.png){kind=icon}
.png){kind=icon}
Return
Solving ring loading problems using bio-inspired algorithms
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jnca.2010.11.003.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In the last years, several combinatorial optimisation problems have arisen in the communication networks field. In many cases, to solve these problems it is necessary the use of emergent optimisation algorithms. The Weighted Ring Loading Problem (WRLP) is an important optimisation problem in the communication optical network field. When managed properly, the ring networks are uniquely suited to deliver a large amount of bandwidth in a reliable and inexpensive way. An optimal load balancing is very important, as it increases the system's capacity and improves the overall ring performance. The WRLP consists on the design, in a communication network of a transmission route (direct path) for each request, such that high load on the arcs/edges is avoided, where an arc is an edge endowed with a direction. In this paper we study this problem in two different ring types: Synchronous Optical NETworking (SONET) rings and Resilient Packet Ring (RPR). In RPR the purpose is to minimise the maximum load on the ring Arcs (WRALP). In SONET rings the purpose is to minimise the maximum load on the ring Edges (WRELP). The load of an arc is defined as the total weight of those requests that are routed through the arc in its direction and the load of an edge is the total weight of the routes traversing the edge in either direction. In this paper we study both problems without demand splitting and we propose three bio-inspired algorithms: Genetic Algorithm with multiple operators, Hybrid Differential Evolution with a multiple strategy and Hybrid Discrete Particle Swarm Optimisation. We also perform comparisons with other algorithms from literature. Simulation results verify the effectiveness of the proposed algorithms. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords:
Communication networks
Bio-inspired algorithms
Optimisation algorithms
Swarm Intelligence
Weighted Ring Arc-Loading Problem
Weighted Ring Edge-Loading Problem
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8
Papers:
3.6K
Citations:
1.1W