Multi-objective tabu search using a multinomial probability mass function |
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| Affiliation: | 1. Management Information Systems, Penn State Berks-Lehigh Valley College, Reading, PA 19610, USA;2. Department of Industrial and Systems Engineering, 207 Dunstan Hall, Auburn University, Auburn, AL 36849, USA;3. Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA;1. Division of Intelligent and Biomechanical Systems, State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China;2. Department of Automotive Engineering, Tsinghua University, Beijing 100084, China;1. School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000, Belgrade, Serbia;2. Innovation Center of School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000, Belgrade, Serbia;3. Mihajlo Pupin Institute, University of Belgrade, Volgina 15, 11000, Belgrade, Serbia;1. University of Amsterdam, Amsterdam, The Netherlands;2. ITMO University, Saint Petersburg, Russia;3. Deltares, Delft, The Netherlands;4. Saint Petersburg State Polytechnic University, Russia;1. Özyeğin University, Department of Industrial Engineering, Nişantepe Mah. Orman Sk. Çekmeköy, İstanbul, Turkey;2. Clemson University, Department of Industrial Engineering, 100-B Freeman Hall, Clemson, SC 29634-0920, United States;3. Rice University, Computational and Applied Mathematics Department, 6100 Main St. - MS 134, Houston, TX 77005-1892, United States |
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| Abstract: | A tabu search approach to solve multi-objective combinatorial optimization problems is developed in this paper. This procedure selects an objective to become active for a given iteration with a multinomial probability mass function. The selection step eliminates two major problems of simple multi-objective methods, a priori weighting and scaling of objectives. Comparison of results on an NP-hard combinatorial problem with a previously published multi-objective tabu search approach and with a deterministic version of this approach shows that the multinomial approach is effective, tractable and flexible. |
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