scholarly journals Solving the Delay-Constrained Least-Cost routing problem using Tabu Search with Edge Betweenness

2021 ◽  
Vol 229 ◽  
pp. 01009
Author(s):  
Amina Boudjelida ◽  
Ali Lemouari
Keyword(s):  
Tabu Search ◽  
Computer Network ◽  
Multicast Routing ◽  
Current Approach ◽  
Multicast Tree ◽  
Steiner Tree Problem ◽  
Routing Problem ◽  
Edge Betweenness ◽  
Tabu Search Method ◽  
Minimal Steiner Tree

Multicast routing consists of concurrently sending the same information from a source to a subset of all possible destinations in a computer network thus becomes an important technology communication. To solve the problem, a current approach for efficiently supporting a multicast session in a network consists of establishing a multicast tree that covers the source and all terminal nodes. This problem can be reduced to a minimal Steiner tree problem (MST) which aims to look for a tree that covers a set of nodes with a minimum total cost, the problem is NP-hard. In this paper, we investigate metaheuristics approaches for the Delay-Constrained Least-Cost (DCLC) problem, we propose a novel algorithm based on Tabu Search procedure with the Edge Betweenness (EB). The EB heuristic used first to improve KMB heuristic, able to measure the edge value to being included in a given path. The obtained solution improved using the tabu search method. The performance of the proposed algorithm is evaluated by experiments on a number of benchmark instances from the Steiner library. Experimental results show that the proposed metaheuristic gives competitive results in terms of cost and delay compared to the optimal results in Steiner library and other existing algorithms in the literature.

Tabu Search for Low-Cost Dynamic Multicast Tree Generation with Quality of Service Guarantees

2015 ◽  
Vol 24 (4) ◽  
pp. 479-489
Author(s):  
Muhammad Atif Tahir ◽  
Asif Jamshed ◽  
Habib-ur Rehman ◽  
Yassine Daadaa
Keyword(s):  
Quality Of Service ◽  
Tabu Search ◽  
Multicast Routing ◽  
Low Cost ◽  
Multicast Tree ◽  
Source Node ◽  
Delay Constraint ◽  
Routing Problem ◽  
Dynamic Multicast

AbstractIn a communication network with a source node, a multicast tree is defined as a tree rooted at the source node and all its leaves being recipients of the multicast originating at the source. The tree or bandwidth cost is normally measured by its utilization of tree links along with the quality of service (QoS) measures such as delay constraint and end-to-end delay. However, if nodes are allowed to join or leave the multicast group at any time during the lifetime of the multicast connection, then the problem is known as dynamic multicast routing problem. In this article, we combine a greedy approach with static multicast routing using Tabu Search to find a low-cost dynamic multicast tree with desirable QoS parameters. The proposed algorithm is then compared with several static multicast routing algorithms. The simulation results show that, on a large number of events, i.e., where nodes are leaving or joining, the proposed algorithm is able to find multicast trees of lower cost and more desirable QoS properties.

scholarly journals Multicast Utility Maximization with Load Balanc-ing in Private Cloud Environment

2018 ◽  
Vol 7 (2.19) ◽  
pp. 12
Author(s):  
N Senthamarai ◽  
M Vijayalakshmi
Keyword(s):  
Cloud Computing ◽  
Utility Maximization ◽  
Multicast Routing ◽  
Multicast Tree ◽  
Cloud Environment ◽  
Selection Algorithm ◽  
Private Cloud ◽  
Queuing Delay ◽  
Routing Problem ◽  
Transmission Overhead

Multicast is a technique for one-to-many communication over the network. It plays important role in cloud computing and reduces the transmission overhead in the private cloud environment. In this paper, build an efficient multicast tree for the multicast routing problem in which a network consists of different categories of nodes, where each category can have one or more nodes of the same characteristic which is different from the characteristics of nodes from other categories. So it is used to reduce the message traffic in such a network, to build a multicast tree and minimize the queuing delay using multicast selection algorithm.

