44- #1105 DISEÑO DE UN ALGORITMO HÍBRIDO GENÉTICO PARA EL PROBLEMA DE PROGRAMACIÓN DE PROYECTOS CON RESTRICCIÓN DE RECURSOS (RCPSP).

“Los problemas de planeación de proyectos son muycomunes en cualquier tipo de industria u organización,ya que pueden ser aplicados a la producción industrial,proyectos de construcción, prestación de servicios,actividades cotidianas y rutinaria, entre otras” (Rivera yCelín, 2010) en el sentido práctico, un proyecto “seprograma bajo el método de ruta crítica tradicional(CPM) en el que los recursos se consideran ilimitados”(Hegazy, Shabeeb, Elbeltalgi y Cheema, 2000) pero enla realmente los recursos son limitados, por tal motivo,se adopta el modelo del Problema de Programación deProyectos con Restricción de Recursos (ResourceConstrained Project Scheduling Problem, RCPSP) elcual considera restricciones activas de precedencia yde recursos limitados. Actualmente, el RCPSP es unode los problemas más importantes en el contexto deprogramación de proyectos” (Abbasi, Shadrokh y Arkat,2006) como consecuencia de la restricción de recursosy el aumento del número de actividades a programar,se puede transformar en un problema de tipo NP-Hard(Blazewicz, Lenstra y Kan, 1983) por consiguiente, se han utilizado Heurísticas, Metaheurísticas e Híbridos para dar solución. Finalmente, el interés creciente eninvestigación de operaciones ha dado lugar a pasar deMetaheurísticas puras a Métodos Híbridos basados endiferentes estrategias Metaheurísticas para resolver elRCPSP (Pellerin, Perrier y Berthaut, 2019) por talmotivo en esta investigación se plantea una estrategiade Hibridación Integrativa, conformada por unAlgoritmo Genético (GA) y un Recocido Simulado (SA)el cual intensifica la busqueda en los vecindarios decada mutación realizada, teniendo como funciónobjetivo minimizar la duración del proyecto (makespan).Basado en Hwang y He (2006) una combinaciónadecuada de GA con SA proporciona una alternativaeficaz para problemas complejos de optimización combinatoria además el uso híbrido de GA con SAmejora el rendimiento de GA para problemas deingeniería. &nbsp

Double Helix Structure and Finite Persisting Sphere Genetic Algorithm in Designing Digital Circuit Structure

This paper proposes a new approach of chromosome representation in digital circuit design which is Double Helix Structure (DHS). The idea of DHS in chromosome representation is inspired from the nature of the DNA\u27s structure that built up the formation of the chromosomes. DHS is an uncomplicated design method. It uses short chromosome string to represent the circuit structure. This new structure representation is flexible in size where it is not restricted by the conventional matrix structure representation. There are some advantages of the proposed method such as convenience to apply due to the simple formation and flexible structure, less requirement of memory allocation and faster processing time due to the short chromosomes representation. In this paper, DHS is combined with Finite Persisting Sphere Genetic Algorithm (FPSGA) to optimal the digital circuit structure design. The experimental results prove that DHS uses short chromosome string to produce the flexible digital circuit structure and FPSGA further optimal the number of gates used in the structure. The proposed method has better performance compared to other methods

Double Helix Structure and Finite Persisting Sphere Genetic Algorithm in Designing Digital Circuit Structure

This paper proposes a new approach of chromosome representation in digital circuit design which is Double Helix Structure (DHS). The idea of DHS in chromosome representation is inspired from the nature of the DNA\u27s structure that built up the formation of the chromosomes. DHS is an uncomplicated design method. It uses short chromosome string to represent the circuit structure. This new structure representation is flexible in size where it is not restricted by the conventional matrix structure representation. There are some advantages of the proposed method such as convenience to apply due to the simple formation and flexible structure, less requirement of memory allocation and faster processing time due to the short chromosomes representation. In this paper, DHS is combined with Finite Persisting Sphere Genetic Algorithm (FPSGA) to optimal the digital circuit structure design. The experimental results prove that DHS uses short chromosome string to produce the flexible digital circuit structure and FPSGA further optimal the number of gates used in the structure. The proposed method has better performance compared to other methods

Application of Evolutionary Algorithms in the Study of Fractional Order Electromagnetic Phenomena

