Percorrer por autor "Gómez-Pulido, Juan Antonio"
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- An evolutionary approach for performing multiple sequence alignmentPublication . Silva, Fernando José Mateus; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel A.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.
- Evolutionary Swarm based algorithms to minimise the link cost in Communication NetworksPublication . Moreira Bernardino, Anabela; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel Ángel; Bernardino, Eugénia MoreiraIn the last decades, nature-inspired algorithms have been widely used to solve complex combinatorial optimisation problems. Among them, Evolutionary Algorithms (EAs) and Swarm Intelligence (SI) algorithms have been extensively employed as search and optimisation tools in various problem domains. Evolutionary and Swarm Intelligent algorithms are Artificial Intelligence (AI) techniques, inspired by natural evolution and adaptation. This paper presents two new nature-inspired algorithms, which use concepts of EAs and SI. The combination of EAs and SI algorithms can unify the fast speed of EAs to find global solutions and the good precision of SI algorithms to find good solutions using the feedback information. The proposed algorithms are applied to a complex NP-hard optimisation problem - the Terminal Assignment Problem (TAP). The objective is to minimise the link cost to form a network. The proposed algorithms are compared with several EAs and SI algorithms from literature. We show that the proposed algorithms are suitable for solving very large scaled problems in short computational times.
- A hybrid ant colony optimization algorithm for solving the terminal assignment problemPublication . Bernardino, Eugénia; Bernardino, Anabela; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel AngelThe past two decades have witnessed tremendous research activities in optimization methods for communication networks. One important problem in communication networks is the Terminal Assignment Problem. This problem involves determining minimum cost links to form a network by connecting a collection of terminals to a collection of concentrators. In this paper, we propose a Hybrid Ant Colony Optimization Algorithm to solve the Terminal Assignment Problem. We compare our results with the results obtained by the standard Genetic Algorithm, the Tabu Search Algorithm and the Hybrid Differential Evolution Algorithm, used in literature.
- A Hybrid Differential Evolution Algorithm for Solving the Terminal Assignment ProblemPublication . Bernardino, Eugénia; Bernardino, Anabela; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel AngelThe 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.
- Hybrid Honey Bees Mating Optimisation algorithm to assign terminals to concentratorsPublication . Bernardino, Eugénia M.; Bernardino, Anabela M.; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel AngelIn this paper we propose a new approach to assign terminals to concentrators using a Hybrid Honey Bees Mating Optimisation algorithm. Honey Bees Mating Optimisation (HBMO) algorithm is a swarm-based optimisation algorithm, which simulates the mating process of real honey bees. We apply a hybridisation of HBMO to solve a combinatorial optimisation problem known as Terminal Assignment Problem (TAP). The purpose is to connect a given set of terminals to a given set of concentrators and minimise the link cost to form a communication network. The feasibility of Hybrid HBMO is demonstrated and compared with the solutions obtained by other algorithms from literature over different TAP instances.
- A Hybrid Population-Based Incremental Learning algorithm for load balancing in RPRPublication . Bernardino, Anabela M.; Bernardino, Eugénia M.; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel AngelWhen 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, because it increases the system capacity and improves the overall ring performance. An important optimisation problem in this context is the Weighted Ring Arc Loading Problem (WRALP). It consists of the design, in a communication network of a transmission route (direct path) for each request, such that high load on the ring arcs will be avoided. WRALP asks for a routing scheme such that the maximum load on the ring arcs will be minimum. In this paper we study WRALP without demand splitting and we propose a Hybrid Populationbased Incremental Learning (HPBIL) to solve it. We show that HPBIL is able to achieve good solutions, improving the results obtained by previous approaches.
- Hybrid population-based incremental learning to assign terminals to concentratorsPublication . Bernardino, Eugénia Moreira; Bernardino, Anabela Moreira; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel AngelIn the last decade, we have seen a significant growth in communication networks. In centralised communication networks, a central computer serves several terminals or workstations. In large networks, some concentrators are used to increase the network efficiency. A collection of terminals is connected to a concentrator and each concentrator is connected to the central computer. In this paper we propose a Hybrid Population-based Incremental Learning (HPBIL) to assign terminals to concentrators. We use this algorithm to determine the minimum cost to form a network by connecting a given collection of terminals to a given collection of concentrators. We show that HPBIL is able to achieve good solutions, improving the results obtained by previous approaches.
- A Hybrid Scatter Search algorithm to assign terminals to concentratorsPublication . Bernardino, Eugénia Moreira; Bernardino, Anabela Moreira; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel AngelThe 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.
- Solving large-scale SONET network design problems using bee-inspired algorithmsPublication . Bernardino, Eugénia Moreira; Bernardino, Anabela Moreira; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel AngelIn 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.
- Solving the non-split weighted ring arc-loading problem in a resilient packet ring using particle swarm optimizationPublication . Bernardino, Anabela; Bernardino, Eugénia; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel AngelMassive growth of the Internet traffic in 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 the optimized transport of data traffic over optical fiber ring networks. Its design is to provide the resilience found in SONET/SDH networks but instead of setting up circuit oriented connections, providing a packet based transmission. This is to increase the efficiency of Ethernet and IP services. In this paper, a weighted ring arc-loading problem (WRALP) is considered which arises in engineering and planning of the RPR systems (combinatorial optimization NP- complete problem). 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 is minimized. This paper suggests four variants of Particle Swarm Optimization (PSO), combined with a Local Search (LS) method to efficient non-split traffic loading on the RPR. Numerical simulation results show the effectiveness and efficiency of the proposed methods.