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CLAHC - custom late acceptance hill climbing: first results on TSP

Published: 08 July 2021 Publication History

Abstract

The Late Acceptance Hill Climbing heuristic is a Hill Climbing algorithm that uses a record of the history of objective values of previously encountered solutions in order to decide whether to accept a new solution. Literature has shown that Late Acceptance Hill Climbing is generally better at not getting stuck in local optima because of the history. In this paper, we propose and investigate a simple, yet effective, modification to Late Acceptance Hill Climbing, where we change how values in the history are replaced. In our tests, referring to the Traveling Salesman Problem, we analyze the behavior of the proposed approach for different history sizes. We also show that the simple change in the algorithm allows the heuristic to find better solutions than the original one on most of the instances tested.

References

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Mosab Bazargani and Fernando G. Lobo. 2017. Parameter-less late acceptance hill-climbing. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, Berlin, Germany, July 15-19, 2017. ACM, 219--226.
[2]
Edmund K. Burke and Yuri Bykov. 2017. The late acceptance Hill-Climbing heuristic. Eur. J. Oper. Res. 258, 1 (2017), 70--78.
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Gunter Dueck. 1993. New Optimization Heuristics: The Great Deluge Algorithm and the Record-to-Record Travel. J. Comput. Phys. 104, 1 (1993), 86--92.
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Gunter Dueck and Tobias Scheuer. 1990. Threshold accepting: A general purpose optimization algorithm appearing superior to simulated annealing. J. Comput. Phys. 90, 1 (1990), 161--175.
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Alberto Franzin and Thomas Stützle. 2017. Comparison of Acceptance Criteria in Randomized Local Searches. In EA 2017Revised Selected Papers (Lecture Notes in Computer Science), Vol. 10764. Springer, 16--29.
[6]
Fred W. Glover and Gary A. Kochenberger (Eds.). 2003. Handbook of Metaheuristics. International Series in Operations Research & Management Science, Vol. 57. Kluwer / Springer.
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Holger H. Hoos and Thomas Stützle. 2004. Stochastic Local Search: Foundations & Applications. Elsevier / Morgan Kaufmann.
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Scott Kirkpatrick, D. Gelatt Jr., and Mario P. Vecchi. 1983. Optimization by Simulated Annealing. Sci. 220, 4598 (1983), 671--680.
[9]
Gerhard Reinelt. 1991. TSPLIB - A Traveling Salesman Problem Library. INFORMS J. Comput. 3, 4 (1991), 376--384.

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    cover image ACM Conferences
    GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion
    July 2021
    2047 pages
    ISBN:9781450383516
    DOI:10.1145/3449726
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 08 July 2021

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    Author Tags

    1. hill climbing
    2. late acceptance hill climbing
    3. local search

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