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

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

A COLLABORATIVE FRAMEWORK FOR DISTRIBUTED MULTI-OBJECTIVE COMBINATORIAL OPTIMIZATION

Niño Elias D., Caicedo William, Salcedo Omer. A Collaborative Framework For Distributed Multi-Objective Combinatorial Optimization. Proceedings of the International Conference on Computer and Computational Intelligence, ASME, ISBN: 9780791859926, Bangkok – Thailand, December 2011.

ABSTRACT

This paper states a collaborative framework for the distributed multiobjective optimization of combinatorial problems. The proposed framework is completely agnostic to the specific specialized metaheuristic used. Thus, it is able to use different hybrid strategies using two or more metaheuristics in a collaborative fashion. Besides, the designed framework uses a central repository of non-dominated solutions. The solutions are further processed in different nodes (machines) and later go back to the central repository. On the other hand, once the metaheuristic has converged to a new solution its quality is checked, and if it is a non-dominated solution then it is stored in the central repository to be used by other nodes (possibly executing a different metaheuristic) as a new starting point. Lastly, we tested the proposed framework using metrics from the specialized literature. Results show a consistent improvement of the Pareto Front as the number of nodes is increased.

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

MIDRS: A METAHEURISTIC BASED ON DETERMINISTIC FINITE AUTOMATA AND SIMULATED ANNEALING TECHNIQUE FOR BI-OBJECTIVE OPTIMIZATION COMBINATORIAL PROBLEMS

Niño Elias D., Sarabia Justo, Ardila Carlos. Nieto Wilson, Barrios Agustin. MIDRS: A Metaheuristic Based On Deterministic Finite Automata And Simulated Annealing Technique For Bi-Objective Optimization Combinatorial Problems. Proceedings of the International Conference on Computer and Computational Intelligence, ASME, ISBN: 9780791859926, Bangkok – Thailand, December 2011.

ABSTRACT

We describe a strategy for optimizing bi-objective combinatorial problems. Initially, we design and implement a metaheuristic of complexity O(n^4 ) for optimizing combinatorial problems. This metaheuristic is appointing Metaheuristic Deterministic Interchange on Automata with Simulated Annealing (MIDRS - Metaheurística de Intercambio Determinista sobre Autómatas con Recocido Simulado). MIDRS based its strategy on the theory of Deterministic Finite Automata Multi – Objective. Subsequently, we analyzed the behavior of the technique by varying the weight ratio between the objective functions. Finally, MIDRS is contrasted with high-impact global metaheuristic such as, algorithms based on Ant Colony, Evolutionary Techniques and Strategies Local Search.

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

A NEW METAHEURISTIC BASED ON DETERMINISTIC FINITE AUTOMATON FOR MULTI-OBJECTIVE OPTIMIZATION OF COMBINATORIAL PROBLEMS

Niño Elias D., Ardila Carlos. A New Metaheuristic Based On Deterministic Finite Automaton For Multi-Objective Optimization Of Combinatorial Problems. Proceedings of the International Conference on Computer and Computational Intelligence, ASME, ISBN: 9780791859926, Bangkok – Thailand, December 2011.

ABSTRACT

In this paper we state a novel metaheuristic based on Deterministic Finite Automaton (DFA) for the multi-objective optimization of combinatorial problems. First, we propose a new DFA based on Swapping (DFAS). DFAS allows the representation of feasible solutions space of combinatorial problems. Last, we define an algorithm that works with DFAS, it is named Exchange Deterministic Algorithm (EDA). EDA has three steps. The first step consists in create the initial solutions, the second step improves the initial solutions and the last step uses transitions between the states of the DFAS for improving the solutions. EDA was tested using well known instances of the Bi-objective Traveling Salesman Problem (TSP). EDA results were compared against Exhaustive Techniques from the specialized literature using Multiobjective Metrics. The results shows that EDA solutions are close to the Optimal Solutions.

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

A HYBRID IMPROVING SCHEMA BETWEEN ID3 ALGORITHMS AND NAIVE BAYES CLASSIFIERS AND ITS APPLICATION TO THE POPULATION DATABASE OF BREAST CANCER

Niño Elias D., Nieto Wilson, Riascos Carlos. A Hybrid Improving Schema Between Id3 Algorithms And Naive Bayes Classifiers And Its Application To The Population Database Of Breast Cancer. Proceedings of the International Conference on Computer and Computational Intelligence, ASME, ISBN: 9780791859926, Bangkok – Thailand, December 2011.

