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grey wolf optimization algorithm

در نشریات گروه مواد و متالورژی
تکرار جستجوی کلیدواژه grey wolf optimization algorithm در نشریات گروه فنی و مهندسی
تکرار جستجوی کلیدواژه grey wolf optimization algorithm در مقالات مجلات علمی
  • I. Fozouni Talouki, A. Toloei *
    This study aims to optimize the flight endurance of a 12-passenger turboprop air taxi using two metaheuristic optimization algorithms: Grey Wolf Optimization (GWO) and Ant Colony Optimization (ACO). Initially, the gradient descent method was employed to estimate the aircraft's maximum weight. Subsequently, the aircraft's performance characteristics were utilized as design variables and flight endurance was optimized under specific constraints without altering the physical structure of the aircraft. The optimization process was implemented, and the results were evaluated and compared in terms of performance and efficiency. This research demonstrated that the two mentioned algorithms, utilizing random and collective strategies, were able to enhance the aircraft's efficiency. Additionally, the optimization of flight endurance for three real aircraft—Piper, Beechcraft, and Bombardier—was examined compared to their original endurance. In this context, the Ant Colony Optimization algorithm exhibited better performance than the Grey Wolf Optimization algorithm, which could have a positive impact on flight operations without refueling or the process of finding alternative airports.
    Keywords: Air Taxi, Optimization, Gradient Descent, Grey Wolf Optimization Algorithm, Ant Colony Optimization Algorithm
  • A. Davani Motlagh, M.S. Sadeghian *, A.H. Javid, M.S. Asgari

    With the growth of population, shortage of water, and severe lack of water resources, optimization of reservoirs operation is a principal step in water resource planning and management. Therefore, in the present study, water was optimally allocated for a period from 2010 to 2020 using two simulation-optimization models based on Grey Wolf Optimization algorithm (GWO) and Genetic Algorithm (GA) and WEAP model. System operational indices including volumetric reliability, temporal reliability, vulnerability, and sustainability were used to evaluated the perforemance of optimization algorithms as well as WEAP model. The objective function of resources allocation problem was minimizing sum of the squared relative deficiencies for each month and maximizing reliability over the entire 11-year period. The results showed that optimal allocation solution found by the GWO algorithm with volumetric reliability, vulnerability, and sustainability indices which were 86.93, 0.29, and 21.48%, respectively was better and more suitable than the optimal allocation solution found by GA algorithm (which were 87.12, 0.41, and 21.34%, respectively). Finally, given an increase in the water demands , it is possible to obviate the needs of beneficiaries to an acceptable level and prevent severe draught in dry months through optimizing the use of available resources. According to the calculated indices for the WEAP model, in which volumetric reliability, vulnerability, and sustainability were equal to 87.46, 0.92, and 1.03%, respectively. It can be concluded that the use of optimization algorithm in optimal operation of the dam is more reliable than WEAP model.

    Keywords: optimization, Taleghan Dam, Grey wolf optimization algorithm, Genetic Algorithm, WEAP software
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