Abstract
The Branch-and-Bound method is known as one of the most powerful but very resource consuming global optimization methods. Parallel and distributed computing can efficiently cope with this issue. The major difficulty in parallel B&B method is the need for dynamic load redistribution. Therefore design and study of load balancing algorithms is a separate and very important research topic. This paper presents a tool for simulating parallel Branchand-Bound method. The simulator allows one to run load balancing algorithms with various numbers of processors, sizes of the search tree, the characteristics of the supercomputer’s interconnect thereby fostering deep study of load distribution strategies. The process of resolution of the optimization problem by B&B method is replaced by a stochastic branching process. Data exchanges are modeled using the concept of logical time. The user friendly graphical interface to the simulator provides efficient visualization and convenient performance analysis.
References
[1] Pardalos P.M., Romeijn E., Tuy H., Recent developments and trends in global optimization, J Comput Appl Math, 2000, 124 (1-2), 209-22810.1016/S0377-0427(00)00425-8Search in Google Scholar
[2] Pardalos. P.M., Resende M. (Eds), Handbook of Applied Optimization Oxford University Press, 2002Search in Google Scholar
[3] Scholz D., Deterministic global optimization: geometric branchand- bound methods and their applications, Springer Science & Business Media, 201110.1007/978-1-4614-1951-8_2Search in Google Scholar
[4] Gendron B., Crainic T.G., Parallel Branch-and-Bound Algorithms: Survey and Synthesis, Oper Res, 1994, 42 (6), 1042-106610.1287/opre.42.6.1042Search in Google Scholar
[5] Willebeek-LeMair M.H., Reeves A.P., Strategies for dynamic load balancing on highly parallel computers, Proc. of Parallel and Distributed Systems, IEEE Transactions on, 1993, 4 (9), 979-99310.1109/71.243526Search in Google Scholar
[6] Lüling R., Monien B., Load balancing for distributed branch & bound algorithms, Proceedings of Sixth International Parallel Processing Symposium, 1992, 543-548Search in Google Scholar
[7] Sergeev Y.D., Strongin R.G., A global minimization algorithm with parallel iterations, Comp Math Math Phys, 1989, 29 (2), 7-1510.1016/0041-5553(89)90002-5Search in Google Scholar
[8] Gergel V.A., Unified Approach to Use of Coprocessors of Various Types for Solving Global Optimization Problems, Proc. of Second International Conference on Mathematics and Computers in Sciences and in Industry (MCSI), IEEE, 13-18Search in Google Scholar
[9] Evtushenko Y., Posypkin M., Sigal I., A framework for parallel large-scale global optimization, Computer Science-Research and Development, 2009, 23 (3-4), 211-21510.1007/s00450-009-0083-7Search in Google Scholar
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