A Genetic Algorithm for Multicast Routing under Delay Constraint in WDM Network with Different Light Splitting
32
TL;DR: A destination-oriented representation to represent chromosomes, three general genetic operators (selection, crossover, and mutation), four types of operators (Chromosome C crossover, Individual Crossover, Chromosome Mutation, and Individual Mutation), and four mutation heuristics are employed in the GA method, which shows that the solution model can obtain a near optimal solution.
read more
Abstract: Because optical WDM networks will become a realistic choice for buildings backbones, multicasting in the WDM network should be supported for various network applications. In this paper, a new multicast problem, Multicast Routing under Delay Constraint Problem (MRDCP), routing a request with delay bound to all destinations in a WDM network with different light splitting is solved by genetic algorithms (GAs), where different light splitting means that nodes in the network can transmit one copy or multiple copies to other nodes by using the same wavelength. The MRDCP can be reduced to the Minimal Steiner Tree Problem (MSTP) which has been shown to be NP-Complete. We propose a destination-oriented representation to represent chromosomes, three general genetic operators (selection, crossover, and mutation), four types of operators (Chromosome Crossover, Individual Crossover, Chromosome Mutation, and Individual Mutation). Four mutation heuristics (Random Mutation (RM), Cost First Mutation (CFM), Delay First Mutation (DFM), and Hybrid Mutation (HM)) are employed in the GA method. Finally, experimental results show that our solution model can obtain a near optimal solution.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A genetic algorithm based approach for energy efficient routing in two-tiered sensor networks
Ataul Bari,Shamsul Wazed,Arunita Jaekel,Subir Bandyopadhyay +3 more
- 01 Jun 2009
TL;DR: This paper has proposed an efficient solution, based on a genetic algorithm (GA), for scheduling the data gathering of relay nodes, which can significantly extend the lifetime of a relay node network.
200
Computational intelligence in photonics technology and optical networks: A survey and future perspectives
TL;DR: This paper reviews in a unified approach the applications of CI starting from the physical layer and ending to services layer, given that here there is a strong relation and unique interplay between components' technology and network issues, being sharing the common target of physical bandwidth's efficient utilization.
35
Node Placement for Maximum Coverage Based on Voronoi Diagram Using Genetic Algorithm in Wireless Sensor Networks
Naeim Rahmani,Farhad Nematy,Amir Masoud Rahmani,Mehdi Hosseinzadeh +3 more
- 01 Jan 2011
TL;DR: Simulations results show that the new approach can outperform other earlier works and guarantee the maximum coverage with less number of nodes and energy consumption decreases.
26
Multicast routing and wavelength assignment with delay constraints in WDM networks with heterogeneous capabilities
TL;DR: A heuristic, near-k-shortest-path heuristic (NKSPH), to solve the problem in large-scale networks of multicast routing and wavelength assignment with delay constraint (MRWA-DC) that incorporates delay constraints in WDM networks having heterogeneous light splitting capabilities.
24
A multipopulation parallel genetic simulated annealing-based QoS routing and wavelength assignment integration algorithm for multicast in optical networks
Hui Cheng,Xingwei Wang,Shengxiang Yang,Min Huang +3 more
- 01 Mar 2009
TL;DR: An integrated Quality of Service (QoS) routing algorithm for optical networks that can find a flexible-QoS-based cost suboptimal routing tree and assign wavelengths to the tree based on the wavelength graph is proposed.
References
•Book
Handbook of Genetic Algorithms
Lawrence Davis
- 01 Jan 1991
TL;DR: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.
8.2K
Shortest connection networks and some generalizations
TL;DR: In this paper, the basic problem of interconnecting a given set of terminals with a shortest possible network of direct links is considered, and a set of simple and practical procedures are given for solving this problem both graphically and computationally.
4.9K
Routing of multipoint connections
TL;DR: In this article, a weighted greedy algorithm is proposed for a version of the dynamic Steiner tree problem, which allows endpoints to come and go during the life of a connection.
3.1K
A fast algorithm for Steiner trees
TL;DR: The heuristic algorithm has a worst case time complexity of O(¦S¦¦V¦2) on a random access computer and it guarantees to output a tree that spans S with total distance on its edges no more than 2(1−1/l) times that of the optimal tree.
1.2K
Multicast routing with end-to-end delay and delay variation constraints
George N. Rouskas,Ilia Baldine +1 more
TL;DR: In this paper, the authors study the problem of constructing multicast trees to meet the quality of service requirements of real-time interactive applications operating in high-speed packet-switched environments and present a heuristic that demonstrates good average case behavior in terms of the maximum interdestination delay variation.