The application of evolutionary algorithms in optimization is currently receiving growing interest from researchers with various backgrounds. A genetic algorithm (GA) is a search technique based on the natural selection process that provides a unique flexibility and robustness for process optimization. Recently, a closer look of some phenomena present in electrical systems and the motivation towards the development of comprehensive models, pointed out the requirement for a fractional calculus approach. In this work, it is applied the concept of fractional calculus to define, and to evaluate, the electrical potential of fractional order. Bearing these ideas in mind, the paper addresses the analysis and the synthesis of fractional-order multipoles, based in a GA optimization scheme.N/

Calibrating the Micromechanical Parameters of the PFC2D(3D) Models Using the Improved Simulated Annealing Algorithm

PFC2D(3D) is commercial software, which is commonly used to model the crack initiation of rock and rock-like materials. For the PFC2D(3D) numerical simulation, a proper set of microparameters need to be determined before the numerical simulation. To obtain a proper set of microparameters for PFC2D(3D) model based on the macroparameters obtained from physical experiments, a novel technique has been carried out in this paper. The improved simulated annealing algorithm was employed to calibrate the microparameters of the numerical simulation model of PFC2D(3D). A Python script completely controls the calibration process, which can terminate automatically based on a termination criterion. The microparameter calibration process is not based on establishing the relationship between microparameters and macroparameters; instead, the microparameters are calibrated according to the improved simulated annealing algorithm. By using the proposed approach, the microparameters of both the contact-bond model and parallel-bond model in PFC2D(3D) can be determined. To verify the validity of calibrating the microparameters of PFC2D(3D) via the improved simulated annealing algorithm, some examples were selected from the literature. The corresponding numerical simulations were performed, and the numerical simulation results indicated that the proposed method is reliable for calibrating the microparameters of PFC2D(3D) model

Process capability assessment for univariate and multivariate non-normal correlated quality characteristics

In today's competitive business and industrial environment, it is becoming more crucial than ever to assess precisely process losses due to non-compliance to customer specifications. To assess these losses, industry is extensively using Process Capability Indices for performance evaluation of their processes. Determination of the performance capability of a stable process using the standard process capability indices such as and requires that the underlying quality characteristics data follow a normal distribution. However it is an undisputed fact that real processes very often produce non-normal quality characteristics data and also these quality characteristics are very often correlated with each other. For such non-normal and correlated multivariate quality characteristics, application of standard capability measures using conventional methods can lead to erroneous results. The research undertaken in this PhD thesis presents several capability assessment methods to estimate more precisely and accurately process performances based on univariate as well as multivariate quality characteristics. The proposed capability assessment methods also take into account the correlation, variance and covariance as well as non-normality issues of the quality characteristics data. A comprehensive review of the existing univariate and multivariate PCI estimations have been provided. We have proposed fitting Burr XII distributions to continuous positively skewed data. The proportion of nonconformance (PNC) for process measurements is then obtained by using Burr XII distribution, rather than through the traditional practice of fitting different distributions to real data. Maximum likelihood method is deployed to improve the accuracy of PCI based on Burr XII distribution. Different numerical methods such as Evolutionary and Simulated Annealing algorithms are deployed to estimate parameters of the fitted Burr XII distribution. We have also introduced new transformation method called Best Root Transformation approach to transform non-normal data to normal data and then apply the traditional PCI method to estimate the proportion of non-conforming data. Another approach which has been introduced in this thesis is to deploy Burr XII cumulative density function for PCI estimation using Cumulative Density Function technique. The proposed approach is in contrast to the approach adopted in the research literature i.e. use of best-fitting density function from known distributions to non-normal data for PCI estimation. The proposed CDF technique has also been extended to estimate process capability for bivariate non-normal quality characteristics data. A new multivariate capability index based on the Generalized Covariance Distance (GCD) is proposed. This novel approach reduces the dimension of multivariate data by transforming correlated variables into univariate ones through a metric function. This approach evaluates process capability for correlated non-normal multivariate quality characteristics. Unlike the Geometric Distance approach, GCD approach takes into account the scaling effect of the variance-covariance matrix and produces a Covariance Distance variable that is based on the Mahanalobis distance. Another novelty introduced in this research is to approximate the distribution of these distances by a Burr XII distribution and then estimate its parameters using numerical search algorithm. It is demonstrates that the proportion of nonconformance (PNC) using proposed method is very close to the actual PNC value

Hybrid training approaches to Hidden Markov Model-based acoustic models for automatic speech recognition

Doctor of Philosoph