Abstract
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.
Highlights
Several real-world applications can be formulated as continuous optimization problems in a wide range of scientific domains, such as engineering design, medical treatment, supply chain management, finance, and manufacturing [1,2,3,4,5,6,7,8,9]
Four new algorithms are presented to deal with various problems and applications
The other three methods are estimation of Distribution Algorithms (EDAs)-based methods which are denoted by DEDA, ASEDA, and FSEDA
Summary
Several real-world applications can be formulated as continuous optimization problems in a wide range of scientific domains, such as engineering design, medical treatment, supply chain management, finance, and manufacturing [1,2,3,4,5,6,7,8,9]. Because of the complicated simulation process, the objective function is subjected to different noise levels followed by expensive computational evaluation These problems are restricted by the following characterizations:. The complexity and time necessary to compute the objective function values; The difficulty of computing the exact gradient of the objective function, as well as its numerical approximation being very expensive; The noise values in the objective function To deal with these characterizations, global search methods should be invoked to avoid using classical nonlinear programming that fails to solve such problems with multiple local optima. In [31], an extension of multi-objective optimization is proposed, based on an differential evolution algorithm to manage the effect of noise in objective functions Their method applies an adaptive range of the sample size for estimating the fitness values.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
