Anisotropic adaptive variance scaling for Gaussian estimation of distribution algorithm

Traditional Gaussian estimation of distribution algorithms (EDAs) are confronted with issues that the variable variances decrease fast and the main search direction tends to become perpendicular to the improvement direction of the fitness function, which reduces the search efficiency of Gaussian EDA...

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Vydáno v:Knowledge-based systems Ročník 146; s. 142 - 151
Hlavní autoři: Ren, Zhigang, Liang, Yongsheng, Wang, Lin, Zhang, Aimin, Pang, Bei, Li, Biying
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 15.04.2018
Elsevier Science Ltd
Témata:
Algorithms
Anisotropic adaptive variance scaling
Anisotropy
Convergence
Estimation
Evolutionary algorithms
Fitness
Gaussian estimation of distribution algorithm
Genetic algorithms
Global optimization
Normal distribution
Optimization
Performance enhancement
Premature convergence
Scaling
Search direction
Searching
Variance
Variance analysis
Search direction
Premature convergence
Anisotropic adaptive variance scaling
Gaussian estimation of distribution algorithm
ISSN:0950-7051, 1872-7409
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Shrnutí:Traditional Gaussian estimation of distribution algorithms (EDAs) are confronted with issues that the variable variances decrease fast and the main search direction tends to become perpendicular to the improvement direction of the fitness function, which reduces the search efficiency of Gaussian EDAs (GEDAs) and makes them subject to premature convergence. In this paper, a novel anisotropic adaptive variance scaling (AAVS) technique is proposed to improve the performance of traditional GEDAs and a new GEDA variant named AAVS-EDA is developed. The advantages of AAVS over the existing variance scaling strategies lie in its ability for tuning the variances and main search direction of GEDA simultaneously, which are achieved by anisotropically scaling the variances along different eigendirections based on corresponding landscape characteristics captured by a simple topology-based detection method. Besides, AAVS-EDA also adopts an auxiliary global monitor to ensure its convergence by shrinking all the variances if no improvement is achieved in a generation. The evaluation results on 30 benchmark functions of CEC2014 test suite demonstrate that AAVS-EDA possesses stronger global optimization efficiency than traditional GEDAs. The comparison with other state-of-the-art evolutionary algorithms also shows that AAVS-EDA is efficient and competitive.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2018.02.001