An ant-based algorithm for finding degree-constrained minimum spanning tree

Bui, Tuan Anh; Zrncic, C. M.

doi:10.1145/1143997.1144000

Cited by 38 publications

(23 citation statements)

References 23 publications

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“…However, in wireless ad hoc networks, owing to the broadcast nature of the wireless channels, the topology graph of such networks can be modeled as an undirected unit disk graph , , where the nodes represent the individual hosts and an edge connects two nodes if the corresponding hosts are within transmission range of each other. In these networks, the Steiner connected dominating set problem [7,8] is a promising approach for solving the multicast routing problem, where the only multicast group members need to be dominated. The following represents the graph-based mathematical formulations of the minimum multicast routing problem in wireless networks.…”

Section: Multicast Routing Problemmentioning

confidence: 99%

Multicast Routing Protocols in MANET

Torkestani

Meybodi

2012

Technological Advancements and Applications in Mobile Ad-Hoc Networks

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Multicast routing is an effective way to establish the group communication when the same message or the same stream of data needs to be sent to multiple receivers. Multicast routing has attracted a lot of attention in group oriented computing due to supporting data transmission from a single source node to multiple destinations concurrently. The advantage of multicast routing lies in its capability of reducing the communication cost and saving the network resources by sending only one copy of the message over the shared link leading to different destinations. Generally speaking, this survey classifies the multicast routing protocols into four categories based on the underlying routing structure: tree-based, mesh-based, hybrid and stateless multicast routing protocols. This survey summarizes the well-known most recent protocols of each category and compares their objectives, performances, advantages and disadvantages. This survey study helps us to choose the best multicasting protocol for each network application with respect to the requirements of application.

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Section: Multicast Routing Problemmentioning

confidence: 99%

Multicast Routing Protocols in MANET

Torkestani

Meybodi

2012

Technological Advancements and Applications in Mobile Ad-Hoc Networks

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“…Representative constrained minimum spanning tree problems studied in the literature include Bounded Diameter Minimum Spanning Tree (BDMST) [30], Degree Constrained Minimum Spanning Tree (DCMST) [31], Capacitated Minimum Spanning Tree (CMST) [32], Generalized Minimum Spanning Tree (GMST) [33], Delay-Constrained Minimum Spanning Tree [34,35], and HopConstrained Minimum Spanning Tree (HMST) [36]. Since the constrained minimum spanning tree problem is an NP-hard problem, heuristic methods, such as tabu search [37], ant colony optimization [31], genetic algorithms [38,39], and fuzzy-based algorithms [32], have been extensively used by the researchers for solving this complex optimization problem.…”

Section: Definition 3 Given a Weighted Undirected Graphmentioning

confidence: 99%

A learning automata-based heuristic algorithm for solving the minimum spanning tree problem in stochastic graphs

Torkestani

Meybodi

2010

J Supercomput

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During the last decades, a host of efficient algorithms have been developed for solving the minimum spanning tree problem in deterministic graphs, where the weight associated with the graph edges is assumed to be fixed. Though it is clear that the edge weight varies with time in realistic applications and such an assumption is wrong, finding the minimum spanning tree of a stochastic graph has not received the attention it merits. This is due to the fact that the minimum spanning tree problem becomes incredibly hard to solve when the edge weight is assumed to be a random variable. This becomes more difficult if we assume that the probability distribution function of the edge weight is unknown. In this paper, we propose a learning automata-based heuristic algorithm to solve the minimum spanning tree problem in stochastic graphs wherein the probability distribution function of the edge weight is unknown. The proposed algorithm taking advantage of learning automata determines the edges that must be sampled at each stage. As the presented algorithm proceeds, the sampling process is concentrated on the edges that constitute the spanning tree with the minimum expected weight. The proposed learning automata-based sampling method decreases the number of samples that need to be taken from the graph by reducing the rate of unnecessary samples. Experimental results show the superiority of the proposed algorithm over the well-known existing methods both in terms of the number of samples and the running time of algorithm.

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“…It will not be possible to do justice here to all of the previous work in this area, the intention is only to give a brief overview. The types of algorithms tried for solving the DCMST include greedy heuristics [7], neural network approaches [8], ant based methods [9], [10], [11], genetic/evolutionary algorithms [12], [13], [14], problem space search [15], local search [16], [17] and Lagrangian methods [18], [19].…”

Section: A the Degree-constrained Minimum Spanning Tree Problemmentioning

confidence: 99%

A hybrid Lagrangian Particle Swarm Optimization Algorithm for the degree-constrained minimum spanning tree problem

Ernst

2010

IEEE Congress on Evolutionary Computation

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This paper presents a new hybrid heuristic combining particle swarm optimization with a Lagrangian heuristic along the lines first proposed by Wedelin. We will refer to this as a Combinatorial Lagrangian Particle Swarm Optimization Algorithm (CoLaPSO). It uses a problem representations that works simultaneously in the dual space (Lagrangian multipliers) and the primal space in the form of cost perturbations. The CoLaPSO method is applied to solving the degree constrained minimum spanning tree problem. This NP-hard problem consists of finding a minimum cost spanning tree on a graph such that none of the vertices is connected to more than a fixed number of edges. The hybrid heuristic inherits from the Lagrangian parent an ability to calculate lower bounds on the objective and from the particle swarm optimization the ability to effectively parallelise the algorithm. Empirical evaluation using standard test problems from the literature show that the new method outperforms previously published heuristics for this problem and also computes useful lower bounds.

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An ant-based algorithm for finding degree-constrained minimum spanning tree

Cited by 38 publications

References 23 publications

Multicast Routing Protocols in MANET

Multicast Routing Protocols in MANET

A learning automata-based heuristic algorithm for solving the minimum spanning tree problem in stochastic graphs

A hybrid Lagrangian Particle Swarm Optimization Algorithm for the degree-constrained minimum spanning tree problem

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