ABSTRACT

Analyzed the principles of the ID3 algorithm, this creates rules based on the concepts of entropy and gain with prepared data set. On the other hand, naïve Bayes classifier, allow us to classify through of the prepared data set considered probabilistic evidence. We propose a hybrid schema based on the ID3 algorithm and the naïve Bayes classifier that let us to improve the accuracy in classification tasks. We believe that this may be useful in many types of applications, so this schema serve as a support tool for research as a way to make decisions. Finally, we use experiment to prove that the hybrid schema increase the accuracy being applied to population databases of breast cancer.

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

IMPROVING THE CLUSTERING ALGORITHM K -MEA NS USING A NEW DISTANCE FUNCTION AND ITS APPLICATION TO THE POPULATION DATABASES OF BREAST CANCER

Niño Elias D., Nieto Wilson, Bolivar Juan. Improving The Clustering Algorithm K -Mea Ns Using A New Distance Function And Its Application To The Population Databases Of Breast Cancer, Proceedings of the International Conference on Computer and Computational Intelligence, ASME, ISBN: 9780791859926, Bangkok – Thailand, December 2011.

ABSTRACT

In this paper we propose an improvement to the clustering heuristic algorithm K-means. This improvement has been tested with databases of breast cancer. Today, clustering problems are everywhere; we can see its application in data mining, learning machines, knowledge discovery, data compression, pattern recognition, among others. One of the most popular and used clustering methods is the K -means , on this algorithm has been worked hard, basically have made several improvements, many of these base d on the definition of the initial parameters. In contrast, this paper proposes a ne w function to calculate the distance; this improvement comes from the experimental analysis of the classical algorithm. Experimentally, the improved algorithm showed a better quality solution being applied to population databases of breast cancer. Finally, we believe that this improvement may be useful in many types of applications, so this application can serve as a support tool for research on breast cancer and as a decision making in the allocation of resources for prevention and treatment.

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

BAYESSIAN CLASIFFIER SUPPORTED BY COVERAGE AND ACURACY IN BREAST CANCER DETECTION

Niño Elias D., Nieto Wilson, Felizzola Ricardo. Bayessian Clasiffier Supported By Coverage And Acuracy In Breast Cancer Detection.Proceedings of the International Conference on Computer and Computational Intelligence, ASME, ISBN: 9780791859926, Bangkok – Thailand, December 2011.

ABSTRACT

This article shows the importance of Bayesian classifiers for prediction in data mining, also as important components such as coverage and accuracy may improve the classification performance in themselves an analysis by performing a mathematical model such as Naive Bayes can be improved by adding coverage and precision. Finally, we believe that this improvement may be useful in many types of applications, so this application can serve as a support tool for research on breast cancer and as a decision making in the allocation of resources for prevention and treatment, also can also be used in previous applications to be improved in many ways.

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

CHAPTER BOOK: Evolutionary Algorithms based on te Automata Theory for the Multiobjective Optimization of Combinatorial Problems

Hi everyone:

This is my first chapter book, here is the information of the book:

Genetic Algorithm / Book 2 ISBN 979-953-307-879-2 The book covers the next topics:

* Evolutionary algorithms
* Evolutionary computing
* Metaheuristics
* Stochastic optimization
* Optimization
* Genetic representation
* Population growth estimation
* Web intelligence domains
* Pattern recognition
* Data mining
* Soft computing
* Bioinformatics
* Learning systems

It will be avaliable by January 2012. In this chapter I review all the techniques proposed by me and a novel metaheuristic is designed. Also, the tested are considered very hard, I work with instance from Bi to Quint-objectives. Here is a brief description of the chapter content:

1. Introduction. (Research regarding to Combinatorial Optimization)

2. Preliminares.
2.1. Multiobjective Optimization
2.2. Genetic Algorithms
2.3. Simulated Annealing Algorithms
2.4. Metaheuristic of Deterministic Swapping (MODS).

3. Simulated Annealing Metaheuristic of Deterministic Swapping (SAMODS)

4. Evolutionary Metaheuristic of Deterministic Swapping (EMODS)

5. Genetic Simulated Annealing Metaheuristic of Deterministic Swapping (SAGAMODS)

6. Experimental Analysis
6.1. Experimental Settings.
6.2. Performance Metrics (Generations of No Dominated Vector (GNDV), Spacing (S), Generational Distance (GD), Inverse Generational Distance (IGD)...)
6.3. Experimental Results
6.4. Analysis

7. Conclusions.

8. References.

A graphical comparison of the results for three objective instances is shown in the next figure:

SAMODS & SAGAMODS: Novel Algorithms based on the Automata Theory for the Multiobjective Optimization of Combinatorial Problems

This paper was accepted for publication in The International Journal of Artificial Intelligence. Special Issue in Metaheuristic in Artificial Intelligence. It will be available by January of 2012.

