A parallel cooperative team of multiobjective evolutionary algorithms for motif discovery

When solving a wide range of complex scenarios of a given optimization problem, it is very difficult, if not impossible, to develop a single technique or algorithm that is able to solve all of them adequately. In this case, it is necessary to combine several algorithms by applying the most appropriate one in each case. Parallel computing can be used to improve the quality of the solutions obtained in a cooperative algorithms model. Exchanging information between parallel cooperative algorithms will alter their behavior in terms of solution searching, and it may be more effective than a sequential metaheuristic. For demonstrating this, a parallel cooperative team of four multiobjective evolutionary algorithms based on OpenMP is proposed for solving different scenarios of the Motif Discovery Problem (MDP), which is an important real-world problem in the biological domain. As we will see, the results show that the application of a properly configured parallel cooperative team achieves high quality solutions when solving the addressed problem, improving those achieved by the algorithms executed independently for a much longer time.     

A multi-start tabu search method for a single-machine scheduling problem with periodic maintenance and sequence-dependent set-up times

In this paper we study a problem of sequencing jobs in a machine with programmed preventive maintenance and sequence-dependent set-up times. The problem combines two NP-hard problems, so we propose a heuristic method for solving it, which hybridizes multi-start strategies with Tabu Search. We compare our method with the only published metaheuristic algorithm for this problem on a set of 420 instances. The comparison favors the method developed in this work, showing that is able to find high quality solutions in very short computational times.     

A multiobjective approach based on artificial bee colony for the static routing and wavelength assignment problem

Nowadays, the most promising technology for designing optical networks is the wavelength division multiplexing. This technique divides the huge bandwidth of an optical fiber link into different wavelengths, providing different available channels per link. However, a problem comes up when it is necessary to interconnect a set of traffic demands. This problem is known as routing and wavelength assignment problem, and due to its complexity (NP-hard problem) it is very suitable for being solved using evolutionary computation. The selected heuristics is the artificial bee colony algorithm, an heuristics based on the behavior of honeybee foraging for nectar. Therefore, we have applied multiobjective optimization to solve the static routing and wavelength assignment problem, and adapted this algorithm to the multiobjective context. New results have been obtained that significantly improve those published in previous researches.     

Perspectives on control-relevant identification through the use of interactive tools

System identification is an essential part of control design. Control engineers must devote substantial effort to identification issues in order to obtain suitable models for closed-loop control. Control-relevant identification seeks to both simplify the modelling task and improve the usefulness of the model by taking into account controller requirements during system identification. The advantages of this methodology can be better understood and appreciated through the interactive software tool described in this paper. The Interactive Tool for Control Relevant Identification (ITCRI) comprehensively captures the control-relevant identification process for the monovariable problem, from input design to closed-loop control, depicting these stages simultaneously and interactively in one screen. By simultaneously displaying both open- and closed-loop responses of the estimated models, ITCRI enables the user to readily assess how design variable choices during identification and control performance requirements impact model error and ultimately, closed-loop performance. Moreover, the work presents several examples which the aim to illustrate the tool and the considerations that arise when control requirements are taken into account during the identification stage.     

Integrating supercomputing clusters into education: a case study in biotechnology

The Journal of Supercomputing - The integration of a Supercomputer in the educational process improves student’s technological skills. The aim of the paper is to study the interaction between...     

Facilitating the learning process in parallel computing by using instant messaging

Parallel Programming skills may require a long time to acquire. “Think in parallel” is a skill that requires time, effort, and experience. In this work, we propose to facilitate the students’ learning process in parallel programming by using instant messaging. Our aim was to find out whether students’ interaction through instant messaging tools is beneficial for the learning process. In order to do so, we asked several students of an HPC course of the Master’s degree in Computer Science of the University of León to develop a specific parallel application, each of them using a different application program interface: OpenMP, MPI, CUDA, or OpenCL. Even though the used APIs are different, there are common points in the design process. We encouraged students to interact with each other by using Gitter, an instant messaging tool for GitHub users. Our analysis of the communications and results demonstrate that the direct interaction of students through the Gitter tool has a positive impact on the learning process.

     

Identification of a binary distillation column

In this paper the identification of a binary distillation column simulated in a digital computer is described. The pseudo-random binary signal method has been used to disturb two inputs of the column. The well-known cross-correlation technique was used to compute the impulse response matrix G(s), and with a non-linear programming technique a parametric model has been obtained. These results were discretized in order to get a digital transfer matrix, and a discrete state space model suitable for digital control purposes was derived. The reduced-order linear model obtained was compared with the complete non-linear model of the distillation column. Problems involved with correction of the reduced model for any operating conditions are examined and a model-correction algorithm is suggested.     

LINGUISTIC FUZZY SYSTEMS AND THEIR APPLICATIONS IN ECONOMETRICS

This paper gives a brief summary of the linguistic fuzzy systems iheory and describes its possible application in econometric modelling. The essential feature of the used approach is a transformation of if-then rale bases into approximative deterministic behavior functions of considered economic systems.