A dynamic heuristic algorithm for minimum cost multicast routing in OTN network

2011 ◽  
Author(s):  
Chao Fang ◽  
Xue Chen
Keyword(s):  
Heuristic Algorithm ◽  
Multicast Routing ◽  
Minimum Cost

A generalized model and a heuristic algorithm for the large-scale covering tour problem

2018 ◽  
Vol 52 (2) ◽  
pp. 577-594
Author(s):  
Keisuke Murakami
Keyword(s):  
Local Search ◽  
Heuristic Algorithm ◽  
Large Scale ◽  
Minimum Cost ◽  
The Other ◽  
Computational Experiments ◽  
Generalized Model ◽  
Search Techniques ◽  
Covering Tour Problem

The covering tour problem (CTP) is defined on a graph, where there exist two types of vertices. One is called visited vertex, which can be visited. The other is called covered vertex, which must be covered but cannot be visited. Each visited vertex covers a subset of covered vertices, and the costs of edges between visited vertices are given. The objective of the CTP is to obtain a minimum cost tour on a subset of visited vertices while covering all covered vertices. In this paper, we deal with the large-scale CTPs, which are composed of tens of thousands of vertices; in the previous studies, the scales of the instances in the experiments are at most a few hundred vertices. We propose a heuristic algorithm using local search techniques for the large-scale CTP. With computational experiments, we show that our algorithm outperforms the existing methods.

Based on Energy-Efficient Opportunistic Multicast Routing for Mobile Wireless Sensor Networks

2014 ◽  
Vol 599-601 ◽  
pp. 851-855
Author(s):  
Zhen Yu Zhang ◽  
Shao Jie Wen ◽  
Wen Zhong Yang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Multicast Routing ◽  
Minimum Cost ◽  
Wireless Sensor ◽  
Mobile Wireless ◽  
Mobile Wireless Sensor Networks ◽  
Routing Method ◽  
Mobile Wireless Sensor

Energy-efficient is one of the main problems among all the problems in mobile wireless sensor networks, for improving the energy efficiency of the nodes in the multicast environment and complete the communication works with the minimum cost, we present a based on energy-efficient opportunistic multicast routing for mobile wireless sensor networks. In order to increase the reliability of links, we use opportunistic routing and collaboration mechanism to realize the routing method, and then use the path aggregation to reduce the energy consumption of the whole network. The results of simulation show that, the method presented in this paper is effective in reducing the consumption of links cost.

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 Minimum Cost Multicast Routing Using Ant Colony Optimization Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Xiao-Min Hu ◽  
Jun Zhang
Keyword(s):  
Ant Colony Optimization ◽  
Multicast Routing ◽  
Minimum Cost ◽  
Multicast Tree ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Specific Knowledge ◽  
Artificial Ants ◽  
Multicast Trees ◽  
Pheromone Information

Multicast routing (MR) is a technology for delivering network data from some source node(s) to a group of destination nodes. The objective of the minimum cost MR (MCMR) problem is to find an optimal multicast tree with the minimum cost for MR. This problem is NP complete. In order to tackle the problem, this paper proposes a novel algorithm termed the minimum cost multicast routing ant colony optimization (MCMRACO). Based on the ant colony optimization (ACO) framework, the artificial ants in the proposed algorithm use a probabilistic greedy realization of Prim’s algorithm to construct multicast trees. Moving in a cost complete graph (CCG) of the network topology, the ants build solutions according to the heuristic and pheromone information. The heuristic information represents problem-specific knowledge for the ants to construct solutions. The pheromone update mechanisms coordinate the ants’ activities by modulating the pheromones. The algorithm can quickly respond to the changes of multicast nodes in a dynamic MR environment. The performance of the proposed algorithm has been compared with published results available in the literature. Results show that the proposed algorithm performs well in both static and dynamic MCMR problems.

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