Abstract
The paper describes the design and implementation of BNB-Solver, an object-oriented framework for discrete and continuous parallel global optimization. The framework supports exact branch-and-bound algorithms, heuristic methods and hybrid approaches. BNB-Solver provides a support for distributed and shared memory architectures. The implementation for distributed memory machines is based on MPI and thus can run on almost any computational cluster. In order to take advantages of multicore processors we provide a separate multi-threaded implementation for shared memory platforms. We introduce a novel collaborative scheme for combining exact and heuristic search methods that provides the support for sophisticated parallel heuristics and convenient balancing between exact and heuristic methods. In the experimental results section we discuss a nonlinear programming solver and a highly efficient knapsack solver that significantly outperforms existing parallel implementations.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alba E, Almeida F, Blesa M et al (2006) Efficient parallel LAN/WAN algorithms for optimization. The MALLBA project. Parall Comput 32(5-6):415–440
Ananth G, Kumar V, Pardalos P (1993) Parallel processing of discrete optimization problems. Encycl Microcomput 13:129–147
Casado L, Martínez J, García I et al (2008) Branch-and-Bound interval global optimization on shared memory multiprocessors. Optim Methods Softw 23(5):689–701
Crainic TG, Le Cun B, Roucairol C (2006) Parallel branch and bound algorithms. In: Parallel Combinatorial Optimization, Chap 1, John Wiley & Sons, Hoboken, NJ, pp 1–28
Crainic TG, Toulouse M (2002) Parallel Strategies for Metaheuristics. In: Glover F, Kochenberger G (eds) State-of-the-Art Hand-book in Metaheuristics, Kluwer Academic Publishers, Dordrecht, pp 475–513
Coello Coello CA, Mezura-Montes E (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inf 16(3):193–203
Drepper U, Molnar I (2005) The Native POSIX Thread Library for Linux. http://people.redhat.com/drepper/nptl-design.pdf. Last access: 13 May 2009
Eckstein J, Philips C, Hart W (2006) PEBBL 1.0 User Guide. RUTCOR Research Report RRR 19-2006. http://rutcor.rutgers.edu/pub/rrr/reports2006/19_2006.ps. Last access: 13 May 2009
Evtushenko Y (1971) Numerical methods for finding global extreme (case of a non-uniform mesh). USSR Comput Maths Math Phys 11(6):38–54
Kellerer H, Pferschy U, Pisinger D (2004) Knapsack Problems. Springer, Berlin
Posypkin M, Sigal I (2008) A combined parallel algorithm for solving the knapsack problem. J Comput Syst Sci Int 47(4):543–551
Ralphs T (2006) Parallel branch and cut. In: Parallel Combinatorial Optimization. John Wiley & Sons, Hoboken, NJ, pp 53–103
Snir M, Otto S, Huss-Lederman S, Walker D, Dongarra J (1996) MPI: The Complete Reference. MIT Press, Boston
Tschöke S, Holthöfer N (1995) A new parallel approach to the constrained two-dimensional cutting stock problem. In: Parallel Algorithms for Irregularly Structured Problems, LNCS 980, pp 285–300, Springer, London
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Evtushenko, Y., Posypkin, M. & Sigal, I. A framework for parallel large-scale global optimization . Comp. Sci. Res. Dev. 23, 211–215 (2009). https://doi.org/10.1007/s00450-009-0083-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00450-009-0083-7