Application of the gravity search algorithm to multi-reservoir operation optimization |
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| Institution: | 1. Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Tehran, Iran;2. Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Tehran, Iran;3. Department of Geography, University of California, Santa Barbara, CA, United States;1. Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Brinellvägen 23, 10044, Stockholm, Sweden;2. Policy Wing, Ministry of Petroleum and Natural Resources, Government of Pakistan, Pakistan;3. Hydrogeology Group, Department of Geotechnical Engineering and Geosciences, Universitat Politècnica de Catalunya, UPC-BarcelonaTech, 08034 Barcelona, Spain;4. School of Mechanical, Aerospace and Civil Engineering, University of Manchester, United Kingdom;1. Department of Mathematics, and College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA;2. Department of Mathematics and Program in Applied Mathematics, University of Arizona, Tucson AZ 85721, USA |
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| Abstract: | Complexities in river discharge, variable rainfall regime, and drought severity merit the use of advanced optimization tools in multi-reservoir operation. The gravity search algorithm (GSA) is an evolutionary optimization algorithm based on the law of gravity and mass interactions. This paper explores the GSA's efficacy for solving benchmark functions, single reservoir, and four-reservoir operation optimization problems. The GSA's solutions are compared with those of the well-known genetic algorithm (GA) in three optimization problems. The results show that the GSA's results are closer to the optimal solutions than the GA's results in minimizing the benchmark functions. The average values of the objective function equal 1.218 and 1.746 with the GSA and GA, respectively, in solving the single-reservoir hydropower operation problem. The global solution equals 1.213 for this same problem. The GSA converged to 99.97% of the global solution in its average-performing history, while the GA converged to 97% of the global solution of the four-reservoir problem. Requiring fewer parameters for algorithmic implementation and reaching the optimal solution in fewer number of functional evaluations are additional advantages of the GSA over the GA. The results of the three optimization problems demonstrate a superior performance of the GSA for optimizing general mathematical problems and the operation of reservoir systems. |
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