This paper discusses how a mobility model can be used jointly with a mobile activity trace and evolutionary computation to reduce the signaling load related to mobility management, an important and fundamental task in any public land mobile network. For this purpose, a mobility model is used to determine the most probable locations of each mobile subscriber, and this information, in turn, is used to assign paging areas. This paging strategy is evaluated by taking into account different probability thresholds and time-delay constraints, and in a multiobjective way. Thus, we study the whole objective space of the problem, ensure the results that are not dependent on the configuration of registration areas used in the analysis, and take into account the signaling traffic of both paging and location updates (in contrast to other published works, in which only the reduction in the paging load is considered). The feasibility of this paging scheme is evaluated by means of a performance analysis, in which it is compared with other paging schemes widely used in the recent literature. Results show that this paging strategy can reduce the blanket paging load by an average of ~56.73%. Furthermore, the performance analysis also shows that using evolutionary computation jointly with a paging procedure based on a mobility model is a very useful strategy for managing mobility in a public land mobile network, because it allows the total signaling load obtained by blanket paging to be reduced by ~67.03%.

At this time, Wireless Sensor Networks (WSNs) are widely used in many fields. This kind of network has some attractive features that have promoted their use, such as the absence of wires and the use of low-cost devices. However, WSNs also have important shortcomings that affect some features like energy cost and quality of service. In this paper, we optimize traditional static WSNs (a set of sensors and a sink node) by means of adding routers to simultaneously optimize a couple of important factors: energy consumption and average coverage. This multiobjective optimization problem was solved in a previous work using two genetic algorithms (NSGA-II and SPEA2) which had an important limitation: the computing time was very high and then, to address complex instances was difficult. In this paper, both algorithms are parallelized using OpenMP in order to reduce the computing time, and a more realistic data set is included. The results obtained are analyzed in depth from both multiobjective and parallel viewpoints. A Quite good efficiency is obtained with a wide range of processing cores, observing that NSGA-II provides the best results in small and medium instances, but in the largest ones the behavior of both algorithms is similar.

Currently, wavelength division multiplexing technology is widely used for exploiting the huge bandwidth of optical networks. It allows simultaneous transmission of traffic on many nonoverlapping channels (wavelengths). These channels support traffic demands in the gigabits per second (Gb/s) range; however, since the majority of devices or applications only require a bandwidth of megabits per second (Mb/s), this is a waste of bandwidth. This problem is efficiently solved by multiplexing a number of low-speed traffic demands (Mb/s) onto a high-speed wavelength channel (Gb/s). This is known as the traffic grooming problem. Since traffic grooming is an NP-hard problem, in this paper, we propose two novel multiobjective evolutionary algorithms for solving it. The selected algorithms are multiobjective variants of the standard differential evolution (DEPT) and variable neighborhood search. With the aim of ensuring the performance of our proposals, we have made comparisons with the well-known fast Nondominated Sort Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm 2, and other approaches published in the literature. After performing diverse comparisons, we can conclude that our novel approaches obtain promising results, highlighting in particular the performance of the DEPT algorithm.

User group key management is a critical task in secure multicast applications of multimedia contents, such as, the Internet TV, pay per view, satellite TV, etc. These keys must be recalculated, encrypted and redistributed when a user is joined or disjoined to a specific group in order to avoid that users which do not belong to a concrete group can access to the contents of that group. This paper presents a high performance dual MicroBlaze System on Chip (SoC) to perform this management as far as possible. This platform aims to reduce the influence of a user join or disjoin in the reception of the multimedia contents by the rest of users.

