The blob code is competitive with edge-sets in genetic algorithms for the minimum routing cost spanning tree problem

Julstrom, Bryant A.

doi:10.1145/1068009.1068108

Cited by 27 publications

(35 citation statements)

References 14 publications

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“…The algorithm is initialized with a population of random solutions and searches for optima by updating generations. However, unlike the GA, the PSO algorithm has no evolution operators such as the crossover and the mutation operator [25], [26], [27].…”

Section: A Particle Swarm Optimizationmentioning

confidence: 99%

A Novel PSO Based Algorithm Approach for the cMTS to Improve QoS in Next Generation Networks

Nguyen¹,

Le²,

Le³

et al. 2013

IJFCC

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Abstract-In this paper, we propose an effective Particle Swarm Optimization (PSO) algorithm to solving the capacitated minimum spanning tree (cMTS) problem to improve Quality of Service (QoS) in Next Generation Network (NGN). To improving QoS of communication network with considering the network provisioning capability and dynamic environment, we formulate this problem with minimizing the communication cost (as a kind of performance measures for network's QoS). We calculate the fitness value of each scheme and update them step by step with the best method to quickly find good approximate solutions of cMST problems. Numerical experiments show that our algorithm proposed have achieved much better than recent researches.Index Terms-Capacitated minimum spanning tree, communication network, quality of service, next generation network, particle swarm optimization.

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Section: A Particle Swarm Optimizationmentioning

confidence: 99%

A Novel PSO Based Algorithm Approach for the cMTS to Improve QoS in Next Generation Networks

Nguyen¹,

Le²,

Le³

et al. 2013

IJFCC

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“…This GA uses a Prim -based algorithm to randomly generate the initial population and performs better than the former one. Some other GAs using variants of Prufer numbers were proposed, such as Picciotto [10], Julstrom [11], [12], etc. However, Jens Gottlieb stated that Prufer numbers based chromosome presentations are not good for constrained spanning tree optimization problems [13].…”

Section: Related Work and Our Workmentioning

confidence: 99%

A Novel Ant Colony Optimization-Based Algorithm for the Optimal Communication Spanning Tree Problem

Nguyễn¹,

Le²,

Le³

2013

IJCTE

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Abstract-The optimal communication spanning tree (OCST) problem finds a spanning tree that connects all node satisfies their communication requirements for a minimum total cost. In this paper, we present a new method of finding optimal solution for OCST problem based on Ant Colony Optimization (ACO) to reduce search space mentioned above but still converge to a global good solution. Our algorithm take account into node biased encoding (NBE) scheme to find nearly optimal solution. The new algorithm can achieve a result that is better than known heuristic algorithms do, as verified by a set of public benchmark problem instances.Index Terms-Optimal communication spanning tree, node biased encoding, ant colony optimization. I. INTRODUCTIONThe problem of finding optimal communication spanning tree was introduced by Hu in 1974 [1]. Its NP-hard property was shown by Johnson et al [2]. The formal definition of this problem is below. Let The function f is defined as the product of two variables:The objective function is then:where  is the set of n n-2 label spanning tree of G [3]. We take the Palmer's 6-nodes instance from [4] as an example to clear out the definition.Example 1: The Palmer's 6-nodes instance.The distant matrix and requirement matrix are given below : 0 16661 18083 21561 21099 13461 16661 0 5658 9194 8797 10440 18083 5658 0 7230 6899 11340 21561 9194 7230 0 4300 13730 21099 8797 6899 4300 0 13130 13461 10440 11340 13730 13130 0 (a) The distant matrix 0 1 1 1 1 2 1 0 10 3 4 3 1 10 0 5 6 2 1 3 5 0 31 2 1 4 6 31 0 2 2 3 2 2 2 The cost of the tree T in Fig.2 For this instance, the optimal solution is given in Fig.2.b with the minimum cost is 693180.It is important to note that the OCST problem is far more different from the minimum spanning tree problem, which is already solved by several polynomial algorithms (such as Prim [5] and Kruskal). Moreover, there are several variations of this problem when the form of the distant matrix D or the requirement matrix R changes. More information can be found in [6]. II. RELATED WORKS AND OUR WORKSDue to its large application, there are many researched on OCST problem, from both exact and heuristic approaches. Authors in [1] defined the problem and discussed two simplest cases. The NP-hard property of the problem was showed [2] and Reshef showed that the problem is even a MAX SNP-hard one, which cannot be solved by a polynomial approximation algorithm unless P=NP [6].Therefore, heuristic optimization methods have been chosen by researchers to find optimal or nearly optimal solutions for the OCST problems. One of the first heuristic approaches was presented by Ahuja and Murty, which is closes to local search methods. Palmer, in his PhD thesis [8], proposed a genetic algorithm (GA) using the node and link biased encoding that will be clearly descript in section III.A. From the same approach, Li and Bouchebaba proposed another GA which works on a tree chromosome without intermediate encoding and decod...

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“…This reinterpretation also allowed Paulden and Smith to recognize that the Blob code is indeed equivalent to the Kreweras-Moszkowski code [24]. Moreover, several experimental analyses have been performed in the field of Genetic Algorithms to test Blob code performances focusing on certain desirable properties (namely locality and heritability) [17,18]. Also theoretical investigations of the Blob code properties have been made [25].…”

Section: Introductionmentioning

confidence: 99%

Parallel Algorithms for Encoding and Decoding Blob Code

Caminiti

Petreschi

2010

WALCOM: Algorithms and Computation

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Abstract. A bijective code is a method for associating labeled n-trees to (n − 2)-strings of node labels in such a way that different trees yield different strings and vice versa. For all known bijective codes, optimal sequential encoding and decoding algorithms are presented in literature, while parallel algorithms are investigated only for some of these codes. In this paper we focus our attention on the Blob code: a code particularly considered in the field of Genetic Algorithms. To the best of our knowledge, here we present the first parallel encoding and decoding algorithms for this code. The encoding algorithm implementation is optimal on an EREW PRAM, while the decoding algorithm requires O(log n) time and O(n) processors on CREW PRAM.

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The blob code is competitive with edge-sets in genetic algorithms for the minimum routing cost spanning tree problem

Cited by 27 publications

References 14 publications

A Novel PSO Based Algorithm Approach for the cMTS to Improve QoS in Next Generation Networks

A Novel PSO Based Algorithm Approach for the cMTS to Improve QoS in Next Generation Networks

A Novel Ant Colony Optimization-Based Algorithm for the Optimal Communication Spanning Tree Problem

Parallel Algorithms for Encoding and Decoding Blob Code

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