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