Estimation of Distribution Algorithms with Fuzzy Sampling for Stochastic Programming Problems

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 problem...

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Bibliographic Details
Published in:Applied sciences Vol. 10; no. 19; p. 6937
Main Authors: Hedar, Abdel-Rahman, Allam, Amira A., Fahim, Alaa
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2020
Subjects:
Algorithms
Approximation
Design
Distribution management software
estimation of distribution algorithms
Fuzzy algorithms
Fuzzy logic
fuzzy sampling
Fuzzy sets
Fuzzy systems
Genetic algorithms
Mathematical functions
Measurement
Methods
Mutation
Noise
Normal distribution
Optimization
Probability distribution
Random variables
Sample size
Sampling techniques
Simulation
simulation-based optimization
Stochastic programming
variable sample path
Saudi Arabia
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary: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.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app10196937