ACOustic: A nature-inspired exploration indicator for ant colony optimization

A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens’ acoustics of their ant hosts.The parasites’ reaction results from their ability to indicate the state of penetration.The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance’s matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied.The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms.Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation.The analytical results showed that the proposed indicator is more informative and more robust

ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization

A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens' acoustics of their ant hosts. The parasites' reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance's matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust

Machine learning into metaheuristics: A survey and taxonomy of data-driven metaheuristics

During the last years, research in applying machine learning (ML) to design efficient, effective and robust metaheuristics became increasingly popular. Many of those data driven metaheuristics have generated high quality results and represent state-of-the-art optimization algorithms. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this research topic. In this paper we will investigate different opportunities for using ML into metaheuristics. We define uniformly the various ways synergies which might be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem, low-level and high-level components of metaheuristics. Our goal is also to motivate researchers in optimization to include ideas from ML into metaheuristics. We identify some open research issues in this topic which needs further in-depth investigations

Programación de Operaciones en Procesos de Fabricación Aditiva

En este proyecto se analiza la tecnología de fabricación aditiva estudiando sus principales ventajas e inconvenientes. Una vez examinado dicho método de fabricación y las características de la maquinaria con la que se fabrican las piezas, se detecta que uno de los problemas que presenta es el alto tiempo que se necesita invertir en la fabricación de las piezas. Por lo tanto, se trata de solucionar dicho problema llevando a cabo una propuesta de un algoritmo el cual realice la programación de las operaciones para ofrecer así una solución que consiste en aportar una secuencia de trabajos en los cuales se definen que piezas van agrupadas en lotes y en que máquina deben procesarse dichos lotes, con el objetivo de minimizar el tiempo total de finalización (Makespan). En dicho problema intervienen distintos factores que son explicados convenientemente, como, por ejemplo, las características de las máquinas y de las piezas. Para su resolución se elabora una metaheurística del tipo de búsqueda de vecindario variable la cual incluye distintos algoritmos. Una vez confeccionada, se estudian los resultados obtenidos para una serie de instancias y se proponen distintas alternativas diferentes para así llevar a cabo una comparativa y finalmente decidir qué alternativa proporciona mejores resultados según los objetivos del usuario.In this project, additive manufacturing technology is analyzed, studying its main advantages and disadvantages. After examining said manufacturing method and the characteristics of the machinery with which the parts are manufactured, it is detected that one of the problems it presents is the high time that needs to be invested in the manufacture of the parts. Therefore, it is about solving this problem by carrying out a proposal of an algorithm which performs the programming of the operations to offer a solution that consists of providing a sequence of works in which the pieces are grouped in batches. and in which machine these batches should be processed, with the aim of minimizing the total completion time (Makespan). Different factors are involved in this problem that are conveniently explained, such as, for example, the characteristics of the machines and parts. For its resolution, a metaheuristic of the variable neighborhood search type is elaborated, which includes different algorithms. Once made, the results obtained for a series of instances are studied and different alternatives are proposed to carry out a comparison and finally decide which alternative provides better results according to the user's objectives.Universidad de Sevilla. Fin de Grado en Ingeniería de Tecnologías Industriale