Prim's algorithm

About: Prim's algorithm is a research topic. Over the lifetime, 775 publications have been published within this topic receiving 17971 citations. The topic is also known as: DJP algorithm & Jarník algorithm.

New heuristic approaches for the dominating tree problem

Abstract: Given an undirected, connected and edge-weighted graph, the dominating tree problem (DTP) seeks on this graph a tree with minimum total edge weight such that each vertex of the graph is either in this tree or adjacent to a vertex in this tree. The DTP is a NP-Hard problem. In the literature only two heuristics for this problem are proposed so far in spite of the fact that it has several practical applications in the field of wireless sensor networks. In this paper, we propose one heuristic and two swarm intelligence techniques, viz. an artificial bee colony algorithm and an ant colony optimization algorithm for the DTP. Computational results show the effectiveness of our approaches.

A hybrid heuristic for the minimum weight vertex cover problem

Abstract: Given an undirected graph with weights associated with its vertices, the minimum weight vertex cover problem seeks a subset of vertices with minimum sum of weights such that each edge of the graph has at least one endpoint belonging to the subset. In this paper, we propose a hybrid approach, combining a steady-state genetic algorithm and a greedy heuristic, for the minimum weight vertex cover problem. The genetic algorithm generates vertex cover, which is then reduced to minimal weight vertex cover by the heuristic. We have evaluated the performance of our algorithm on several test problems of varying sizes. Computational results show the effectiveness of our approach in solving the minimum weight vertex cover problem.

Minimum-energy broadcasting in multi-hop wireless networks using a single broadcast tree

Abstract: In this paper we address the minimum-energy broadcast problem in multi-hop wireless networks, so that all broadcast requests initiated by different source nodes take place on the same broadcast tree. Our approach differs from the most commonly used one where the determination of the broadcast tree depends on the source node, thus resulting in different tree construction processes for different source nodes. Using a single broadcast tree simplifies considerably the tree maintenance problem and allows scaling to larger networks. We first show that, using the same broadcast tree, the total power consumed for broadcasting from a given source node is at most twice the total power consumed for broadcasting from any other source node. We next develop a polynomial-time approximation algorithm for the construction of a single broadcast tree. The performance analysis of the algorithm indicates that the total power consumed for broadcasting from any source node is within 2H(n-1) from the optimal, where n is the number of nodes in the network and H(n) is the harmonic function. This approximation ratio is close to the best achievable bound in polynomial time. We also provide a useful relation between the minimum-energy broadcast problem and the minimum spanning tree, which shows that a minimum spanning tree may be a good candidate in sparsely connected networks. The performance of our algorithm is also evaluated numerically with simulations.

Randomized minimum spanning tree algorithms using exponentially fewer random bits

Abstract: For many fundamental problems there exist randomized algorithms that are asymptotically optimal and are superior to the best-known deterministic algorithm. Among these are the minimum spanning tree (MST) problem, the MST sensitivity analysis problem, the parallel connected components and parallel minimum spanning tree problems, and the local sorting and set maxima problems. (For the first two problems there are provably optimal deterministic algorithms with unknown, and possibly superlinear, running times.) One downside of the randomized methods for solving these problems is that they use a number of random bits linear in the size of input. In this article we develop some general methods for reducing exponentially the consumption of random bits in comparison-based algorithms. In some cases we are able to reduce the number of random bits from linear to nearly constant, without affecting the expected running time.Most of our results are obtained by adjusting or reorganizing existing randomized algorithms to work well with a pairwise or O(1)-wise independent sampler. The prominent exception, and the main focus of this article, is a linear-time randomized minimum spanning tree algorithm that is not derived from the well-known Karger-Klein-Tarjan algorithm. In many ways it resembles more closely the deterministic minimum spanning tree algorithms based on soft heaps. Further, using our algorithm as a guide, we present a unified view of the existing “nongreedy” minimum spanning tree algorithms. Concepts from the Karger-Klein-Tarjan algorithm, such as F-lightness, MST verification, and sampled graphs, are related to the concepts of edge corruption, subgraph contractibility, and soft heaps, which are the basis of the deterministic MST algorithms of Chazelle and Pettie-Ramachandran.