Eugénia Moreira Bernardino

Efficient load balancing for a resilient packet ring using artificial bee colony

Resilient Packet Ring (RPR), also known as IEEE 802.17, is a standard designed for optimising the transport of data traffic over optical fiber ring networks. The Weighted Ring Arc-Loading Problem (WRALP) is a NP-complete problem that arises in engineering and planning of the RPR systems. Specifically, for a given set of non-split and uni-directional point-to-point demands (weights), the objective is to find the routing for each demand (i.e., assignment of the demand to either clockwise or counter-clockwise ring) so that the maximum arc load will be minimised. This paper suggests an efficient traffic loading algorithm- Artificial Bee Colony (ABC). We compare our results with the ones obtained by the standard Genetic Algorithm, Tabu Search Algorithm and Particle Swarm Optimisation, used in literature. Simulation results verify the effectiveness of the ABC algorithm.

A Hybrid Differential Evolution Algorithm for Solving the Terminal Assignment Problem

The field of communication networks has witnessed tremendous growth in recent years resulting in a large variety of combinatorial optimization problems in the design and in the management of communication networks. One of these problems is the terminal assignment problem. The task here is to assign a given set of terminals to a given set of concentrators. In this paper, we propose a Hybrid Differential Evolution Algorithm to solve the terminal assignment problem. We compare our results with the results obtained by the classical Genetic Algorithm and the Tabu Search Algorithm, widely used in literature.

Solving the Terminal Assignment Problem Using a Local Search Genetic Algorithm

Terminal assignment is an important issue in telecommunication networks optimization. The task here is to assign a given collection of terminals to a given collection of concentrators. The main objective is to minimize the link cost to form a network. This optimization task is an NP-complete problem. The intractability of this problem is a motivation for the pursuits of a local search genetic algorithm that produces approximate, rather than exact, solutions. In this paper, we explore one of the most successful emerging ideas combining local search with population-based search. Simulation results verify the effectiveness of the proposed method. The results show that our algorithm provides good solutions in a better running time.

Solving ring loading problems using bio-inspired algorithms

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 systems 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.

Using the bees algorithm to assign terminals to concentrators

With the recent growth of communication networks, a large variety of combinatorial optimization problems appeared. One of these problems is the Terminal Assignment Problem. The main objective is to assign a given set of terminals to a given set of concentrators. In this paper, we propose the Bees Algorithm to assign terminals to concentrators. The algorithm performs a kind of neighbourhood search and uses a local search method to locate the global minimum. The Bees Algorithm is a swam-based optimization algorithm that mimics the natural behaviour of honey bees. We show that the Bees Algorithm is able to achieve feasible solutions to Terminal Assignment instances, improving the results obtained by previous approaches.

Solving the weighted ring edge-loading problem without demand splitting using a Hybrid Differential Evolution Algorithm

In the last few years we have seen a significant growth in synchronous optical network (SONET) deployments in telecommunication service providers. With growth of data traffic, network operators seek network-engineering tools to extract the maximum benefits out of the existing infrastructure. This has suggested a number of new optimization problems, most of them in the field of combinatorial optimization. We address here the Weighted Ring Edge-Loading Problem (WRELP). The WRELP is an important optimization problem arising in a popular ring topology for communication networks - given a set of nodes connected along a bi-directional SONET ring, the objective is to minimize the maximum load on the edges (pairwise) of a ring. Our procedure includes some original features, including the application of Hybrid Differential Evolution. We also perform comparisons with standard differential evolution, genetic algorithm and tabu search.

Solving the Ring Loading Problem Using Genetic Algorithms with Intelligent Multiple Operators

Planning optical communication networks suggests a number of new optimization problems, most of them in the field of combinatorial optimization. We address here the Ring Loading Problem. The objective of the problem is to find a routing scheme such that the maximum weighted load on the ring is minimized. In this paper we consider two variants: (i) demands can be split into two parts, and then each part is sent in a different direction; (ii) each demand must be entirely routed in either of the two directions, clockwise or counterclockwise. In this paper, we propose a genetic algorithm employing multiple crossover and mutation operators. Two sets of available crossover and mutation operators are established initially. In each generation a crossover method is selected for recombination and a mutation method is selected for mutation based on the amount fitness improvements achieve over a number of previous operations (recombinations/mutations). We use tournament selection for this purpose. Simulation results with the different methods implemented are compared.

A Genetic Algorithm with Multiple Operators for Solving the Terminal Assignment Problem

In recent years we have witnessed a tremendous growth of communication networks resulted in a large variety of combinatorial optimization problems. One of these problems is the terminal assignment problem. In this paper, we propose a genetic algorithm employing multiple crossover and mutation operators for solving the well-known terminal assignment problem. Two sets of available crossover and mutation operators are established initially. In each generation a crossover method is selected for recombination and a mutation method is selected for mutation based on the amount fitness improvements achieved over a number of previous operations (recombinations/mutations). We use tournament selection for this purpose. Simulation results with the different methods implemented are compared.

Efficient load balancing using the bees algorithm

The massive growth of the Internet traffic in the last decades has motivated the design of high-speed optical networks. Resilient Packet Ring (RPR), also known as IEEE 802.17, is a standard designed for optimising the transport of data traffic over optical fiber ring networks. In this paper, we consider a Weighted Ring Arc-Loading Problem (WRALP), which arises in engineering and planning of the RPR systems. The WRALP is a combinatorial optimisation NP-complete problem. Given a set of point-to-point unidirectional traffic demands of a specified bandwidth, the demands should be assigned to the clockwise or to the counter-clockwise ring in order to yield the best performance. This paper suggests an efficient load balancing algorithm - the Bees Algorithm. We compare our results with nine meta-heuristics used in literature to solve the same problem. The simulation results verify the effectiveness and robustness of the Bees Algorithm.

Solving large-scale SONET network design problems using bee-inspired algorithms

Abstract In the past years, the number of users of Internet-based applications has exponentially increased and consequently the request for transmission capacity or bandwidth has significantly augmented. When managed properly, the ring networks are uniquely suited to deliver a large amount of bandwidth in a reliable and inexpensive way. In this paper, we consider two problems that arise in the design of optical telecommunication networks, namely the SONET Ring Assignment Problem (SRAP) and the Intraring Synchronous Optical Network Design Problem (IDP), known to be NP-hard. In SRAP, the objective is to minimise the number of rings (i.e., DXCs). In IDP, the objective is to minimise the number of ADMs. Both problems are subject to a ring capacity constraint. To solve these problems, we propose two bee-inspired algorithms: Hybrid Artificial Bee Colony and Hybrid Bees Algorithm. We hybridise the basic form of these algorithms with local search, in order to refine newly constructed solutions. We also perform comparisons with other algorithms from the literature and use larger instances. The simulation results verify the effectiveness and robustness of the proposed algorithms.