Abstract
With the rapid growth of simulation software packages, generating practical tools for simulation-based optimization has attracted a lot of interest over the last decades. In this paper, a modified method of Estimation of Distribution Algorithms (EDAs) is constructed by a combination with variable-sample techniques to deal with simulation-based optimization problems. Moreover, a new variable-sample technique is introduced to support the search process whenever the sample sizes are small, especially in the beginning of the search process. The proposed method shows efficient results by simulating several numerical experiments.
Highlights
Realistic systems often lack a sufficient amount of real data for the purposes of output response evaluation
We propose an Estimation of Distribution Algorithms (EDAs)-based method to deal with the simulation-based global optimization problems
EDA-Min-Max Sampling Search (MMSS) algorithm is modified by applying the same main steps of the EDA-Sampling Pure Random Search (SPRS) method, which were explained in the previous subsection, except the function evaluation method
Summary
Realistic systems often lack a sufficient amount of real data for the purposes of output response evaluation. The main idea of the variable-sample method is to convert a stochastic optimization problem to a deterministic one This is achieved by estimating the objective function that contains random variables at a certain solution by sampling it with several trails. This type of Monte Carlo simulation can obtain approximate values of the objective function. The proposed modified MMSS method restricts the noisy values of the estimated function in small-size samples at the early stages of the search process. Several experiments with their technical discussion have been done in order to test the performance of the proposed methods.
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