A Multicast Routing Algorithm with Cost, Delay and Bandwidth Constraints

2013 ◽  
Vol 756-759 ◽  
pp. 1850-1854
Author(s):  
Yuan Chen Li ◽  
Guo Fang Kuang
Keyword(s):  
Communication Networks ◽  
Multicast Routing ◽  
Routing Algorithm ◽  
Multicast Tree ◽  
Routing Problem ◽  
Qos Parameters ◽  
The Cost ◽  
Multicast Routing Algorithm ◽  
Multiple Qos

Quality of service (QoS) generally assumes more than one QoS measure which implies that routing can be categorized as an instance of routing subject to multiple constraints: such as cost, delay, bandwidth, etc. The problem of constructing multicast trees is studied to meet the QoS requirements where it is necessary to provide bounded constraints among the source and all destinations while keeping the cost of the multicast tree low. So, a kind of source-destination QoS multicast routing problem is addressed about communication networks. The algorithm we presented takes bandwidth, delay and loss rate as premise, constructs routing selected function based on shortest path, modifies selected path according to the function above so as to fit multi-QoS parameters. Simulation results show that the algorithm has both lower delay and better performance and can be extended to cases of multiple QoS parameters conveniently.

A Multiple Constrained QoS Routing Algorithm for the Wireless Sensor Networks

2013 ◽  
Vol 347-350 ◽  
pp. 553-558
Author(s):  
Ze Shun Zhou ◽  
Yi Xu ◽  
Jun Jie Yan ◽  
Zhong Wei Nie ◽  
La Yuan Li
Keyword(s):  
Multicast Routing ◽  
Routing Algorithm ◽  
Qos Routing ◽  
Multicast Tree ◽  
Global Network ◽  
Wireless Sensor ◽  
Hop Count ◽  
Routing Problem ◽  
Multiple Qos Constraints ◽  
Multiple Qos

Routing problem is one of the most important issues to a wireless sensor network (WSN). It is the key problem to find an efficient energy strategy for prolonging network's lifetime because power supply might be impossible. This paper discusses the multicast routing problem of WSN with multiple QoS constraints, which may deal with the delay, bandwidth, hop count and packet reception rat and surplus energy metrics, and finds a minimum resource consumption path while satisfying multiple constraints optimization conditions, and describes a network model for researching the multicast routing problem. It presents a dynamic multicast routing algorithm with multiple QoS constraints (MCQoSRA). The MCQoSRA successfully solves the QoS routing problems when multicast nodes change dynamically in the networks. The MCQoSRA only requires the local state information of the link (or node), but does not require any global network sate to be maintained. In MCQoSRA, a multicast group member can join or leave the multicast session dynamically. The MCQoSRA can effectively decrease the overhead for constructing a multicast tree and the delay of the nodes, and improve the success ratio of seeking links. Simulation results show that the MCQoSRA provides an available means to implement multicast routing, and adapt to all kinds of the topology networks, and have better expansibility.

Routing algorithm with QoS constraints for real time multicast communications using particle swarm optimization and genetic meta-optimizer

2016 ◽  
Vol 33 (2) ◽  
Author(s):  
YEISON JULIAN CAMARGO ◽  
Leonardo Juan Ramirez ◽  
Ana Karina Martinez
Keyword(s):  
Particle Swarm Optimization ◽  
Design Methodology ◽  
Multicast Routing ◽  
Routing Algorithm ◽  
Particle Swarm ◽  
Minimum Cost ◽  
Fine Tuning ◽  
Steiner Tree Problem ◽  
Swarm Optimization ◽  
Routing Problem

Purpose The current work shows an approach to solve the QoS multicast routing problem by using Particle Swarm Optimization (PSO). The problem of finding a route from a source node to multiple destination nodes (multicast) at a minimum cost is an NP-Complete problem (Steiner tree problem) and is even greater if Quality of Service -QoS- constraints are taken into account. Thus, approximation algorithms are necessary to solve this problem. This work presents a routing algorithm with two QoS constraints (delay and delay variation) for solving the routing problem based on a modified version of particle swarm optimization. Design/methodology/approach This work involved the following methodology: 1. Literature Review 2. Routing algorithm design 3. Implementation of the designed routing algorithm by java programming. 4. Simulations and results. Findings In this work we compared our routing algorithm against the exhaustive search approach. The results showed that our algorithm improves the execution times in about 40% with different topologies. Research limitations/implications The algorithm was tested in three different topologies with 30, 40 and 50 nodes with and a dense graph topology. Originality/value Our algorithm implements a novel technique for fine tuning the parameters of the implemented bio-inspired model (Particles Swarm Optimization) by using a Genetic Meta-Optimizer. We also present a simple and multi implementation approach by using an encoding system that fits multiple bio-inspired models.

