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.
Mahdi Ahmadnia; Reza Ghanbari; Khatere Ghorbani-Moghadam
Abstract
In a water distribution network, in order to analyze and determine its parameters such as head and flow rate, we have to solve nonlinear hydraulic equations in each component of the network. Contrary to most of the water distribution network simulation software, solving ...
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In a water distribution network, in order to analyze and determine its parameters such as head and flow rate, we have to solve nonlinear hydraulic equations in each component of the network. Contrary to most of the water distribution network simulation software, solving these equations by using the gradient method, we propose a trust-region method to solve them, as the trust-region method is newer than the gradient method and has well worked in mathematical problems. To prove the effectiveness of our method, we made a comparison between our proposed method and the well-known gradient method. The results show that the trust-region method is convergent in all instances, but the gradient method diverges when the dimension of nonlinear hydraulic equations of water distribution networks increases. In addition, our results convince the solution obtained from the trust-region method is more accurate compared to the gradient method. Thus, using the trust-region method in solving the network equations can lead to a better hydraulic analysis of the network.