Libro (Book): Optimizacion Combinatoria (Combinatorial Optimization)

Saludos Estimados:

Tengo el gusto de compartir con ustedes mi mas reciente publicacion, mi primer libro:

ISBN-13:	978-3-8465-6442-4
ISBN-10:	3846564427
EAN:	9783846564424
Book language:	Español

Optimización Combinatoria: Una perspectiva desde la teoría de autómatas provee herramientas para la optimización de problemas combinatorios fundamentando sus estrategias en la teoría de autómatas sin olvidar los mecanismos clásicos de optimización. Optimizar es un proceso que se lleva a cabo todos los días en nuestras vidas, por ejemplo constantemente deseamos maximizar los beneficios minimizando los costos. Los problemas combinatorios son problemas encontrados a diario en muchos sectores de la ingeniería. Están basados en la toma de decisiones y conllevan, en la mayoría de casos, a numerosas posibles soluciones que tomarían años ser revisadas. Por lo tanto, una aproximación a la solución de un problema real es considerada razonable, por ejemplo problemas en real-time que demandan soluciones inmediatas tales como la reprogramación de la producción por indisponibilidad de máquinas. El libro provee las bases necesarias para afrontar problemas cotidianos de la ingeniería así como algoritmos y programas en software especializado para la optimización multi-objetivo de problemas combinatorios.

Disponible en: 

https://www.morebooks.de/store/gb/book/optimizaci%C3%B3n-combinatoria/isbn/978-3-8465-6442-4

A NOVEL NON GRADIENT DEPENDENT METHOD FOR UNCONSTRAINED MULTIVARIATE OPTIMIZATION

Niño Elias D., Posada Hector, Rodriguez Robinson, Toro Luis. A Novel Non Gradient Dependent Method For Unconstrained Multivariate Optimization. Proceedings of the International Conference on Computer and Computational Intelligence, ASME, ISBN: 9780791859926, Bangkok – Thailand, December 2011.

ABSTRACT

This paper states a novel method based on the Hill Climbing for unrestricted multivariate optimization. The proposed method was compared against method from the specialized literature such as Multivariate Newton-Raphson and Multivariate Fletcher-Powell. For making a real comparison, metrics such as Number of Iteration, Processing Time and Stability of the Solution were taken into account. The results showed that the proposed method was the best with a good performance in the metrics, in some cases, of 100% out of 100%.

http://www.asme.org/products/books/international-conference-on-computer-and-computati

A NOVEL ALGORITHM FOR MULTIVARIATE OPTIMIZATION: MONARCHY METHOD

Niño Elias D., Ariza Angela, Arrieta Javier, Manjarres Jose. A Novel Algorithm For Multivariate Optimization: Monarchy Method. Proceedings of the International Conference on Computer and Computational Intelligence, ASME, ISBN: 9780791859926, Bangkok – Thailand, December 2011.

ABSTRACT

This paper proposes a novel method for the unconstrained multivariate optimization, which compared against methods from the specialized literature such as Newton-Raphson and Fletcher-Powell, improves the Processing Time. The proposal consists of cover the biggest part of the solution set of the function; evaluating points generated with simple operations to reach the goal of reduce the time. Finally, the novel method has been proved in an application problem.

http://www.asme.org/products/books/international-conference-on-computer-and-computati

STAIRS: A NOVEL MULTIVARIATE OPTIMIZATION METHOD BASED ON A UNIVARIATE APPROACH

Niño Elias D., Garrido Johan, Encinales Luis. STAIRS: A Novel Multivariate Optimization Method Based On A Univariate Approach. Proceedings of the International Conference on Computer and Computational Intelligence, ASME, ISBN: 9780791859926, Bangkok – Thailand, December 2011.

ABSTRACT

This article proposes a novel method for multivariate optimization unconstrained named Stairs, based on optimization in one variable. The proposed method is compared against methods from the specialized literature such as the Multivariate Newton-Raphson and the Multivariate Fletcher-Powell. The instances of the problems were taken from real life situations. For a real comparison, metrics such as Number of Iterations, Number of Instructions and Processing time were taken into account. Stairs showed a speed improvement relative to the compared methods in problems that include difficult differentiation because it does not use matrix operations.

http://www.asme.org/products/books/international-conference-on-computer-and-computati

A NOVEL METHOD FOR MULTIVARIABLE OPTIMIZATION WITHOUT CONSTRAINTS

Niño Elias D., Cuellar Sebastian, Delgado Steven, Dahmen Andres. A Novel Method For Multivariable Optimization Without Constraints. Proceedings of the International Conference on Computer and Computational Intelligence, ASME, ISBN: 9780791859926, Bangkok – Thailand, December 2011.

ABSTRACT

This paper presents a compilation of some kind of application examples of unrestricted multivariate optimization problems, using two principal methods one of this is Newton-Raphson method (NR) which is commonly used to calculate roots of a polynomial with only one variable, just with certain kind of changes to calculate roots with multiple variables. We use too the Fletcher-Powell method (FP) for multiple variables; this both methods are commonly used in some problems of optimization, in our case just with some changes to solve some cases where we are not working just with one variable but multiple variables and always looking for the optimization in different problems of some engineering areas. The novel algorithm is an improvement over the Newton’s method and can be classified as quasi-Newtonian.

http://www.asme.org/products/books/international-conference-on-computer-and-computati

A NOVEL METHOD FOR UNCONSTRAINED MULTIVARIATE OPTIMIZATION BASED ON FLETCHER REEVES THEORY

Niño Elias D., Pacheco Luis, Steer Mario, Perez Rafael. A Novel Method For Unconstrained Multivariate Optimization Based On Fletcher Reeves Theory. Proceedings of the International Conference on Computer and Computational Intelligence, ASME, ISBN: 9780791859926, Bangkok – Thailand, December 2011.

ABSTRACT

There are some methods for optimization problems, they differ in the way the reach the optimum, among these methods, those which are based on the function’s gradient have a great advantage as they find the fastest way to reach de objective, here we show three methods that base on this principle, two of them are part of our course, and a third one which we would like to propose, as it turns out to be very effective.

Through this research we achieve to implement and built a serial of algorithms that recreate the steps from mathematical structures design for solving the many challenging optimization issues that are found in an engineering career.

Based on our theory, seen on this course, and several extra sources we were provided with tools strong enough to understand and rebuilt such logic. The processes and results are exposed in this journal.

Not only are we going to solve a proposed example, but also we’re going to show how three different methods based on the same primitive concept can differ in quality, accuracy and speed.

http://www.asme.org/products/books/international-conference-on-computer-and-computati

A NOVEL ANT COLONY INSPIRED ALGORITHM FOR THE MONO-OBJECTIVE OPTIMIZATION OF COMBINATORIAL PROBLEMS

Niño Elias D. ET AL. A Novel Ant Colony Inspired Algorithm For The Mono-Objective Optimization Of Combinatorial Problems. Proceedings of the International Conference on Computer and Computational Intelligence, ASME, ISBN: 9780791859926, Bangkok – Thailand, December 2011.

ABSTRACT

The Ant Colony method is one of the most used metaheuristics in the analysis of Traveling Salesman Problem (TSP). Our objective in this research is to take one of the instances proposed by the research group of University of Heidelberg in Germany and apply this method to obtain the optimal solution that has been found so far. First of all, it is important to define aspects such as the TSP, metaheuristics, and then analyze how works the Ant Colony method, the characteristics that has the instance that we chose and finally the pseudocode. All this was done by consulting, and investigating, papers, articles and research done earlier by other scientists, mathematicians, and even students like us.

http://www.asme.org/products/books/international-conference-on-computer-and-computati