A wide variety of optimization problems requires the combination of Bioinspired and Parallel Computing to address the complexity needed to get optimal solutions in reduced times. The multicore era allows the researcher to exploit modern arqitectures to resolve these NP-Hard problems. Inferring phylogenetic trees which describe a hypothesis of the evolution of species is a well-known example of this kind of problems. As the space of possible tree topologies increases exponentially with the number of species, exhaustive searches cannot be applied. Also, additional difficulties arise when we must consider simultaneously multiple optimality measures to resolve the problem. In this paper, we report a performance study on multicore machines of a parallel multiobjective adaptation of the Artificial Bee Colony algorithm for inferring phylogenies according to the maximum parsimony and maximum likelihood criteria. Experimental results reveal that our proposal can improve other approaches based on advanced High Performance Computing techniques on large data sets.

Bioinformatics and computational biology include researchers from many areas: biochemists, physicists, mathematicians, and engineers. The scale of the problems that are discussed ranges from small molecules to complex systems, where many organisms coexist. However, among all these issues, we can highlight genomics, which studies the genomes of microorganisms, plants, and animals. Predicting common patterns, i.e., motifs, in a set of deoxyribonucleic acid (DNA) sequences is one of the important sequence analysis problems, and it has not yet been resolved in an efficient manner. In this study, we study the application of evolutionary multiobjective optimization to solve the motif discovery problem, applied to the specific task of discovering novel transcription factor binding sites in DNA sequences. For this, we have designed, adapted, configured, and evaluated several types of multiobjective metaheuristics. After a detailed study, the results indicate that these metaheuristics are appropriate for discovering motifs. To find good approximations to the Pareto front, we use the hypervolume indicator, which has been successfully integrated into evolutionary algorithms. Besides the hypervolume indicator, we also use the coverage relation to ensure: Which is the best Pareto front? New results have been obtained, which significantly improve those published in previous research works.

The future of designing optical networks is focused on the wavelength division multiplexing (WDM) technology. This technology divides the huge bandwidth of an optical fiber into different wavelengths, providing different available channels per link of fiber. However, when it is necessary to establish a set of demands, a problem comes up. This problem is known as a routing and wavelength assignment (RWA) problem. Depending on the traffic pattern, two varieties of a RWA problem have been considered in the literature: static and dynamic. In this paper, we present a comparative study among three multiobjective evolutionary algorithms (MOEAs) based on swarm intelligence to solve the RWA problem in real-world optical networks. Artificial bee colony (ABC) algorithm, gravitational search algorithm (GSA), and firefly algorithm (FA) are the selected evolutionary algorithms, but are adapted to multiobjective domain (MO-ABC, MO-GSA, and MO-FA, respectively). In order to prove the goodness of the swarm proposals, we have compared them with a standard MOEA: fast nondominated sorting genetic algorithm. Finally, we present a comparison among the metaheuristics based on swarm intelligence and several techniques published in the literature, coming to the conclusion that swarm intelligence is very suitable to solve the RWA problem, and presumably that it may obtain such quality results not only in diverse telecommunication optimization problems, but also in other engineering optimization problems.

WiMAX (Worldwide Interoperability for Microwave Access) or IEEE 802.16 is a technology developed to provide greater bandwidth and coverage. One of the most important challenges in wireless deploying network is the Base Stations (BS) location to meet traffic and coverage requirements efficiently. In this paper we are using a particularized swarm intelligence algorithm based on collective behavior of honey bees to find good solutions. In order to minimize the interference among cells and reduce energy consumption, we apply a post-processing to obtain power control and sectorization. Simulations results show that our method provides more efficient and better solutions than those are obtained by other evolutionary algorithms in the same instances.

The last few years have seen a significant growth in communication networks. With the 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 optimisation problems, most of them in the field of combinatorial optimisation. We address here the Terminal Assignment problem. The main objective is to assign a collection of terminals to a collection of concentrators. In this paper, we propose a Hybrid Scatter Search (HSS) algorithm to assign terminals to concentrators. Coupled with the Scatter Search algorithm we use a Tabu Search algorithm to locate the global minimum. We show that HSS is able to achieve feasible solutions to Terminal Assignment instances, improving the results obtained by previous approaches.

