Artykuły w czasopismach: „Tabu Search”

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Tuson, A., F. Glover i M. Laguna. "Tabu Search." Journal of the Operational Research Society 50, nr 1 (styczeń 1999): 106. http://dx.doi.org/10.2307/3010402.

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Tuson, A. "Tabu Search". Journal of the Operational Research Society 50, nr 1 (styczeń 1999): 106–7. http://dx.doi.org/10.1057/palgrave.jors.2600048.

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Kallab, Chadi, Samir Haddad, Imad El-Zakhem, Jinane Sayah, Mohamad Chakroun, Nisrine Turkey, Jinan Charafeddine, Hani Hamdan i Wafaa Shakir. "Generic Tabu Search". Journal of Software Engineering and Applications 15, nr 07 (2022): 262–73. http://dx.doi.org/10.4236/jsea.2022.157016.

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de Werra, D., i A. Hertz. "Tabu search techniques". Operations-Research-Spektrum 11, nr 3 (wrzesień 1989): 131–41. http://dx.doi.org/10.1007/bf01720782.

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Stepanenko, Svetlana, i Bernd Engels. "Gradient tabu search". Journal of Computational Chemistry 28, nr 2 (2006): 601–11. http://dx.doi.org/10.1002/jcc.20564.

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Glover, Fred. "Tabu Search: A Tutorial". Interfaces 20, nr 4 (sierpień 1990): 74–94. http://dx.doi.org/10.1287/inte.20.4.74.

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Glover, Fred. "Tabu Search—Part I". ORSA Journal on Computing 1, nr 3 (sierpień 1989): 190–206. http://dx.doi.org/10.1287/ijoc.1.3.190.

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Glover, Fred. "Tabu Search—Part II". ORSA Journal on Computing 2, nr 1 (luty 1990): 4–32. http://dx.doi.org/10.1287/ijoc.2.1.4.

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Battiti, Roberto, i Giampietro Tecchiolli. "The Reactive Tabu Search". ORSA Journal on Computing 6, nr 2 (maj 1994): 126–40. http://dx.doi.org/10.1287/ijoc.6.2.126.

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Glover, Fred. "Tabu search—Uncharted domains". Annals of Operations Research 149, nr 1 (2.12.2006): 89–98. http://dx.doi.org/10.1007/s10479-006-0113-9.

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Zhang, Xing Wen. "An Adaptive Tabu Search Method for Optimizing Procurement Plans under Correlated Demands". Advanced Materials Research 748 (sierpień 2013): 972–75. http://dx.doi.org/10.4028/www.scientific.net/amr.748.972.

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In this paper we develop an adaptive Tabu search method to facilitate retailers in deciding procurement plans while facing correlated demands. The Tabu search method examines mul-tiple candidate moves in a single iteration, and randomly accepts one of the candidate moves based the solution quality and an adaptive probability measure. We vary the acceptance probability so that Tabu search is able to switch between intensification and diversification. The Tabu search method is compared against a Hillclimbing-based construction method, and computational results demonstrate that Tabu search performed significantly better than the construction heuristics.

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Stepanenko, Svetlana, i Bernd Engels. "Tabu Search Based Strategies for Conformational Search†". Journal of Physical Chemistry A 113, nr 43 (29.10.2009): 11699–705. http://dx.doi.org/10.1021/jp9028084.

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Miyamoto, Keisuke, i Keiichiro Yasuda. "Proximate Optimality Principle Based Tabu Search". Proceedings of OPTIS 2004.6 (2004): 67–72. http://dx.doi.org/10.1299/jsmeoptis.2004.6.67.

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Zhang, Jing. "The Ply Stacking Optimization for Composite Laminated Structure: Application of Tabu Search Algorithm". Advanced Materials Research 366 (październik 2011): 514–17. http://dx.doi.org/10.4028/www.scientific.net/amr.366.514.

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In this paper, tabu search algorithm, the laminated overlay has been optimized. The total thickness of the laminated structure under certain circumstances, to composite laminates maximum system reliability indices as the objective function, the composite fiber orientation angle and relative thickness of the optimized design. It is through the local neighborhood search mechanism and the corresponding tabus to avoid circuitous search criteria and standards to cut through the broken tabu excellent release from the state, thereby ensuring a variety of effective exploration in order to ultimately achieve global optimization. This article is laminated overlay design provide a new and efficient framework, the framework of the algorithm relative to the previously used method has obvious advantages: After a limited number of cycles that can converge to satisfactory results, the optimization process shows good robustness of the algorithm, and the method to solve the structural optimization of composite materials and other combinatorial optimization problems provide a new way of thinking. This paper describes the basic idea of tabu search algorithm, composition, processes, principles, and so on.

