Quasi - Geostrophic Simulation

CFP - IJAI - Combinatorial Optimization 2013 : Special issue of IJAI on Combinatorial Optimization

CFP - IJAI - Combinatorial Optimization 2013 : Special issue of IJAI on Combinatorial Optimization

Call for Papers: Special Issue on Combinatorial Optimization

Special issue of IJAI on Combinatorial Optimization
 
Combinatorial Optimization is a branch of Optimization in which problems can be represented (or reduced) to discrete structures. Typically, in this kind of problems, the size of the feasible solution space increases exponentially with regard to the input parameters (or variables). Due to this, the analytical computation of true solutions (global solutions) for combinatorial problems involves high computational efforts. Thus, proposals such as Evolutionary Methods, Simulated Annealing inspired algorithms, Automata algorithms and Heuristics strategies have been designed in order to approach the set of optimal solutions. Nevertheless, no one, in general, guarantees the global solutions for combinatorial problems.
 
This special issue of the International Journal of Artificial Intelligence (IJAI) invites contributions and reviews of the latest developments in methods and their applications for the solution of combinatorial problems. Topics include (but they are not limited to):
 
1. Heuristics for the solution of combinatorial problems. 
2. Theoretical formulation and implementation.
3. Parallel computing for the solution of combinatorial problems.
4. Design and implementation of metaheuristics for the solution of  real-life combinatorial problems (networking design, planning and scheduling)
5. Hybrid metaheuristics for the solution of combinatorial problems.
 
Interested authors are solicited to make their original contributions in the above areas and contact one of the guest editors at: elias.d.nino@gmail.com or ydonoso@uniandes.edu.co or iv.saavedra@gmail.com.
 
For details and submission information concerning the International Journal of Artificial Intelligence (IJAI), please visit online:
 
http://www.ceser.in/ijai.html
 
Important Dates:
 
July 30, 2012:  Expression of interest (title and abstract to guest editors)
August 30, 2012:   Full manuscript and cover letter
December 30, 2012:   Review comments and decision
February 30, 2013:   Revised, final manuscript
 
Guest Editors:
 
Elias D. Niño-Ruiz
Assistant Professor
Department of Computer Science
Universidad del Norte, Barranquilla, Colombia
Full Time Researcher
Department of Computer Science
Virginia Tech, Blacksburg, VA 24060, USA
(email: elias.d.nino@gmail.com )
 
Yezid Donoso
Assistant Professor
Department of Computer Science
Universidad de los Andes
Bogota, Colombia
(email: ydonoso@uniandes.edu.co)
 
Ivan Saavedra-Antolinez
Full Time Researcher
Department of Industrial Engineering
University of Wisconsin – Milwaukee
Milwaukee, WI  53201, USA
(email: iv.saavedra@gmail.com)

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