Abstract
To practically solve NP-hard combinatorial optimizationproblems, local search algorithms and their parallel implementations on PVM or MPI have been frequently discussed. Since a huge number of neighbors may be examined to discover a locally optimal neighbor in each of local search calls, many of parallelization schemes, excluding so-called the multi-start parallel scheme, try to extract parallelism from a local search by distributing the examinations of neighbors to processors. However, in straightforward implementations, when the next local search starts, all the processors will be assigned to the neighbors of the latest solution, and the results of all (but one) examinations in the previous local search are thus discarded in vain, despite that they would contain useful information on further search.
This paper explores the possibility of extracting information even from unsuccessful neighbor examinations in a systematic way to boost parallel local search algorithms. Our key concept is neighborhood composition. We demonstrate how this idea improves parallel implementations on PVM, by taking as examples well-known local search algorithms for the Traveling Salesman Problem.
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References
Aarts, E., Lenstra, J.K.: Local Search in Combinatorial Optimization. John Wiley, Chichester (1997)
Asahiro, Y., Ishibashi, M., Yamashita, M.: Independent and Cooperative Parallel Search Methods for the Generalized Assignment Problem. Optimization Methods and Software 18(2), 129–141 (2003)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, New York (1979)
Gill, P.E., Murray, W., Wright, M.H.: Practical Optimization. Academic Press, London (1981)
Helsgaun, K.: An Effective Implementation of the Lin-Kernighan Traveling Salesman Heuristic. European Journal of Operational Research 126(1), 106–130 (2000)
Lawler, E.L., Lenstra, J.K., RinnooyKan, A.H.G., Shmoys, D.B.: TheTraveling Salesman Problem, A Guided Tour of Combinatorial Optimization. John Wiley and Sons, Chichester (1985)
Vandewalle, S., Driesschie, R.V., Piessens, R.: The parallel performance of standard parabolic marching schemes. International Journals of Super Scomputing 3(1), 1–29 (1991)
Vazirani, V.V.: Approximation Algorithms. Springer, Heidelberg (2000)
Wagner, A.S., Sreekantaswamy, H.V., Chanson, S.T.: Performance Models for the Processor Farm Paradigm. IEEE Transactions on Parallel and Distributed Systems 8(5), 475–489 (1997)
Yagiura, M., Ibaraki, T.: On Metaheuristic Algorithms for Combinatorial Optimization Problems. Systems and Computers in Japan 32(3), 33–55 (2001)
Yagiura, M., Ibaraki, T., Glover, F.: An Ejection Chain Approach for the Generalized Assignment Problem. INFORMS Journal on Computing 16, 133–151 (2004)
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Handa, Y., Ono, H., Sadakane, K., Yamashita, M. (2004). Neighborhood Composition: A Parallelization of Local Search Algorithms. In: Kranzlmüller, D., Kacsuk, P., Dongarra, J. (eds) Recent Advances in Parallel Virtual Machine and Message Passing Interface. EuroPVM/MPI 2004. Lecture Notes in Computer Science, vol 3241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30218-6_26
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DOI: https://doi.org/10.1007/978-3-540-30218-6_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23163-9
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