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Smaili, Ahmad A., Nadim A. Diab i Naji A. Atallah. "Optimum Synthesis of Mechanisms Using Tabu-Gradient Search Algorithm". Journal of Mechanical Design 127, nr 5 (29.11.2004): 917–23. http://dx.doi.org/10.1115/1.1904640.

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A tabu-gradient search is herein presented for optimum synthesis of planar mechanisms. The solution generated by a recency-based, short term memory tabu search is used to start a gradient search to drive the solution ever closer to the global minimum. A brief overview of the tabu-search method is first presented. A tabu-gradient algorithm is then used to synthesize four-bar mechanisms for path generation tasks by way of three examples, including two benchmark examples used before to test other deterministic and intelligent optimization schemes. Compared with the corresponding results generated by other schemes, the tabu-gradient search rendered the most optimal solutions of all.

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Watson, J. P., L. D. Whitley i A. E. Howe. "Linking Search Space Structure, Run-Time Dynamics, and Problem Difficulty: A Step Toward Demystifying Tabu Search". Journal of Artificial Intelligence Research 24 (1.08.2005): 221–61. http://dx.doi.org/10.1613/jair.1576.

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Tabu search is one of the most effective heuristics for locating high-quality solutions to a diverse array of NP-hard combinatorial optimization problems. Despite the widespread success of tabu search, researchers have a poor understanding of many key theoretical aspects of this algorithm, including models of the high-level run-time dynamics and identification of those search space features that influence problem difficulty. We consider these questions in the context of the job-shop scheduling problem (JSP), a domain where tabu search algorithms have been shown to be remarkably effective. Previously, we demonstrated that the mean distance between random local optima and the nearest optimal solution is highly correlated with problem difficulty for a well-known tabu search algorithm for the JSP introduced by Taillard. In this paper, we discuss various shortcomings of this measure and develop a new model of problem difficulty that corrects these deficiencies. We show that Taillard's algorithm can be modeled with high fidelity as a simple variant of a straightforward random walk. The random walk model accounts for nearly all of the variability in the cost required to locate both optimal and sub-optimal solutions to random JSPs, and provides an explanation for differences in the difficulty of random versus structured JSPs. Finally, we discuss and empirically substantiate two novel predictions regarding tabu search algorithm behavior. First, the method for constructing the initial solution is highly unlikely to impact the performance of tabu search. Second, tabu tenure should be selected to be as small as possible while simultaneously avoiding search stagnation; values larger than necessary lead to significant degradations in performance.

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Zhang, Song Tao, Gong Bao Wang i Hui Bo Wang. "Application of Tabu-Genetic Algorithm in Reactive Power Optimization". Advanced Materials Research 1006-1007 (sierpień 2014): 1021–25. http://dx.doi.org/10.4028/www.scientific.net/amr.1006-1007.1021.

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By using tabu search algorithm which has strong local search ability as mutation operator of genetic algorithm, the tabu-genetic algorithm is designed for reactive power optimization in this paper, the strong global search ability of genetic algorithm and strong local search ability of tabu search algorithm is combined, the disadvantage of weak local search ability of genetic algorithm is conquered. Otherwise, the over limit of population is recorded and filtered, to ensure the final individual is under limit and effective. The tabu-genetic algorithm and simple genetic algorithm are used for simulation of IEEE 14-bus system 500 times, the results indicate that the performance of the tabu-genetic algorithm is much better than the simple genetic algorithm, its local search ability is improved obviously, and the active power loss is reduced more.

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Glover, Fred, i Saı̈d Hanafi. "Tabu search and finite convergence". Discrete Applied Mathematics 119, nr 1-2 (czerwiec 2002): 3–36. http://dx.doi.org/10.1016/s0166-218x(01)00263-3.

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Sait, Sadiq M., i Munir M. Zahra. "Tabu search based circuit optimization". Engineering Applications of Artificial Intelligence 15, nr 3-4 (czerwiec 2002): 357–68. http://dx.doi.org/10.1016/s0952-1976(02)00054-4.