scholarly journals Multiobjective Multicast DSR Algorithm for Routing in Mobile Networks with Cost, Delay, and Hop Count

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hamdy H. El-Sayed ◽  
A. Younes ◽  
Fahad A. Alghamdi
Keyword(s):  
Mobile Networks ◽  
Communication Systems ◽  
Multicast Routing ◽  
Multicast Tree ◽  
Total Weight ◽  
Hop Count ◽  
Routing Problem ◽  
Evaluation Algorithm ◽  
Wireless Mobile Communication ◽  
Communication Needs

Tremendous evaluation of wireless mobile communication needs more efficient algorithms for communication systems. The use of conventional single-objective optimization algorithms may be unsuitable for real applications, because they act to the detriment of the rest of the performance parameters like lifetime network, delay, cost, and hop count; for this reason, multiobjective is needed. This paper presents performance evaluation and compares between the Multicast MDSR and MAODV with MACO. The proposed MDSR is concerned with change of the route discovery phase, where the route selection is based on the shortest path of route reply packets on the route with calculating the number of hop counts. Also, this article compares our MDSR modification with the evaluation algorithm based on Ant Colony Optimization (ACO), which finds the best path and multicast tree optimizes total weight (cost, delay, and hop count) of the multicast tree using multiobjective. Experimental results proved that the proposed MDSR algorithm is more efficient than MAODV and MACO in the total weight (cost, delay, and hop count), respectively. Moreover, the MACO outperforms MAODV for multicast routing problem.

Penentuan Rute Dan Penjadwalan Pengangkutan Sampah di Kota Bandung Wilayah Bandung Barat dengan Menggunakan Tabu Search

2020 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Yogi Yogaswara
Keyword(s):  
Tabu Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Search Algorithm ◽  
Initial Solution ◽  
Sequential Method ◽  
Routing Problem ◽  
Taboo Search ◽  
Total Distance ◽  
Tabu Search Method

Abstract― The route determination Model is commonly known as the Vehicle Routing Problem (VRP), VRP deals with determining the route to produce the best route in problems involving more than one vehicle with a certain capacity to serve a number of customer's point according to their respective demands, one of the main purpose of route determination is to minimize total distances. PD. Kebersihan Kota Bandung has been facing one of the problems related to vehicle routing since the route used for waste transport by the company currently does not pay attention to the location and distance of the TPS to be visited, resulting in a longer total distance of 564, 30 km. With the current routes, the company does not have a definite schedule of trash transport, this problem concern involve for trashbin heap. Therefore, VRP research was conducted to determine the transportation of waste routes in West Bandung area by producing solutions that can be proposed to reduce the total distances. The research was solved using the Tabu Search method, the application of this method requires the initial solution. In this study, the saving and sequential method insertion used to create the initial solution, then the initial solution was done repair by using the Tabu Search algorithm. The result of data processing with taboo Search generates 15 routes with the total mileage for each day of 448.48 km. Total distance generated by Tabu Search resulted in a decline of 115.82 km or give a savings of 20.53% from Total distance with the current route. Based on the route comes from Tabu Search, there is a schedule for garbage transport schedules in the TPS and obtained the total time of service by 15 vehicles on each day of 63.45 hours. Abstrak― Model penentuan rute pada umumnya dikenal dengan Vehicle Routing Problem (VRP), VRP berkaitan dengan penentuan rute untuk menghasilkan rute terbaik dalam permasalahan yang melibatkan lebih dari satu kendaraan dengan kapasitas tertentu untuk melayani sejumlah titik pelanggan sesuai dengan permintaan masing-masing, tujuan penentuan rute ini salah satunya adalah untuk meminimumkan jarak tempuh. Penentuan rute menjadi salah satu permasalahan PD. Kebersihan Kota Bandung yang bergerak dibidang pengangkutan sampah, karena rute yang digunakan oleh perusahaan saat ini tidak memperhatikan lokasi dan jarak Tempat Pembuangan Sementara (TPS) yang akan dikunjungi, sehingga menghasilkan total jarak tempuh yang lebih jauh yaitu sebesar 564,30 km. Dengan rute yang digunakan saat ini, perusahaan tidak memiliki jadwal pengangkutan sampah secara pasti yang memberikan kekhawatiran timbulnya penumpukan sampah. Oleh karena itu, dilakukan penelitian VRP untuk menentukan rute pengangkutan sampah diwilayah Bandung Barat dengan menghasilkan solusi yang dapat diajukan untuk mengurangi total jarak. Penelitian ini diselesaikan dengan menggunakan metode Tabu Search, penerapan metode ini memerlukan adanya solusi awal. Dalam penelitian ini, metode saving dan sequential insertion yang digunakan untuk membuat solusi awal, selanjutnya solusi awal tersebut dilakukan perbaikan dengan menggunakan algoritma Tabu Search. Hasil pengolahan data dengan Tabu Search menghasilkan 15 rute dengan total jarak tempuh untuk setiap harinya sebesar 448,48 km. Total jarak yang dihasilkan Tabu Search menghasilkan penurunan sebesar 115,82 km atau memberikan penghematan sebesar 20,53% dari total jarak dengan rute saat ini. Berdasarkan rute yang dihasilkan dari Tabu Search, selanjutnya dilakukan penjadwalan pengangkutan sampah disetiap TPS dan memperoleh waktu pelayanan yang dibutuhkan oleh 15 kendaraan setip harinya sebesar 63,45 jam.

