Control and Optimization
Mohammad Alsaeedi; Mostafa Tavakolli; Ahmad Abouyee; Khatere Ghorbani Moghadam; Reza Ghanbari
Abstract
In this study, we proposed a novel graph partitioning problem where the edges are characterized by trapezoidal fuzzy numbers. A linear ranking function is employed to establish an order among these fuzzy numbers. We derive the necessary conditions for the existence of an ...
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In this study, we proposed a novel graph partitioning problem where the edges are characterized by trapezoidal fuzzy numbers. A linear ranking function is employed to establish an order among these fuzzy numbers. We derive the necessary conditions for the existence of an optimal solution to this problem. To address the fuzzy graph partitioning problem, we implement and compare the performance of three algorithms: Genetic Algorithm, Tabu Search, and Sequential Least Squares Programming. The algorithms are evaluated based on objective values, computational time, and the number of iterations across multiple numerical examples. Utilizing Dolan-Moré performance profiles, we demonstrate the superiority of our proposed approach relative to existing methods. The findings highlight the robustness and computational efficiency of our methodology, making a meaningful contribution to the advancement of fuzzy graph algorithms and their practical applications.
Control and Optimization
Ali Akbar Sohrabi; Reza Ghanbari; Khatere Ghorbani-Moghadam
Abstract
Project portfolio selection is a critical challenge for many organizations as they often face budget constraints that limit their ability to support all available projects. To address this issue, organizations seek to select a feasible subset of projects that maximizes utility. ...
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Project portfolio selection is a critical challenge for many organizations as they often face budget constraints that limit their ability to support all available projects. To address this issue, organizations seek to select a feasible subset of projects that maximizes utility. While several models for project portfolio selection based on multiple criteria have been proposed, they are typically NP-hard problems. In this study, we propose an efficient Variable Neighborhood Search (VNS) algorithm to solve these problems. Our algorithm includes a formula for computing the difference value of the objective function, which enhances its accuracy and ensures that selected projects meet desired criteria. We demonstrate the effectiveness of our algorithm through rigorous testing and comparison with a genetic algorithm (GA) and CPLEX. The results of the Wilcoxon non-parametric test confirm that our algorithm outperforms both GA and CPLEX in terms of speed and accuracy. Moreover, the variance of the relative error of our algorithm is less than that of GA.
Saeed Nezhadhosein; Reza Ghanbari; Khatere Ghorbani-Moghadam
Abstract
In this paper, we solve a class of nonlinear optimal control problems using a hybrid genetic algorithm (HGA) and a direct method based on the Haar wavelets where the performance index is Bolza-form and the dynamic system is linear. First, we change the problem by using HWs to a static ...
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In this paper, we solve a class of nonlinear optimal control problems using a hybrid genetic algorithm (HGA) and a direct method based on the Haar wavelets where the performance index is Bolza-form and the dynamic system is linear. First, we change the problem by using HWs to a static optimization problem in which the decision variables are the unknown coefficients of the state and control variables in the Haar series. Next, we apply HGA with a local search for higher power of GA in investigating the search space for solving optimization problems. Finally, we give some examples to illustrate the high accuracy of the proposed method.