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Glover, Fred. "Tabu search — wellsprings and challenges". European Journal of Operational Research 106, nr 2-3 (kwiecień 1998): 221–25. http://dx.doi.org/10.1016/s0377-2217(97)00259-2.

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Dodin, B., A. A. Elimam i E. Rolland. "Tabu search in audit scheduling". European Journal of Operational Research 106, nr 2-3 (kwiecień 1998): 373–92. http://dx.doi.org/10.1016/s0377-2217(97)00280-4.

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Blue, Jennifer A., i Kristin P. Bennett. "Hybrid extreme point tabu search". European Journal of Operational Research 106, nr 2-3 (kwiecień 1998): 676–88. http://dx.doi.org/10.1016/s0377-2217(97)00297-x.

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Alshibli, Mohammad, Ahmed El Sayed, Elif Kongar, Tarek M. Sobh i Surendra M. Gupta. "Disassembly Sequencing Using Tabu Search". Journal of Intelligent & Robotic Systems 82, nr 1 (14.10.2015): 69–79. http://dx.doi.org/10.1007/s10846-015-0289-9.

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Woodruff, David L., i Eitan Zemel. "Hashing vectors for tabu search". Annals of Operations Research 41, nr 2 (czerwiec 1993): 123–37. http://dx.doi.org/10.1007/bf02022565.

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Bland, J. A., i G. P. Dawson. "Tabu search and design optimization". Computer-Aided Design 23, nr 3 (kwiecień 1991): 195–201. http://dx.doi.org/10.1016/0010-4485(91)90089-f.

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Mantawy, A. H., Y. L. Abdel-Magid i S. Z. Selim. "Unit commitment by tabu search". IEE Proceedings - Generation, Transmission and Distribution 145, nr 1 (1998): 56. http://dx.doi.org/10.1049/ip-gtd:19981681.

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Gupta, D. K. "Using Tabu search in CSPS". Korean Journal of Computational & Applied Mathematics 8, nr 1 (styczeń 2001): 181–97. http://dx.doi.org/10.1007/bf03011631.

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Sun, Dake, Li Lin i Rajan Batta. "Cell formation using tabu search". Computers & Industrial Engineering 28, nr 3 (lipiec 1995): 485–94. http://dx.doi.org/10.1016/0360-8352(94)00185-p.

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Rolland, Erik, Hasan Pirkul i Fred Glover. "Tabu search for graph partitioning". Annals of Operations Research 63, nr 2 (kwiecień 1996): 209–32. http://dx.doi.org/10.1007/bf02125455.

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Hageman, J. A., M. Streppel, R. Wehrens i L. M. C. Buydens. "Wavelength selection with Tabu Search". Journal of Chemometrics 17, nr 8-9 (2003): 427–37. http://dx.doi.org/10.1002/cem.782.

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ESWARAMURTHY, V. P., i A. TAMILARASI. "TABU SEARCH STRATEGIES FOR SOLVING JOB SHOP SCHEDULING PROBLEMS". Journal of Advanced Manufacturing Systems 06, nr 01 (czerwiec 2007): 59–75. http://dx.doi.org/10.1142/s0219686707000899.

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Scheduling is considered to be a major task to improve shop-floor productivity. The job shop problem is under this category and is combinatorial in nature. Research on optimization of job shop problem is one of the most significant and promising areas of optimization. This paper presents an application of the global optimization technique called tabu search to the job shop scheduling problem. An easily implementable algorithm for the problem of finding a minimum makespan in a job shop is presented. The method employs critical paths and blocks of operations. The algorithm is based on a specific neighborhood and dynamic tabu length strategies. A neighboring solution is a solution obtained by permuting two successive and critical operations that use the same machine. Tabus are useful to help the search move away from previously visited portions of the search space and thus perform more extensive exploration. Experiments using well-known bench mark problems are carried out to check the performance of the proposed method.

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Awad, Fouad H., Ali Al-kubaisi i Maha Mahmood. "Large-scale timetabling problems with adaptive tabu search". Journal of Intelligent Systems 31, nr 1 (1.01.2022): 168–76. http://dx.doi.org/10.1515/jisys-2022-0003.

