A Teaching-Learning-Based Optimization Algorithm for the Weighted Set-Covering Problem

The need to make good use of resources has allowed metaheuristics to become a tool to achieve this goal. There are a number of complex problems to solve, among which is the Set-Covering Problem, which is a representation of a type of combinatorial optimization problem, which has been applied to several real industrial problems. We use a binary version of the optimization algorithm based on teaching and learning to solve the problem, incorporating various binarization schemes, in order to solve the binary problem. In this paper, several binarization techniques are implemented in the teaching/learning based optimization algorithm, which presents only the minimum parameters to be configured such as the population and number of iterations to be evaluated. The performance of metaheuristic was evaluated through 65 benchmark instances. The results obtained are promising compared to those found in the literature

Binary Fruit Fly Swarm Algorithms for the Set Covering Problem

Currently, the industry is experiencing an exponential increase in dealing with binary-based combinatorial problems. In this sense, metaheuristics have been a common trend in the field in order to design approaches to solve them successfully. Thus, a well-known strategy consists in the use of algorithms based on discrete swarms transformed to perform in binary environments. Following the No Free Lunch theorem, we are interested in testing the performance of the Fruit Fly Algorithm, this is a bio-inspired metaheuristic for deducing global optimization in continuous spaces, based on the foraging behavior of the fruit fly, which usually has much better sensory perception of smell and vision than any other species. On the other hand, the Set Coverage Problem is a well-known NP-hard problem with many practical applications, including production line balancing, utility installation, and crew scheduling in railroad and mass transit companies. In this paper, we propose different binarization methods for the Fruit Fly Algorithm, using S-shaped and V-shaped transfer functions and various discretization methods to make the algorithm work in a binary search space. We are motivated with this approach, because in this way we can deliver to future researchers interested in this area, a way to be able to work with continuous metaheuristics in binary domains. This new approach was tested on benchmark instances of the Set Coverage Problem and the computational results show that the proposed algorithm is robust enough to produce good results with low computational cost.publishedVersio

A Meta-Optimization Approach to Solve the Set Covering Problem

Context: In the industry the resources are increasingly scarce. For this reason, we must make a good use of it. Being the optimization tools, a good alternative that it is necessary to bear in mind. A realworld problem is the facilities location being the Set Covering Problem, one of the most used models. Our interest, it is to find solution alternatives to this problem of the real-world using metaheuristics. Method: One of the main problems which we turn out to be faced on having used metaheuristic is the difficulty of realizing a correct parametrization with the purpose to find good solutions. This is not an easy task, for which our proposal is to use a metaheuristic that allows to provide good parameters to another metaheuristics that will be responsible for resolving the Set Covering Problem. Results: To prove our proposal, we use the set of 65 instances of OR-Library which also was compared with other recent algorithms, used to solve the Set Covering Problem. Conclusions: Our proposal has proved to be very effective able to produce solutions of good quality avoiding also have to invest large amounts of time in the parametrization of the metaheuristic responsible for resolving the problem

Un Enfoque de Meta-Optimización para Resolver el Problema de Cobertura de Conjunto

Context: In the industry the resources are increasingly scarce. For this reason, we must make a gooduse of it. Being the optimization tools, a good alternative that it is necessary to bear in mind. A realworldproblem is the facilities location being the Set Covering Problem, one of the most used models.Our interest, it is to find solution alternatives to this problem of the real-world using metaheuristics. Method: One of the main problems which we turn out to be faced on having used metaheuristic is thedifficulty of realizing a correct parametrization with the purpose to find good solutions. This is not aneasy task, for which our proposal is to use a metaheuristic that allows to provide good parameters toanother metaheuristics that will be responsible for resolving the Set Covering Problem. Results: To prove our proposal, we use the set of 65 instances of OR-Library which also was comparedwith other recent algorithms, used to solve the Set Covering Problem. Conclusions: Our proposal has proved to be very effective able to produce solutions of good qualityavoiding also have to invest large amounts of time in the parametrization of the metaheuristic responsiblefor resolving the problem.Contexto: En la industria los recursos son cada vez más escasos. Por esta razón debemos hacer un buen uso de ellos.Siendo las herramientas de optimización una buena alternativa que se debe tener presente. Un problema del mundo real lo contituye la ubicación de instalaciones siendo el Problema de Cobertura de Conjuntos uno de los modelos más utilizados. Nuestro interés, es encontrar alternativas de solución a este problema de la vida-real utilizando metaheuristicas. Método: Uno de los principales problemas a que nos vemos enfrentados al utilizar metaheurísticas es la dificultad de realizar una correcta parametrización con el objetivo de encontrar buenas soluciones. Esta no es una tarea fácil, para lo cual nuestra propuesta es utilizar una metaheurística que permita proporcionar buenos parametros a otra metaheurstica que será la encargada de resolver el Problema de Cobertura de Conjuntos. Resultados: Para probar nuestra propuesta, utilizamos el set de 65 instancias de OR-Library el cual además fue comparado con otros recientes algoritmos utilizados para resolver el Problema de Cobertura de Conjuntos. Conclusiones: Nuestra propuesta a demostrado ser muy efectiva logrando producir soluciones de buena calidad evitando además que se tenga que invertir gran cantidad de tiempo en la parametrización de la metaheurística encargada de resolver el problema

Tissue segmentation using medical image processing chain optimization

Surveyed literature shows many segmentation algorithms using different types of optimization methods. These methods were used to minimize or maximize objective functions of entropy, similarity, clustering, contour, or thresholding. These specially developed functions target specific feature or step in the presented segmentation algorithms. To the best of our knowledge, this thesis is the first research work that uses an optimizer to build and optimize parameters of a full sequence of image processing chain. This thesis presents a universal algorithm that uses three images and their corresponding gold images to train the framework. The optimization algorithm explores the search space for the best sequence of the image processing chain to segment the targeted feature. Experiments indicate that using differential evolution to build Image processing chain (IPC) out of forty-five algorithms increases the segmentation performance of basic thresholding algorithms ranging from 2% to 78%

Usabilidade pedagógica: um fator determinante na adoção do e-Learning no ensino superior

O artigo que apresentamos neste simpósio doutoral surge no âmbito do Curso de Doutoramento em Educação, especialidade em Tecnologias de Informação e Comunicação na Educação, do Instituto de Educação da Universidade de Lisboa. O estudo tem como objetivo principal propor e testar um modelo que permita explicar a intenção comportamental dos docentes do Ensino Superior aquando da adoção e uso continuado das plataformas de e-Learning. Para o efeito procura-se compreender o contributo da usabilidade pedagógica como fator determinante no processo de adoção da tecnologia