An Improved Chaotic Binary Bat Algorithm for QoS Multicast Routing

2016 ◽  
Vol 25 (04) ◽  
pp. 1650025 ◽  
Author(s):  
Yassine Meraihi ◽  
Dalila Acheli ◽  
Amar Ramdane-Cherif
Keyword(s):  
Particle Swarm Optimization ◽  
Communication Networks ◽  
Heuristic Algorithms ◽  
Multicast Routing ◽  
Logistic Map ◽  
Particle Swarm ◽  
Bat Algorithm ◽  
Multicast Tree ◽  
Swarm Optimization ◽  
Routing Problem

The quality of service (QoS) multicast routing problem is one of the main issues for transmission in communication networks. It is known to be an NP-hard problem, so many heuristic algorithms have been employed to solve the multicast routing problem and find the optimal multicast tree which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate. In this paper, we propose an improved chaotic binary bat algorithm to solve the QoS multicast routing problem. We introduce two modification methods into the binary bat algorithm. First, we use the two most representative chaotic maps, namely the logistic map and the tent map, to determine the parameter [Formula: see text] of the pulse frequency [Formula: see text]. Second, we use a dynamic formulation to update the parameter α of the loudness [Formula: see text]. The aim of these modifications is to enhance the performance and the robustness of the binary bat algorithm and ensure the diversity of the solutions. The simulation results reveal the superiority, effectiveness and efficiency of our proposed algorithms compared with some well-known algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Jumping Particle Swarm Optimization (JPSO), and Binary Bat Algorithm (BBA).

Research on QoS Multicast Tree Based on Ant Colony Algorithm

2014 ◽  
Vol 635-637 ◽  
pp. 1734-1737 ◽  
Author(s):  
Yong Huang
Keyword(s):  
Data Transmission ◽  
Ant Colony Algorithm ◽  
Multicast Routing ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Multicast Tree ◽  
Ant Colony ◽  
Shortest Path Algorithm ◽  
Routing Problem ◽  
Delay Constraints

Ant colony algorithm is a stochastic search algorithm, evolutionary algorithm with other models, like the evolution of the composition of the population by the candidate solutions to find the optimal solution, this paper proposes a new ant colony algorithm to solve by bandwidth and QoS multicast routing problem delay constraints, k shortest path algorithm by means of genetic algorithm we propose obtained, and then use the ant colony algorithm to construct optimal multicast tree for data transmission.

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