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Abstract Timetabling problems are specific types of scheduling problems that deal with assigning certain events to the timeslots. This assigning is subject to certain hard constraints that should be achieved to get a feasible timetable and soft constraints that must meet as many as possible during forming a feasible schedule. This paper introduces an adaptive tabu search. Eleven benchmark datasets of the year 2002 are applied to show the effectiveness of the introduced algorithm. These datasets consist of 5-small, 5-medium, and 1-large dataset. As compared to other methods from previous works, the proposed algorithm produces excellent timetables, in comparison with the algorithms as well as the current results, the mathematical results showed the high effectiveness of the suggested algorithm. It has a minor deficit on the medium or the small problem adaptive Tabu, and the tabu search relies on the tabu list and penalty cost when the change in the penalty cost is checked; if it is still unchanged for the period of iterations (1,000 iterations), the tabu list reduces automatically by (−2); furthermore, the tabu list remains constant.

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Grebner, Christoph, Johannes Becker, Svetlana Stepanenko i Bernd Engels. "Efficiency of tabu-search-based conformational search algorithms". Journal of Computational Chemistry 32, nr 10 (3.05.2011): 2245–53. http://dx.doi.org/10.1002/jcc.21807.

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BEAUSOLEIL, RICARDO P. "ASSOCIATIVE CLASSIFICATION WITH MULTIOBJECTIVE TABU SEARCH". Revista de Matemática: Teoría y Aplicaciones 27, nr 2 (23.06.2020): 353–74. http://dx.doi.org/10.15517/rmta.v27i2.42438.

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This paper presents an application of Tabu Search algorithm to association rule mining. We focus our attention specifically on classification rule mining, often called associative classification, where the consequent part of each rule is a class label. Our approach is based on seek a rule set handled as an individual. A Tabu search algorithm is used to search for Pareto-optimal rule sets with respect to some evaluation criteria such as accuracy and complexity. We apply a called Apriori algorithm for an association rules mining and then a multiobjective tabu search to a selection rules. We report experimental results where the effect of our multiobjective selection rules is examined for some well-known benchmark data sets from the UCI machine learning repository.

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Sulistiono, Sulistiono, i Noor Saif Muhammad Mussafi. "Rancang Bangun Vehicle Routing Problem Menggunakan Algoritma Tabu Search". Jurnal Fourier 4, nr 2 (1.10.2015): 113. http://dx.doi.org/10.14421/fourier.2015.42.113-122.

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Pendistribusian produk berperan penting dalam dunia industri. Salah satu usaha yang dapat dilakukan perusahaan untuk mengoptimalkan pendistribusian produk adalah meminimalkan biaya tranportasi melalui penentuan rute optimal kendaraan yang disebut dengan VRP (Vehicle Routing Problem). Tujuan dari VRP adalah menentukan rute optimal yaitu rute dengan jarak minimum untuk mendistribusikan produk kepada konsumen. Salah satu variasi VRP adalah Capacitated Vehicle Routing Problem (CVRP), yaitu VRP dengan kendala kapasitas kendaraan. Kasus CVRP tersebut dapat diselesaikan dengan menggunakan Algoritma Tabu Search. Cara kerja Algoritma Tabu Search dimulai dengan penentuan initial solution menggunakan Nearest Neighbor, evaluasi move menggunakan metode 2-Opt, Relocated, dan Exchange, update Tabu List, kemudian apabila kriteria pemberhentian terpenuhi maka proses Algoritma Tabu Search berhenti jika tidak, maka kembali pada evaluasi move. Proses perhitungan Algoritma Tabu Search dilakukan secara manual dan rancang bangun menggunakan MATLAB pada PT Sinergi Bio Natural. Berdasarkan proses perhitungan manual dan rancang bangun diperoleh dua solusi optimal yaitu rute dengan jarak terpendek dengan total jarak optimal sebesar 101,1 km.

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Lenin, K. "REDUCTION OF ACTIVE POWER LOSS BY IMPROVED TABU SEARCH ALGORITHM". International Journal of Research -GRANTHAALAYAH 6, nr 7 (31.07.2018): 1–9. http://dx.doi.org/10.29121/granthaalayah.v6.i7.2018.1277.

