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An iterated local search algorithm for the minimum differential dispersion problem
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DOI：10.1016/j.knosys.2017.03.028.png)
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Abstract
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Given a set of n elements separated by a pairwise distance matrix, the minimum differential dispersion problem (MM-Diff DP) aims to identify a subset of m elements (m < n) such that the difference between the maximum sum and the minimum sum of the inter-element distances between any two chosen elements is minimized. We propose an effective iterated local search (denoted by ILS_MinDiff) for Min-Diff DP. To ensure an effective exploration and exploitation of the search space, ILS_MinDiff iterates through three sequential search phases: a fast descent-based neighborhood search phase to find a local optimum from a given starting solution, a local optima exploring phase to visit nearby high-quality solutions around a given local optimum, and a local optima escaping phase to move away from the current search region. Experimental results on six data sets of 190 benchmark instances demonstrate that ILS_MinDiff competes favorably with the state-of-the-art algorithms by finding 131 improved best results (new upper bounds). (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Combinatorial optimization
Dispersion problems
Heuristics
Iterated local search
Three phase search
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