On memetic search for the max-mean dispersion problem

TL;DR

This paper introduces a memetic algorithm for the NP-hard max-mean dispersion problem, demonstrating superior performance on benchmark instances by effectively combining solution recombination and local search.

Contribution

The paper proposes a novel memetic algorithm that significantly improves solution quality for MaxMeanDP, outperforming existing methods on standard benchmarks.

Findings

Achieved 59 new best results out of 60 challenging instances.

Matched or improved all known best results on 160 benchmark instances.

Successfully handled large instances with up to 5000 elements.

Abstract

Given a set $V$ of $n$ elements and a distance matrix $[d_{ij}]_{n\times n}$ among elements, the max-mean dispersion problem (MaxMeanDP) consists in selecting a subset $M$ from $V$ such that the mean dispersion (or distance) among the selected elements is maximized. Being a useful model to formulate several relevant applications, MaxMeanDP is known to be NP-hard and thus computationally difficult. In this paper, we present a highly effective memetic algorithm for MaxMeanDP which relies on solution recombination and local optimization to find high quality solutions. Computational experiments on the set of 160 benchmark instances with up to 1000 elements commonly used in the literature show that the proposed algorithm improves or matches the published best known results for all instances in a short computing time, with only one exception, while achieving a high success rate of 100\%. In particular, we improve 59 previous best results out of the 60 most challenging instances. Results on a set of 40 new large instances with 3000 and 5000 elements are also presented. The key ingredients of the proposed algorithm are investigated to shed light on how they affect the performance of the algorithm.