Estimation of Distribution Algorithms with Fuzzy Sampling for Stochastic Programming Problems

被引:2
|
作者
Hedar, Abdel-Rahman [1 ,2 ]
Allam, Amira A. [3 ]
Fahim, Alaa [3 ]
机构
[1] Umm Al Qura Univ, Dept Comp Sci Jamoum, Mecca 25371, Saudi Arabia
[2] Assiut Univ, Fac Comp & Informat, Dept Comp Sci, Assiut 71526, Egypt
[3] Assiut Univ, Fac Sci, Dept Math, Assiut 71516, Egypt
来源
APPLIED SCIENCES-BASEL | 2020年 / 10卷 / 19期
关键词
estimation of distribution algorithms; stochastic programming; simulation-based optimization; fuzzy sampling; variable sample path; HISTOGRAM-BASED ESTIMATION; DIFFERENTIAL EVOLUTION; OPTIMIZATION; SIMULATION; SELECTION;
D O I
10.3390/app10196937
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Generating practical methods for simulation-based optimization has attracted a great deal of attention recently. In this paper, the estimation of distribution algorithms are used to solve nonlinear continuous optimization problems that contain noise. One common approach to dealing with these problems is to combine sampling methods with optimal search methods. Sampling techniques have a serious problem when the sample size is small, so estimating the objective function values with noise is not accurate in this case. In this research, a new sampling technique is proposed based on fuzzy logic to deal with small sample sizes. Then, simulation-based optimization methods are designed by combining the estimation of distribution algorithms with the proposed sampling technique and other sampling techniques to solve the stochastic programming problems. Moreover, additive versions of the proposed methods are developed to optimize functions without noise in order to evaluate different efficiency levels of the proposed methods. In order to test the performance of the proposed methods, different numerical experiments were carried out using several benchmark test functions. Finally, three real-world applications are considered to assess the performance of the proposed methods.
引用
收藏
页码:1 / 27
页数:27
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