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Distance-guided local search

Author

Listed:
  • Daniel Porumbel

    (Conservatoire National des Arts et Métiers)

  • Jin-Kao Hao

    (Université d’Angers
    Institut Universitaire de France)

Abstract

We present several techniques that use distances between candidate solutions to achieve intensification in Local Search (LS) algorithms. An important drawback of classical LS is that after visiting a very high-quality solution the search process can “forget about it” and continue towards very different areas. We propose a method that works on top of a given LS to equip it with a form of memory so as to record the highest-quality visited areas (spheres). More exactly, this new method uses distances between candidate solutions to perform a coarse–grained recording of the LS trajectory, i.e., it records a number of discovered spheres. The (centers of the) spheres are kept sorted in a priority queue in which new centers are continually inserted as in insertion-sort algorithms. After thoroughly investigating a sphere, the proposed method resumes the search from the first best sphere center in the priority queue. The resulting LS trajectory is no longer a continuous path, but a tree-like structure, with closed branches (already investigated spheres) and open branches (as-yet-unexplored spheres). We also explore several other techniques relying on distances, e.g., in Section 2.3, we show how to use distance information to prevent the search from looping indefinitely on large (quasi-)plateaus. Experiments on three problems based on different encodings (partitions, vectors and permutations) confirm the intensification potential of the proposed ideas.

Suggested Citation

  • Daniel Porumbel & Jin-Kao Hao, 2020. "Distance-guided local search," Journal of Heuristics, Springer, vol. 26(5), pages 711-741, October.
  • Handle: RePEc:spr:joheur:v:26:y:2020:i:5:d:10.1007_s10732-020-09446-w
    DOI: 10.1007/s10732-020-09446-w
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    References listed on IDEAS

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    1. Porumbel, Daniel & Goncalves, Gilles & Allaoui, Hamid & Hsu, Tienté, 2017. "Iterated Local Search and Column Generation to solve Arc-Routing as a permutation set-covering problem," European Journal of Operational Research, Elsevier, vol. 256(2), pages 349-367.
    2. Qinghua Wu & Jin-Kao Hao, 2013. "An adaptive multistart tabu search approach to solve the maximum clique problem," Journal of Combinatorial Optimization, Springer, vol. 26(1), pages 86-108, July.
    3. Vicente Campos & Manuel Laguna & Rafael Martí, 2005. "Context-Independent Scatter and Tabu Search for Permutation Problems," INFORMS Journal on Computing, INFORMS, vol. 17(1), pages 111-122, February.
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