  Given a set of $n$ elements separated by a pairwise distance matrix, the
minimum differential dispersion problem (Min-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, the proposed ILS_MinDiff algorithm 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 130 improved best results (new upper bounds).