This paper proposes the use of Differential Evolution with Pareto Tournaments (DEPT) to identify common patterns, motifs, in biological sequences. The work is motivated by two fundamental facts: first, the role that bioinformatics problems are taking in computer engineering in recent years, and second, the limited existence of scientific papers that use evolutionary techniques for solving such problems. Although finding motifs in deoxyribonucleic acid (DNA) sequences is one of the classical sequence analysis problems, it has not yet been resolved in an efficient manner. Using evolutionary algorithms we can get nearly optimal solutions in a reasonable time. The Motif Discovery Problem (MDP) aims to maximize conflicting objectives: support, motif length, and similarity. These objectives imply multiobjective optimization (MOO) to obtain motifs in the most efficient way as possible. Moreover, in this work, we incorporate the hypervolume indicator to measure the quality of the solutions to this problem. As we will see, our results surpass the results obtained by other approaches proposed in the literature.

The technology based on Wavelength Division Multiplexing (WDM) applied to optical networks has resolved the bandwidth waste in this kind of networks. WDM divides the bandwidth of an optical fiber in different wavelengths that can be used by electronic devices to send and receive data without bottlenecks. Another problem appears when the necessity of choice of the path and the wavelengths to interconnect a set of source-destination pairs comes up. This problem is known as Routing and Wavelength Assignment (RWA) and there are two types, depending on the demands: Static-RWA and Dynamic-RWA. In this paper we present a multiobjective evolutionary algorithm to solve this problem. We choose the Differential Evolution (DE), incorporating the concept of Pareto Tournament (DEPT). To determine the parameters of the algorithm, we used two real different topologies (the first is a topology from USA, NSF network; and the second is a topology from Japan, NTT network) and six sets of source-destination pairs for each topology, that is, a total of twelve instances. After all experiments, we can conclude that with this multiobjective evolutionary algorithm, we have obtained better results than the other approaches published in the literature.

Despite of being a very common task in bioinformatics, multiple sequence alignment is not a trivial matter. Arranging a set of molecular sequences to reveal their similarities and their differences is often hardened by the complexity and the size of the search space involved, which undermine the approaches that try to explore exhaustively the solution's search space. Due to its nature, Genetic Algorithms, which are prone for general combinatorial problems optimization in large and complex search spaces, emerge as serious candidates to tackle with the multiple sequence alignment problem. We have developed an evolutionary approach, AlineaGA, which uses a Genetic Algorithm with local search optimization embedded on its mutation operators for performing multiple sequence alignment. Now, we have enhanced its selection method by employing an elitist strategy, and we have also developed a new crossover operator. These transformations allow AlineaGA to improve its robustness and to obtain better fit solutions. Also, we have studied the effect of the mutation probability in solutions' evolution by analyzing the performance of the whole population throughout generations. We conclude that increasing the mutation probability leads to better solutions in fewer generations and that the mutation operators have a dramatic effect in this particular domain.

This paper presents a new approach based on the Scatter Search (SS) algorithm applied to the Location Management problem using the Location Area (LA) scheme. The LA scheme is used to achieve the best configuration of the network partitioning, into groups of cells (location areas), that minimizes the costs involved. In this work we execute five distinct experiments with the aim of setting the best values for the Scatter Search parameters, using test networks generated with realistic data [1]. We also want to compare the results obtained by this new approach with those achieved through classical strategies, other algorithms from our previous work and also by other authors. The simulation results show that this SS based approach is very encouraging.

Searching for the best possible alignment for a set of sequences is not an easy task, mainly because of the size and complexity of the search space involved. Genetic algorithms are predisposed for optimizing general combinatorial problems in large and complex search spaces. We have designed a Genetic Algorithm for this purpose, AlineaGA, which introduced new mutation operators with local search optimization. Now we present the contribution that these new operators bring to this field, comparing them with similar versions present in the literature that do not use local search mechanisms. For this purpose, we have tested different configurations of mutation operators in eight BAliBASE alignments, taking conclusions regarding population evolution and quality of the final results. We conclude that the new operators represent an improvement in this area, and that their combined use with mutation operators that do not use optimization strategies, can help the algorithm to reach quality solutions.