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In this paper, an Improved Tabu Search (ITS) algorithm has been proposed to solve the optimal reactive power problem. In this work Tabu Search- has been hybridized with Simulated Annealing algorithm to solve the optimal reactive power problem. Hybridization of these two algorithms improves the exploration & exploitation capabilities during the search. Proposed Improved Tabu Search (ITS) algorithm has been tested in Standard IEEE 57,118 bus systems & real power loss has been comparatively reduced with voltage profiles are within the limits.

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Jiang, Qiu, Zhou Kang, Shao Kai i Zhao Juan. "VRP Research Based on Hybrid Artificial Intelligent Algorithm". Advanced Materials Research 962-965 (czerwiec 2014): 2201–5. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2201.

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In order to find a better way to solve the Vehicle Routing Problem ,this paper puts forward a new way. In this paper combines Artificial Fish Swarm Algorithm and Tabu Search Algorithm to solve the Vehicle Routing Problem, at first use Artificial Fish Swarm Algorithm to get the phase solution as the initial solution of Tabu Search Algorithm, and then get the best solution of the problem by Tabu Search Algorithm.Experiments show this has a good search ability to Vehicle Routing Problem.

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Riswan, A. Sahari i D. Lusiyanti. "Penentuan Rute Terpendek Pendistribusian Tabung Gas Lpg 3 Kg Pt. Fega Gas Palu Pratama Menggunakan Algoritma Tabu Search". JURNAL ILMIAH MATEMATIKA DAN TERAPAN 16, nr 2 (31.01.2020): 221–29. http://dx.doi.org/10.22487/2540766x.2019.v16.i2.15004.

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ABSTRACTDistribution is one of the important tools in the company business activities. The problem that is often occurred indistribution is the determination of the shortest route. The purpose of this study is optimitation distribution route of3 kg LPG gas cylinders which is carried out by PT. Fega Gas Palu Pratama in Palu City, considering that thiscompany has not used a particular method in determining the distribution route of 3 kg LPG gas cylinders. Themethod used in this study is the Tabu Search algorithm. The algorithm of the Tabu Search method are of follows,first by determine the initial solution using the closest Nearest Neighbor, determine alternatives by exchange 2points in the solution, evaluate alternative solutions, determine a new optimum solution, update the Tabu List, thenwhen the termination criteria are obtained then the Tabu Search algorithm will stop otherwise it will revert toexchanging 2 points evaluation. The process of calculating the Tabu Search algorithm is conducted manually andbuilt using MATLAB. Based on the research that has been done, it is obtained that the shortest, more efficient routeis 21.91 km which has reduction of 7.26 km from the initial route 29.17 km.Keywords : Algorithm Tabu Search, Distribution, Shortest Route.

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Icasia, Gabriella, Raras Tyasnurita i Etria Sepwardhani Purba. "Application of Heuristic Combinations in Hyper-Heuristic Framework for Exam Scheduling Problems". Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, nr 4 (17.08.2020): 664–71. http://dx.doi.org/10.29207/resti.v4i4.2066.

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Examination Timetabling Problem is one of the optimization and combinatorial problems. It is proved to be a non-deterministic polynomial (NP)-hard problem. On a large scale of data, the examination timetabling problem becomes a complex problem and takes time if it solved manually. Therefore, heuristics exist to provide reasonable enough solutions and meet the constraints of the problem. In this study, a real-world dataset of Examination Timetabling (Toronto dataset) is solved using a Hill-Climbing and Tabu Search algorithm. Different from the approach in the literature, Tabu Search is a meta-heuristic method, but we implemented a Tabu Search within the hyper-heuristic framework. The main objective of this study is to provide a better understanding of the application of Hill-Climbing and Tabu Search in hyper-heuristics to solve timetabling problems. The results of the experiments show that Hill-Climbing and Tabu Search succeeded in automating the timetabling process by reducing the penalty 18-65% from the initial solution. Besides, we tested the algorithms within 10,000-100,000 iterations, and the results were compared with a previous study. Most of the solutions generated from this experiment are better compared to the previous study that also used Tabu Search algorithm.

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Salhi, S. "Defining tabu list size and aspiration criterion within tabu search methods". Computers & Operations Research 29, nr 1 (styczeń 2002): 67–86. http://dx.doi.org/10.1016/s0305-0548(00)00062-9.

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Ge, Yan, Aimin Wang, Zijin Zhao i Jieran Ye. "A Tabu-Genetic Hybrid Search Algorithm for Job-shop Scheduling Problem". E3S Web of Conferences 95 (2019): 04007. http://dx.doi.org/10.1051/e3sconf/20199504007.

