Performance Analysis of Artificial Bee-Colony Algorithm for Routing and Wavelength Assignment in DWDM Transport Network

Setting up lightpaths for a set of requested connection of wavelength division multiplexing (WDM) network, is by routing and assigning wavelengths to each connection. So as to minimize the use of network resources or maximize the traffic served, is called the routing and wavelength assignment (RWA) problem. A new idea based on Artificial Bee Colony (ABC) algorithm is introduced for solving RWA problem which is known to be an NP-hard problem. In the proposed ABC-RWA approach every food source represents a possible and feasible candidate lightpath between each original and destination node span in demand matrix. The situation of the food source is modified by some artificial bee in the population where the aim is to discover the places of food sources. The food source with the highest nectar value seems to be a solution which is evaluated by the fitness function. This thesis proposes solutions to solve the RWA problem using artificial bee-colony algorithm in order to achieve better performance of the network connection to serve a given demand matrix of an optical network to reach RWA global solution. The work will evaluate the path length (propagation delay) for solving RWA problem with ABC algorithm in a real-world optical networks test bench to find optimal routes for connection request in demand matrix according to objective function and some physical and operational constraints in Dense Wavelength Division Multiplexing (DWDM) optical networks. Based on simulation with several generated traffic distribution, ABC algorithm can be used to solve routing and wavelength problem at DWDM transport network as shown that in line with iteration process the path length observed toward minimum value. The number of iteration needed to reach the fitness value depends on several parameter such as number of connection request, number of wavelength and alternative path, the distribution of generated traffic and also population size

An artificial bee colony algorithm for public bike repositioning problem

Paper PresentationConference Theme: Informing transport’s future through practical researchPublic bike repositioning is crucial in public bike sharing systems due to the imbalanced distribution of public bikes. This paper models the public bike repositioning problem (PBRP) involving two non-linear objectives, which are to minimize total service duration and the duration of the longest vehicle route. It includes practical constraints such as the tolerance of demand dissatisfaction and the limitation of duration on the longest route. These objective functions and constraints make the PBRP become NP-hard, so here introduces an artificial bee colony (ABC) algorithm to solve this PBRP. Three neighbourhood operators are introduced to improve the solution search. A modified ABC is proposed to further improve the solution quality. The performance of the modified heuristic was evaluated with the network of Vélib', and compared with the original heuristic and the Genetic Algorithm. These results may therefore prove that the modified heuristic can be an alternative to solve the PBRP. The numerical studies demonstrated that the two objective functions performed differently in which the increase in fleet size may not improve the objective value. This paper will therefore discuss on the practical implications of the trade-offs and provide suggestions about similar repositioning operations.postprin

Survey on Various Aspects of Clustering in Wireless Sensor Networks Employing Classical, Optimization, and Machine Learning Techniques

A wide range of academic scholars, engineers, scientific and technology communities are interested in energy utilization of Wireless Sensor Networks (WSNs). Their extensive research is going on in areas like scalability, coverage, energy efficiency, data communication, connection, load balancing, security, reliability and network lifespan. Individual researchers are searching for affordable methods to enhance the solutions to existing problems that show unique techniques, protocols, concepts, and algorithms in the wanted domain. Review studies typically offer complete, simple access or a solution to these problems. Taking into account this motivating factor and the effect of clustering on the decline of energy, this article focuses on clustering techniques using various wireless sensor networks aspects. The important contribution of this paper is to give a succinct overview of clustering

Shortest Paths Routing Problem in MANETs

The need for communication services is rapidly increasing, because the mobile communication service is synonymous with an ideal communication style realizing communication anytime, anywhere and with anyone. The availability of a path depends on the number of links and the reliability of each link forming the path. Many routing metrics in terms of number of links have been proposed, such as the shortest path routing. Shortest path routing selects a path having minimum cost to forward the data to the destination node. Shortest path routing algorithm selection depends on direct traffic form source to destination, maximizing the network performance and minimizing the cost. Performance of the network can be enhanced through shortest path routing but it also depends upon the functionality of the routing protocol and the parameters that are selected for the shortest path routing. The primary goal of such an adhoc network routing protocol is correct and efficient route establishment between a pair of nodes so that messages may be delivered in a timely manner. Route construction should be done with a minimum of cost, overhead and bandwidth consumption. Some of researchers explored the concept of shortest path routing over ad hoc network. Each one uses his own parameters with different topology. No one uses all parameters. In this paper, we will discuss the solutions ideas that have been proposed by them

An artificial bee colony algorithm for the capacitated vehicle routing problem

This paper introduces an artificial bee colony heuristic for solving the capacitated vehicle routing problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. An enhanced version of the artificial bee colony heuristic is also proposed to improve the solution quality of the original version. The performance of the enhanced heuristic is evaluated on two sets of standard benchmark instances, and compared with the original artificial bee colony heuristic. The computational results show that the enhanced heuristic outperforms the original one, and can produce good solutions when compared with the existing heuristics. These results seem to indicate that the enhanced heuristic is an alternative to solve the capacitated vehicle routing problem. © 2011 Elsevier B.V. All rights reserved.postprin