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Showing 5 results for Darvishi
Dr. Davood Darvishi,
Volume 9, Issue 1 (7-2018)
Abstract
Linear programming problems with interval grey numbers have recently attracted some interest. In this paper, we study linear programs in which right hand sides are interval grey numbers. This model is relevant when uncertain and inaccurate factors make difficult the assignment of a single value to each right hand side. Some methods have been developed for solving these problems. In this paper, we propose a new approach for solving interval grey number linear programming problems is introduced without converting them to classical linear programming problems. A numerical example is provided to illustrate the proposed approach.
Dr Hamid Reza Yousefzadeh, Dr Davood Darvishi, Mrs Arezoo Sayadi Salar,
Volume 11, Issue 1 (9-2020)
Abstract
Ant colony optimization (ACOR) is a meta-heuristic algorithm for solving continuous optimization
problems (MOPs). In the last decades, some improved versions of ACOR have been proposed.
The UACOR is a unified version of ACOR that is designed for continuous domains. By adjusting
some specified components of the UACOR, some new versions of ACOR can be deduced. By doing
that, it becomes more practical for different types of MOPs. Based on the nature of meta-heuristic
algorithms, the performance of meta-heuristic algorithms are depends on the exploitation and
exploration, which are known as the two useful factors to generate solutions with different
qualities. Since all the meta-heuristic algorithms with random parameters use the probability
functions to generate the random numbers and as a result, there is no any control over the
amount of diversity; hence in this paper, by using the best parameters of UACOR and making
some other changes, we propose a new version of ACOR to increase the efficiency of UACOR.
These changes include using chaotic sequences to generate various random sequences and also
using a new local search to increase the quality of the solution. The proposed algorithm, the two
standard versions of UACOR and the genetic algorithm are tested on the CEC05 benchmark
functions, and then numerical results are reported. Furthermore, we apply these four algorithms
to solve the utilization of complex multi-reservoir systems, the three-reservoir system of Karkheh
dam, as a case study. The numerical results confirm the superiority of proposed algorithm over
the three other algorithms.
Jafar Pourmahmoud, Mahdi Eini, Davood Darvishi Salokolaei, Saeid Mehrabian,
Volume 14, Issue 2 (12-2023)
Abstract
In the evaluation of decision making units with classical models of data envelopment analysis, it is assumed that the factors are deterministic. In some decision-making problems, the amount of inputs or outputs of the units is not exactly known and it is a three-parameter interval in grey form. In this case, it is recommended to choose the factors from their center of gravity. In the classic models of data envelopment analysis, all factors are also considered desirable, but in real problems there are undesirable factors too which cannot be used to evaluate problems with undesirable inputs and undesirable outputs. In this paper, a model is presented for calculating the efficiency of decision making units in the presence of the center of gravity of undesirable three-parameter interval grey undesirable factors based on the combination of strong and weak disposability principles. To this end, the proposed method is discussed with a practical example.
Dr Narjes Amiri, Dr Seyed Hadi Nasseri, Dr Davood Darvishi,
Volume 16, Issue 1 (3-2025)
Abstract
This article examines and analyzes fuzzy linear programming models and techniques. Since its emergence in the 1970s, fuzzy linear programming has addressed the growing complexity of decision-making problems in the real world that occur in uncertain and dynamic environments. Fuzzy linear programming is based on fuzzy set theory and traditional linear programming theory, covering a wide range of theoretical research and algorithmic advancements. Unlike traditional linear programming, fuzzy linear programming does not have a single model, as fuzziness can manifest in various aspects of the model. This paper focuses on solving fuzzy linear programming problems that include inequality constraints. The suggested method employs Yager's linear fuzzy relation, providing a simple and effective way to manage the complexities associated with fuzzy parameters.
Dr Mohammad Mohammadi, Dr Davood Darvishi,
Volume 16, Issue 1 (3-2025)
Abstract
Prostate cancer is the most common cancer in men and the second leading cause of cancer-related death worldwide. Over the years, researchers from various fields, beyond medicine, have sought to expand their understanding of the disease to develop more effective treatments. Treatment planning for high-dose-rate (HDR) brachytherapy involves designing the trajectory of the radiation source to deliver sufficient doses to the target area while minimizing exposure to surrounding organs at risk (OAR) within clinically safe limits. Since the exact tumor volume is not known, the model uses gray numbers instead of tumor volume, which provides more accurate results.
In this study, four powerful multi-objective evolutionary algorithms (MOEAs) NSGA1-II, PESA2-II, SPEA3-II, and MOPSO4 are employed. Instead of yielding a single best solution, these algorithms produce a set of Pareto-optimal solutions, each representing a trade-off where no one solution is definitively better than the rest. However, they demonstrate improved performance compared to other optimization methods. The results show that the MOPSO algorithm performs better than the other three powerful algorithms in terms of solution quality and maintaining diversity among solutions.
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