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To deal with the job-shop scheduling problem (JSP), a tabu-genetic hybrid search algorithm is proposed. The algorithm generates several initial solutions distributed in the whole solution space for tabu search by genetic algorithm, which avoids the over-dependence on the initial solution of tabu search algorithm. With the mechanism mentioned above, the algorithm proposed has both global search performance of genetic algorithm and local search performance of labu search algorithm. Finally, a program was developed with the achral data of FT (10x 10). to verify the feasibility and effectiveness of the algorithm. The result shows that the algorithm achieves satisfactory results in all indexes mentioned above.

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Kanazawa, Takahiko, i Keiichiro Yasuda. "Proximate Optimality Principle Based Tabu Search". IEEJ Transactions on Electronics, Information and Systems 124, nr 3 (2004): 912–20. http://dx.doi.org/10.1541/ieejeiss.124.912.

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Islam, Tanzila, Zunayed Shahriar, Mohammad Anower Perves i Monirul Hasan. "University Timetable Generator Using Tabu Search". Journal of Computer and Communications 04, nr 16 (2016): 28–37. http://dx.doi.org/10.4236/jcc.2016.416003.

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Dewil, Reginald, Pieter Vansteenwegen i Dirk Cattrysse. "Cutting Path Optimization Using Tabu Search". Key Engineering Materials 473 (marzec 2011): 739–48. http://dx.doi.org/10.4028/www.scientific.net/kem.473.739.

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This paper deals with generating paths for cutting irregular parts nested on a metal sheet. The objective is to minimize the total non-cutting time for the cutter head starting at a known location, cutting all the required elements and returning to the known location. In contrast to most literature on this topic, a part does not have to be cut at once. If this reduces the total non-cutting time, it is possible to cut a number of elements on a given part, then cut other parts and then return later to finish cutting the given part. The problem is modeled as a generalized traveling salesperson problem with special precedence constraints. An initial feasible solution is generated and improved by local moves embedded in a tabu search framework. The proposed algorithm shows promising results in comparison with a commercial software package on a limited set of test cases.

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Emmert, John M., i Dinesh K. Bhatia. "Two-dimensional Placement Using Tabu Search". VLSI Design 12, nr 1 (1.01.2001): 13–23. http://dx.doi.org/10.1155/2001/92781.

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Search based placement of modules is an important problem in VLSI design. It is always desired that the search should converge quickly to a high quality solution. This paper presents a tabu search based optimization technique to place modules on a regular two-dimensional array. The goal of the technique is to speed up the placement process. The technique is based on a two-step placement strategy. The first step is targeted toward improving circuit routability and the second step addresses circuit performance. The technique is demonstrated through placement of several benchmark circuits on academic as well as commercial FPGAs. Results are compared to placements generated by commercial CAE tools and published simulated annealing based techniques. The tabu search technique compares favorably to published simulated annealing based techniques, and it demonstrates an average execution time speedup of 20 with no impact on quality of results when compared to commercial tools.

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Cao, Dong, i Mingyuan Chen. "Parallel flowshop scheduling using Tabu search". International Journal of Production Research 41, nr 13 (styczeń 2003): 3059–73. http://dx.doi.org/10.1080/0020754031000106443.

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Zhang, Hongbin, i Guangyu Sun. "Feature selection using tabu search method". Pattern Recognition 35, nr 3 (marzec 2002): 701–11. http://dx.doi.org/10.1016/s0031-3203(01)00046-2.

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Verhoeven, M. G. A. "Tabu search for resource-constrained scheduling". European Journal of Operational Research 106, nr 2-3 (kwiecień 1998): 266–76. http://dx.doi.org/10.1016/s0377-2217(98)80001-5.

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Chelouah, Rachid, i Patrick Siarry. "Tabu Search applied to global optimization". European Journal of Operational Research 123, nr 2 (czerwiec 2000): 256–70. http://dx.doi.org/10.1016/s0377-2217(99)00255-6.

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Li, Chunguang, Hongbing Xu, Xiaofeng Liao i Juebang Yu. "Tabu search for CNN template learning". Neurocomputing 51 (kwiecień 2003): 475–79. http://dx.doi.org/10.1016/s0925-2312(02)00699-9.

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