Modified movement force vector in an electromagnetism-like mechanism for global optimization

This paper presents an algorithm for solving global optimization problems with bounded variables. The algorithm is a modification of the electromagnetism-like mechanism proposed by Birbil and Fang [An electromagnetism-like mechanism for global optimization, J. Global Optim. 25 (2003), pp. 263–282]. The differences are mainly on the local search procedure and on the force vector used to move each point in the population. Several widely-used benchmark problems were solved in a performance evaluation of the new algorithm when compared with the original one. A comparison with other stochastic methods is also included. The algorithm seems appropriate for large dimension problems

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices

Algoritmos metaheurísticos para la segmentación de imágenes

Uno de los temas más tratados en la comunidad de procesamiento de imágenes es la segmentación, consistente en obtener estructuras subyacentes para facilitar su interpretación, por ejemplo, obtener bordes o agrupaciones de píxeles que forman regiones con alguna propiedad. Dado que se utiliza como un paso de preprocesamiento antes de las tareas de visión por computador de alto nivel, como el reconocimiento de objetos y la representación de imágenes, se han propuesto diversos enfoques para la segmentación, que se centran en la mejora de la calidad de los procesos aplicados para conseguir los mejores resultados posibles. Sin embargo, en muchos casos el coste computacional de estas técnicas puede ser elevado, limitando su uso. En el área de la optimización global se han propuesto una gran cantidad de algoritmos metaheurísticos (AM) para resolver problemas complejos de ingeniería en un tiempo razonable. Los AMs son algoritmos de búsqueda estocásticos que utilizan reglas o heurísticas aplicables a cualquier problema para acelerar su convergencia a soluciones cercanas al óptimo. Es común observar que los AMs emulan procesos y comportamientos inspirados por mecanismos presentes en la naturaleza, como la evolución..