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.

The radio network design (RND) is an NP-hard optimization problem which consists of the maximization of the coverage of a given area while minimizing the base station deployment. Solving RND problems efficiently is relevant to many fields of application and has a direct impact in the engineering, telecommunication, scientific, and industrial areas. Numerous works can be found in the literature dealing with the RND problem, although they all suffer from the same shortfall: a noncomparable efficiency. Therefore, the aim of this paper is twofold: first, to offer a reliable RND comparison base reference in order to cover a wide algorithmic spectrum, and, second, to offer a comprehensible insight into accurate comparisons of efficiency, reliability, and swiftness of the different techniques applied to solve the RND problem. In order to achieve the first aim we propose a canonical RND problem formulation driven by two main directives: technology independence and a normalized comparison criterion. Following this, we have included an exhaustive behavior comparison between 14 different techniques. Finally, this paper indicates algorithmic trends and different patterns that can be observed through this analysis.

In this work we present a Grid implementation of a FPGA optimization tool. The application is based on a Distributed Genetic Algorithm (DGA). It solves the placement and routing problem into the FPGA design cycle. The Grid infrastructure is based both on gLite middleware and GridWay metascheduler. The DGA’s different islands are sent to the Working Nodes (WN), where they evolve as remote jobs. We implemented a migration system between islands based on centralizing the exchanging data on a local node. Parting from this data, the local node builds new islands and the evolution continues until the stop criterion is reached. Obtained results show us that the main benefit of the distributed model is a large reduction of the execution time. By using the distributed platform users can launch more complex tasks and increase the number of experiments comparing with sequential execution, expending less amounts of time and effort.

Frequency planning (also known as frequency assignment problem -FAP-) is a very important task for current GSM operators. The problem consists in trying to minimize the number of interferences caused when a limited number of frequencies has to be assigned to a quite high number of transceivers. In this work we focus on solving this problem for a realistic-sized, real-world GSM network using a parallelized version of the PBIL (population-based incremental learning) algorithm. Therefore, we have parallelized the PBIL algorithm fixed to the FAP problem using cluster computing. The analysis of the results proves that we have reached a double goal: on the one hand, with the parallelized version of the algorithm, its execution time is reduced down to the optimum values; and on the other hand, we prove that using a distributed island model applied to PBIL, the results for the network-planning are better than the ones obtained with the sequential version.

In this paper we present a Differential Evolution based algorithm used to solve the Radio Network Design (RND) problem. This problem consists in determining the optimal locations for base station transmitters in order to get a maximum coverage area with a minimum number of transmitters. Because of the very high amount of possible solutions, this problem is suitable to be tackled with evolutionary techniques, so in our work it has been developed an algorithm inspired on the well-known Differential Evolution algorithm, obtaining good results.

In this work we present a system for implementing the placement and routing stages in the FPGA cycle of design, into the physical design stage. We start with the ISCAS benchmarks, on EDIF format, of Boolean functions to be implemented. They are processed by a parser in order to obtain an internal representation which is able to be processed by a genetic algorithm (GA) tool. This tool develops the placement and routing tasks, considering possible restricted area into the FPGA. In order to help to the GA to make the routing stage we have added a local search procedure. That local search gets a path between two points without considering neither their placement nor the restricted areas among them. The GA is fully customizable, featuring the ability to work with one or several islands. The experiments have verified that using distributing execution improves the costs and speeds up the convergence towards better results in smaller slots of time.

In this paper we study the use of FPGAs as part of an industrial inspection system. More exactly, to evaluate the cork stopper quality applying real-time image processing. Our system uses the HOT2-XL PCI board, a hardware module library implementing some of the most common operations for image processing, and a Visual C++ application in order to validate the hardware designs and manage the platform. Also, we propose an algorithm for obtaining different features of cork stopper defects, so allowing their classification. Finally, the practical results obtained with this algorithm are presented along with the conclusions and future work.