Simulation-Based EDAs for Stochastic Programming Problems

Hedar, Abdel-Rahman; Allam, Amira A.; Abdel-Hakim, Alaa E.

doi:10.3390/computation8010018

Cited by 3 publications

(7 citation statements)

References 48 publications

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“…Two main comparison experiments are presented to test the FSEDA performance against some benchmark methods. The first experiment used Test Set C to make the comparisons with methods in [10,68], while the other experiment used Test Set D with methods in [12,13]. Table 7 shows the best and the average errors obtained by the proposed FSEDA method and the following evolutionary-based methods:…”

Section: Simulation-based Optimization Resultsmentioning

confidence: 99%

“…The FSEDA method found the objective function value f min = 1558.9, with the decision variable values x min = (2,13,10,20). The best known value for the KANDW3 Problem is f * = 2613, as mentioned in [78].…”

Section: Results Of Kandw3 Problemmentioning

confidence: 99%

“…For parameters m and L, which follow parameter R, setting higher values yields higher computational times without any significant improvement. For sample size parameters, the settings follow the recommended values in [10,13].…”

Section: Parameter Tuning and Settingmentioning

confidence: 99%

“…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]. Many of these optimization formulations have some sort of uncertainty and their objective functions contain noise [10][11][12][13]. Moreover, it is sometimes necessary to deal with complex problems with high nonlinearity and/or dimensionality, and occasionally there is no analytical form for the objective function [14].…”

Section: Introductionmentioning

confidence: 99%

“…Wilcoxon rank-sum test for the results ofTable 7.The other comparison experiment compared the FSEDA results with those of the following benchmark methods[12,13] using Test Set D with dimensions 30 and 100.…”

mentioning

confidence: 99%

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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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Section: Simulation-based Optimization Resultsmentioning

confidence: 99%

Section: Results Of Kandw3 Problemmentioning

confidence: 99%

Section: Parameter Tuning and Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decision-Making Based on Spherical Linear Diophantine Fuzzy Rough Aggregation Operators and EDAS Method

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Khan,

Naeem

et al. 2023

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In everyday life, decision-making is a difficult task fraught with ambiguity and uncertainty. Many researchers and scholars have suggested numerous fuzzy set theories to resolve these ambiguities and uncertainties. The EDAS method (evaluation based on distance from average answer) is extremely beneficial in decision-making situations. In multi-attribute group decision-making (MAGDM) situations, this is especially true when there are more competing criteria. In this paper, we introduce the concept of spherical linear diophantine fuzzy rough sets (SLDFRSs). We develop basic operational laws and a number of propose aggregation operators. Furthermore, the necessary and desired features of SLDFRS are explored. This study proposes a new technique known as the spherical linear diophantine fuzzy rough set EDAS (SLDFRS-EDAS) method to deal with these uncertainties in the MAGDM problem. A MAGDM technique is intended to evaluate the emergency system based on the newly introduced operators. Furthermore, a comparison study of the activity and applicability of the suggested approach with earlier procedures is used to validate its viability and applicability.

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An adaptive differential evolution algorithm based on data preprocessing method to solve global optimization problem

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2023

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Differential evolution (DE) algorithm is a classical natural-inspired optimization algorithm, which has good optimization performance. However, with the deepening of research, some researchers found that the quality of the candidate solution of the population in the differential evolution algorithm is poor and its global search ability is not enough when solving the global optimization problem. Therefore, in order to solve the above problems, we propose an adaptive differential evolution algorithm based on data processing method (ADEDPM). In this paper, the data preprocessing method is implemented by k-means clustering algorithm, which is used to divide the initial population into multiple clusters according to the average value of fitness, and select candidate solutions in each cluster according to different proportions. This method improves the quality of candidate solutions of the population to a certain extent. In addition, in order to solve the problem of insufficient global search ability in differential evolution algorithm, we also proposed a new mutation strategy, which is called “DE/current-to-𝑝1 best&𝑝2 best”. This strategy guides the search direction of the differential evolution algorithm by selecting individuals with good fitness, so that its search range in the most promising candidate solution region, and indirectly increases the population diversity of the algorithm. Finally, we propose an adaptive parameter control method, which can effectively balance the relationship between the exploration process and the exploitation process to achieve better performance of the algorithm. In order to verify the effectiveness of the proposed algorithm, the ADEDPM is compared with five optimization algorithms of the same type in the past three years, which are AAGSA, DFPSO, HGASSO, HHO and VAGWO, respectively. In the simulation experiment, 21 benchmark test functions and 4 engineering example problems are used, and the convergence accuracy, convergence speed, stability and rank sum test of the algorithm are fully compared. The experimental results show that compared with the five latest optimization algorithms of the same type, the proposed algorithm has strong competitiveness in each test index.

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Simulation-Based EDAs for Stochastic Programming Problems

Cited by 3 publications

References 48 publications

Estimation of Distribution Algorithms with Fuzzy Sampling for Stochastic Programming Problems

Estimation of Distribution Algorithms with Fuzzy Sampling for Stochastic Programming Problems

Decision-Making Based on Spherical Linear Diophantine Fuzzy Rough Aggregation Operators and EDAS Method

An adaptive differential evolution algorithm based on data preprocessing method to solve global optimization problem

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