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Improving the Robustness of EPS to Solve the TSP

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Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13292))

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Abstract

Embarrassingly Parallel Search (EPS) parallelizes the search for solutions in CP by decomposing the initial problem into a huge number of sub-problems that are consistent with propagation. Then, each waiting worker takes a sub-problem and solves it. The process is repeated until all the sub-problems have been solved. EPS is based on the idea that if there are many sub-problems to solve then the solving times of the workers will be balanced even if the solving times of the sub-problems are not. This approach gives rather good results for solving the Traveling Salesman Problem (TSP). Unfortunately, for some instances, sub-problems with extremely different solving times appear, for example one requiring a huge part of the total solving time. In this case the load balancing is poor. We show that a general increase in the number of sub-problems does not solve this imbalance. We present a method that identifies the presence of difficult sub-problems during the solving process and decompose them again. This method keeps the advantages of EPS: the communication is very reduced (the workers do not communicate with each other) and it is independent of the solver. Experimental results for the TSP show a good improvement of load balancing and a better scaling with hundred of cores.

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Authors and Affiliations

  1. Université Côte d’Azur - I3S - CNRS, 2000, route des Lucioles - Les Algorithmes - Euclide B, BP 121, 06903, Sophia Antipolis Cedex, France

    Nicolas Isoart & Jean-Charles Régin

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  1. Nicolas Isoart
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  2. Jean-Charles Régin
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Correspondence to Jean-Charles Régin .

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  1. UCLouvain, Louvain-la-Neuve, Belgium

    Pierre Schaus

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Isoart, N., Régin, JC. (2022). Improving the Robustness of EPS to Solve the TSP. In: Schaus, P. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2022. Lecture Notes in Computer Science, vol 13292. Springer, Cham. https://doi.org/10.1007/978-3-031-08011-1_12

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  • DOI: https://doi.org/10.1007/978-3-031-08011-1_12

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