Authors: Elias D. Nino

Here is the abstract:

This paper states two novel algorithms based on the Automata Theory for the Multiobjective Optimization of Combinatorial Problems. The first algorithm proposed is named SAMODS. It is a hybrid Simulated Annealing MODS inspired algorithm. The main idea behind this approach consists in optimizing a Combinatorial Problem changing the Angle Improvement; this is a novel theory maintain in the classic weighted sum metric. SAMODS avoids unfeasible solutions fall back on the MODS theory. Also, it avoids local optimum due to the use of Boltzmann Distribution Probability for accepting bad solutions as good solutions. The last proposed algorithm was named SAGAMODS. It is an Evolutionary Algorithm based on the MODS theory. In addition to the advantages of SAMODS, SAGAMODS avoids in two times local optimums because of its Crossover Step. It is taken from the Natural Selection Theory that allows creating new solutions (next generation) support in the current solutions (actual generation). Only the best solutions survive. The proposed algorithms were tested using instances from the well-known TSPLIB. The test was made using problems with two objectives, three objectives, four objectives and five objectives inclusive. The proposed algorithms were compared using metrics from the specialized literature of the Multiobjective Optimization. The results of the metrics applied to the algorithms shows that MODS algorithm was superseded up to 100% out of 100%, in some of the instances worked, by the proposed algorithms.

Graphic Comparison MODS, SAMODS and SAGAMODS for Three-objectives instances of TSP:

For a detail definition and analysis of the results, wait for the entire paper.

MODS: A Novel Metaheuristic of Deterministic Swapping for the Multi – Objective Optimization of Combinatorials Problems

Tipo de Publicación: Articulo
Categoria: C (COLCIENCIAS)
Idioma: Inglés
Revista: Elias D. Niño, Carlos J. Ardila , Agustín Barrios and Yezid Donoso. MODS: A Novel Metaheuristic of Deterministic Swapping for the Multi – Objective Optimization of Combinatorials Problems. Computer Technology and Application. Volume 2, Number 4, 2011

Abstract

This paper states a new metaheuristic based on Deterministic Finite Automata (DFA) for the multi – objective optimization of combinatorial problems. First, a new DFA named Multi – Objective Deterministic Finite Automata (MDFA) is defined. MDFA allows the representation of the feasible solutions space of combinatorial problems. Second, it is defined and implemented a metaheuritic based on MDFA theory. It is named Metaheuristic Of Deterministic Swapping (MODS). MODS is a local search strategy that works using a MDFA. Due to this, MODS never take into account unfeasible solutions. Hence, it is not necessary to verify the problem constraints for a new solution found. Lastly, MODS is tested using well know instances of the Bi – Objective Traveling Salesman Problem (TSP) from TSPLIB. Its results were compared with eight Ant Colony inspired algorithms and two Genetic algorithms taken from the specialized literature. The comparison was made using metrics such as Spacing, Generational Distance, Inverse Generational Distance and No-Dominated Generation Vectors. In every case, the MODS results on the metrics were always better and in some of those cases, the superiority was 100%.

A novel Algorithm based on Deterministic Finite Automaton for solving the mono-objective Symmetric Traveling Salesman Problem

Tipo de Publicación: Articulo
Categoria: SCOPUS
Idioma: Inglés
Revista: Elias Niño, Carlos Ardila, Daladier Jabba, Yezid Donoso. A novel Algorithm based on Deterministic Finite Automaton for solving the mono-objective Symmetric Traveling Salesman Problem. International Journal of Artificial Intelligence, Volume 5, Number A10. ISSN 0974-0635. 2010.

Abstract

This paper states an Algorithm Based on Finite Automata: the Exchange Deterministic Algorithm (EDA). EDA is an improvement of the EDNR algorithm(Ausiello, Feuerstein, Leonardi, Stougie and Talamo, 2001). that is applied to one of the classic problems in combinatorial optimization: The Symmetric Traveling Salesman Problem (TSP). First, the feasible solutions space of TSP was modeled by using a Deterministic Finite Automaton of Swapping (DFAS). DFAS is a data structure that allows the representation of the feasible solutions space of a combinatorial problem. Once that structure is defined, the algorithm is applied to different random scenarios of the TSP in order to contrast proximity with the global optimum obtained by exhaustive searching. Afterwards, the algorithm is applied to a particular problem, and the results are compared with others that were obtained by techniques such as Ant Colony, GRASP, Genetic Algorithms, Simulated Annealing, and Tabu Search(Bin and Raidl, 2008). EDA surpassed the others in terms of number of iterations the optimum is obtained.

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