Abstract
The Multidimensional Assignment Problem (MAP) is an NP-hard combinatorial optimization problem occurring in many applications, such as data association, target tracking, and resource planning. As many solution approaches to this problem rely, at least partly, on local neighborhood search algorithms, the number of local minima affects solution difficulty for these algorithms. This paper investigates the expected number of local minima in randomly generated instances of the MAP. Lower and upper bounds are developed for the expected number of local minima, E[M], in an MAP with iid standard normal coefficients. In a special case of the MAP, a closed-form expression for E[M] is obtained when costs are iid continuous random variables. These results imply that the expected number of local minima is exponential in the number of dimensions of the MAP. Our numerical experiments indicate that larger numbers of local minima have a statistically significant negative effect on the quality of solutions produced by several heuristic algorithms that involve local neighborhood search.
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References
Aiex R, Resende M, Pardalos PM, Toraldo G (2005) GRASP with path relinking for the three-index assignment problem. INFORMS J Comput 17(2):224–247
Andrijich SM, Caccetta L (2001) Solving the multisensor data association problem. Nonlinear Analysis 47:5525–5536.
Angel E, Zissimopoulos V (2001) On the landscape ruggedness of the quadratic assignment problem. Theor Comput Sci 263:159–172
Balas E, Saltzman MJ (1991) An algorithm for the three-index assignment problem. Oper Res 39:150–161
Clemons W, Grundel D, Jeffcoat D (2003) Applying simulated annealing on the multidimensional assignment problem. In: Proceedings of the 2nd cooperative control and optimization conference
Feo TA, Resende MGC (1989) A probabilistic heuristic for a computationally difficult set covering problem. Oper Res Lett 8:67–71
Feo TA, Resende MGC (1995) Greedy randomized adaptive search procedures. J Glob Optim 6:109–133
Festa P, Resende M (2001) GRASP: An annotated bibliography. In: Hansen P, Ribeiro CC (eds.) Essays and surveys on metaheuristics. Kluwer Academic Publishers, pp 325–367
Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. WH Freeman and Company
Gosavi A (2003) Simulation-based optimization: parametric optimization techniques and reinforcement learning. Kluwer Academic Publishers.
Grundel DA, Oliveira CAS, Pardalos PM, Pasiliao EL (2005) Asymptotic results for random multidimensional assignment problems. Comput Optim Appl 31(3):275–293
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680
Law A, Kelton W (1991) Simulation modeling and analysis, 2nd edn. McGraw-Hill, Inc., New York
Lin S, Kernighan BW (1973) An effective heuristic algorithm for the traveling salesman problem. Oper Res 21:498–516
Metropolis N, Rosenbluth A, Rosenbluth M, Teller A, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087–1092
Murphey R, Pardalos P, Pitsoulis L (1998) A greedy randomized adaptive search procedure for the multitarget multisensor tracking problem. In: DIMACS Series vol 40. American Mathematical Society, pp 277–302.
Olver FW (1997) Asymptotics and special functions. 2nd edn. AK Peters Ltd, Wellesley, MA
Palmer R (1991) Optimization on rugged landscapes. In: Perelson A, Kauffman S (eds), Molecular evolution on rugged ladscapes: proteins, RNA, and the immune system. Addison Wesley, Redwood City, CA, pp 3–25
Pardalos PM, Pitsoulis L (eds.) (2000) Nonlinear assignment: problems, algorithms and applicationssignment: problems, algorithms and applications. Kluwer Academic Publishers, Dordrecht
Pasiliao EL (2003) Algorithms for multidimensional assignment problems. PhD. thesis, Department of Industrial and Systems Engineering, University of Florida
Pierskalla W (1968) The multidimensional assignment problem. Operations Research 16:422–431
Slepian D (1962) The one-sided barrier problem for gaussian noise. Bell Syst Techn J 41:463–501
Stanley R (1986) Enumerative combinatorics, Wadsworth & Brooks, Belmont, CA
Tong YL (1990) The multivariate normal distribution. Springer Verlag, Berlin
Veenman CJ, Hendriks EA, Reinders MJT (1998) A fast and robust point tracking algorithm. In: Proceedings of the fifth IEEE international conference on image processing Chicago, USA, pp 653–657
Yong L, Pardalos PM (1992) Generating quadratic assignment test problems with known optimal permutations. Comput Optim Appl 1(2):163–184
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Partially supported by the NSF grant DMI-0457473.
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Grundel, D.A., Krokhmal, P.A., Oliveira, C.A.S. et al. On the number of local minima for the multidimensional assignment problem. J Comb Optim 13, 1–18 (2007). https://doi.org/10.1007/s10878-006-9009-5
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DOI: https://doi.org/10.1007/s10878-006-9009-5