.The population is divided into two subpopulations according to fitness ranking,and the subpopulations composed of poorly evaluated individuals are evolved using a distribution estimation algorithm(EDA).Gaussian distribution is used to model the solution space,using the randomness of multiple individual noises in the solution space to offset the noise impact.Differential evolution algorithm(DE) is used to evolve subpopulations with better evaluated individual composition,and a time-based stagnation resampling mechanism is introduced to denoise to improve convergence accuracy.The EDA infor
Author WANG Bin, ZHANG Xinyu, JIN Haiyan
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Snippet In the optimization process of solving engineering problems,the evaluation of individual fitness may be affected by environmental noise,so as to affect the...
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SubjectTerms differential evolution|distribution estimation|noise|resampling|hybrid evolution|information utilization
Title Improved Differential Evolution Algorithm Based on Time-Space Joint Denoising
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