Parameter structure identification using tabu search and simulated annealing |
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| Institution: | 1. Department of Geology, University of Alabama, Tuscaloosa, Alabama, USA;2. Department of Mathematics, University of Alabama, Tuscaloosa, Alabama, USA;1. Department of Mathematics, University of Science and Technology of China, Hefei 230026, PR China;2. Department of Mathematics and Technologies of Programming, Francisk Skorina Gomel State University, Gomel 246019, Belarus;1. Chemical Engineering Department, College of Chemical and Petroleum Engineering, Shiraz University, Shiraz, Iran;2. Mechanical Engineering Department, Shiraz University, Shiraz, Iran;3. School of Engineering, University of Newcastle, Callaghan, NSW 2308, Australia;4. School of Environmental and Life Sciences (Chemistry), University of Newcastle, Callaghan, NSW 2308, Australia;5. Chemistry Department, College of Sciences, Shiraz University, Shiraz, Iran;1. Department of Mechanical Engineering, Capital University of Science & Technology, Islamabad, Pakistan;2. NUST Business School (NBS), National University of Sciences and Technology (NUST), Islamabad, Pakistan;3. Department of Mechanical Engineering, Technische Universitat (TU) Dresden, Germany;1. Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China;2. DeGroote School of Business, McMaster University, Hamilton, Ontario L8S4M4, Canada;3. Engineering Research Center of MES Technology for Iron & Steel Production, Ministry of Education, Beijing 100083, China |
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| Abstract: | In groundwater modeling the identification of an optimal flow or transport parameter that varies spatially should include both the values and structure of the parameter. However, most existing techniques for parameter identification only consider the parameter values. In this study, the problem of identifying optimal parameter structure is treated as a large combinatorial optimization problem. Two recently developed heuristic search techniques, simulated annealing and tabu search, are used to solve the large combinatorial optimization problem. The effectiveness and flexibility of these two techniques are evaluated and compared with simple grid search and descent search, using preliminary results from one-dimensional examples. Among the techniques examined in this paper, tabu search performs extremely well in terms of the total number of function evaluations required. |
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