A Genetic Algorithm for Steiner Tree Optimization with Multiple Constraints Using Prüfer Number

Haghighat, Abolfazl Toroghi; Faez, Karim; Dehghan, Mehdi; Mowlaei, A.; Ghahremani, Y.

doi:10.1007/3-540-36087-5_32

Cited by 17 publications

(8 citation statements)

References 16 publications

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“…The existing tree-based GAs can be categorized into two groups according to the chromosome representing: GAs with direct tree (GAs with DT) [6,9] and GAs with encoding. For GAs with encoding, there are two kinds of encoding schemes: GAs with Prüfer [10,11] and GAs with the sequence and topology encoding (GAs with ST) [12,13] .…”

Section: Genetic Algorithms For Multicastmentioning

confidence: 99%

A Novel Tree‐Based Genetic Algorithm for the Multicast Protocol in Two‐Tiered WSNs

2020

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Section: Genetic Algorithms For Multicastmentioning

confidence: 99%

A Novel Tree‐Based Genetic Algorithm for the Multicast Protocol in Two‐Tiered WSNs

2020

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“…There are three major encoding scheme categories in the tree-based topology [21]: (i) edge encoding, which is generally unsuitable for a spanning tree; (ii) vertex encoding, which is more popular and often employs the Prüfer number encoding method [8]; (iii) edge and vertex encoding. A better encoding approach would make the crossover and mutation operations more efficient.…”

Section: Conventional Genetic Encodingmentioning

confidence: 99%

“…A better encoding approach would make the crossover and mutation operations more efficient. 2, we give a string P n of Prüfer number and its leaf node string P l In [8], we can get below to illustrate the Prüfer number encoding and decoding results with more details. Prüfer number encoding [8] a named tree is a unique sequence associated with the tree.…”

Section: Conventional Genetic Encodingmentioning

confidence: 99%

A genetic algorithm for energy-efficient based multicast routing on MANETs

Yen¹,

Chan²,

Chao³

et al. 2008

Computer Communications

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a b s t r a c tIn ad hoc networks, mobile node battery energy is finite and represents one of the greatest constraints for designing multicast routing protocols. In regards to the battery lifetime limitation in supporting multicast routing, some studies have investigated a power saving network layer. These proposed methods have always considered several techniques such as a route load for relaying, battery lifetime in route selection, decreasing the frequency of sending control messages, optimizing the size of control headers, and efficient route reconfiguration techniques. This paper discusses the multicast routing problem with multiple QoS constraints in MANETs. It is also an NP-Complete problem that deals with the various constraints. We propose an energy-efficient genetic algorithm mechanism to resolve these problems. Furthermore, we design a source-tree-based routing algorithm and build the shortest-path multicast tree to minimize delay time by using a small population size in the genetic algorithm. Only a few nodes are involved in the route computation. We also improve the genetic sequence and topology encoding and prolong the lifetime of mobile nodes that calculate the residual battery energy of all nodes in a multicast tree. The simulation results show that our proposal method is an efficient and robust algorithm for multicast route selection.

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“…However, other models employ more elaborate GAs which include sampling operators, different types of individuals (conservative and explorer), tree representation and multiobjective GAs. Moreover, Tsai et al [11] deal with large scale networks (16,20,32,50, and 64 nodes). A number of different representations for individuals were employed: (i) Zhang and Leung [10] encode all network edges in each chromosome in order to construct a tree; Hwang et al [15] represent paths in route tables as genes; (iii) Haghighat et al [16] use Prüfer numbers to encode multicast trees, and Li [17] employs a GA to optimize randomly selected source-destination paths stored in buffers.…”

Section: Introductionmentioning

confidence: 99%

MulRoGA: A Multicast Routing Genetic Algorithm approach considering multiple objectives

Araújo

Garrozi

2008

Appl Intell

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We solve a multicast routing problem by means of a genetic algorithm (GA) without using multicast trees. The source-destination routes need to fulfill two conflicting objectives: maximization of the common links and minimization of the route sizes. The proposed GA can be characterized by its representation of network links and routes in a variable size multi-chromosome problem; local viability restrictions in order to generate the initial population and define variation operators; selection operators in order to choose the most promising individuals thus preserving diversity, and the fitness function in order to handle the conflicting multiple objectives. The proposed model is called a Multicast Routing Genetic Algorithm (MulRoGA). The model was tested on the 33-node European GÉANT WAN network backbone and three other networks (66-node, 100-node and 200-node) randomly generated using the Waxman model on a network topology generator BRITE. On considering each network, a number of solutions were found for changes in the size and node members of the multicast groups, and the source node. The results of the MulRoGA operation suggest a consistent and robust performance in the various cases including comparisons with the methods of unicast shortest path routing, shortest path tree routing (SPT), and simulated annealing (SA) heuristic.

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A Genetic Algorithm for Steiner Tree Optimization with Multiple Constraints Using Prüfer Number

Cited by 17 publications

References 16 publications

A Novel Tree‐Based Genetic Algorithm for the Multicast Protocol in Two‐Tiered WSNs

A Novel Tree‐Based Genetic Algorithm for the Multicast Protocol in Two‐Tiered WSNs

A genetic algorithm for energy-efficient based multicast routing on MANETs

MulRoGA: A Multicast Routing Genetic Algorithm approach considering multiple objectives

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