Search results for "Distributed minimum spanning tree"
showing 6 items of 6 documents
The node-depth encoding
2008
The node-depth encoding has elements from direct and indirect encoding for trees which encodes trees by storing the depth of nodes in a list. Node-depth encoding applies specific search operators that is a typical characteristic for direct encodings. An investigation into the bias of the initialization process and the mutation operators of the node-depth encoding shows that the initialization process has a bias to solutions with small depths and diameters, and a bias towards stars. This investigation, also, shows that the mutation operators are unbiased. The performance of node-depth encoding is investigated for the bounded-diameter minimum spanning tree problem. The results are presented f…
Tabu search with strategic oscillation for the quadratic minimum spanning tree
2014
The quadratic minimum spanning tree problem consists of determining a spanning tree that minimizes the sum of costs of the edges and pairs of edges in the tree. Many algorithms and methods have been proposed for this hard combinatorial problem, including several highly sophisticated metaheuristics. This article presents a simple Tabu Search (TS) for this problem that incorporates Strategic Oscillation (SO) by alternating between constructive and destructive phases. The commonalties shared by this strategy and the more recently introduced methodology called iterated greedy search are shown and implications of their differences regarding the use of memory structures are identified. Extensive …
Guided local search for the optimal communication spanning tree problem
2011
This paper considers the optimal communication spanning tree (OCST) problem. Previous work analyzed features of high-quality solutions. Consequently, integrating this knowledge into a metaheuristic increases its performance for the OCST problem. In this paper, we present a guided local search (GLS) approach which dynamically changes the objective function to guide the search process into promising areas. In contrast to traditional approaches which reward promising solution features by favoring edges with low weights pointing towards the tree's center, GLS penalizes low-quality edges with large weights that do not point towards the tree's center.
Strategic sharing of a costly network
2012
We study minimum cost spanning tree problems for a set of users connected to a source. Prim’s algorithm provides a way of finding the minimum cost tree mm. This has led to several definitions in the literature, regarding how to distribute the cost. These rules propose different cost allocations, which can be understood as compensations and/or payments between players, with respect to the status quo point: each user pays for the connection she uses to be linked to the source. In this paper we analyze the rationale behind a distribution of the minimum cost by defining an a priori transfer structure. Our first result states the existence of a transfer structure such that no user is willing to …
Maintaining Dynamic Minimum Spanning Trees: An Experimental Study
2010
AbstractWe report our findings on an extensive empirical study on the performance of several algorithms for maintaining minimum spanning trees in dynamic graphs. In particular, we have implemented and tested several variants of the polylogarithmic algorithm by Holm et al., sparsification on top of Frederickson’s algorithm, and other (less sophisticated) dynamic algorithms. In our experiments, we considered as test sets several random, semi-random and worst-case inputs previously considered in the literature together with inputs arising from real-world applications (e.g., a graph of the Internet Autonomous Systems).
Image Segmentation through a Hierarchy of Minimum Spanning Trees
2012
Many approaches have been adopted to solve the problem of image segmentation. Among them a noticeable part is based on graph theory casting the pixels as nodes in a graph. This paper proposes an algorithm to select clusters in the images (corresponding to relevant segments in the image) corresponding to the areas induced in the images through the search of the Minimum Spanning Tree (MST). In particular is is based on a clustering algorithm that extracts clusters computing a hierarchy of Minimum Spanning Trees. The main drawback of this previous algorithm is that the dimension of the cluster is not predictable and a relevant portion of found clusters can be composed by micro-clusters that ar…