Planificación automática de menús saludables y equilibrados

With the raise of diseases related with unhealthy lifestyles such as heartattacks, overweight, diabetes, etc., encouraging healthy and balanced patterns in the population is one of the most important action points for governments around the world. Furthermore, it is actually even a more critical situation when a high percentage of patients are children and teenagers whose habits consist merely in eating fast or ultra-processed food and a sedentary life. The development of healthy and balanced menu plans becomes a typical task for physicians and nutritionists, and it is at this point that Computer Science has taken an important role. Discovering new approaches for generating healthy and balanced, as well as inexpensive menu plans will play an important part in banish of diseases from actual and new generations. In this Master Thesis, a recently proposed Evolutionary Algorithm has been compared to other state-of-art evolutionary algorithms for solving the Menu Planning Problem. In order to evaluate the performance of the developed algorithm, an exhaustive experimental assessment was made. Firstly, we focused on evaluating the parameter setting of the algorithm so afterwards the best configuration found could be compared with other well-known algorithms

Development of a PSO metaheuristic for the menu planning problem with DCE preferences analysis

La planeación del menú es un problema de optimización que busca minimizar el costo de los insumos alimentarios para su preparación a la vez que se satisfacen las necesidades nutricionales. Este proyecto se aplicará en el Colegio Mayor de San Bartolomé, la cual es una escuela subsidiada con recursos limitados; por lo tanto, es pertinente mejorar su eficiencia en sus gastos, lo que se puede lograr minimizando sus costos. Para abordar este problema y comprender la compensación en las preferencias alimentarias que enfrentan los estudiantes de sexto a undécimo grado inscritos en la institución, así como el efecto que tiene sobre ellos el suministro de información nutricional, se diseña un DCE ( Discrete Choice Experiment ). Luego, se propone un modelo matemático de optimización lineal como una aproximación para resolver el problema de la planeación del menú, sin embargo, se muestra que este caso particular expone características de un problema NP-hard ; por tanto, una metaheurística como PSO ( Particle swarm optimization ) es muy conveniente. Esta metaheurística tiene como objetivo minimizar tanto la desviación nutricional en cuanto al requerimiento como el costo de los suministros alimentarios y generar una solución de menú variada, contemplando un horizonte temporal de 15 días. Por lo tanto, se desarrolla un algoritmo considerando el PSO, las preferencias estimadas y las restricciones especificadas por la escuela, generando soluciones para los rangos de edad previamente estipulados de acuerdo con el resultado de un clustering jerárquico. Finalmente, una aplicación diseñada bajo la norma ISO/IEC 25010 facilita la entrada de datos y la visualización de los resultados.Menu planning is an optimization problem that seeks to minimize the cost of food inputs for its preparation while satisfying nutritional needs. This project will be applied in Colegio Mayor de San Bartolomé, which is a subsidized school with limited resources; therefore, it is pertinent to improve their efficiency in their expenditures, which can be achieved by optimally minimizing their costs. In order to address this problem and understand the tradeoff in food preferences faced by sixth to eleven grade students enrolled in the institution, as well as the effect that the provision of nutritional information has on them, a DCE (Discrete Choice Experiment) is designed. Then, a linear optimization mathematical model is proposed as an approximation to solve the menu planning problem however, it is shown that this particular case exposes characteristics of an NP-hard problem; thus, a metaheuristic such as PSO (Particle Swarm Optimization) is highly convenient. This metaheuristic aims to minimize both the nutritional deviation concerning the requirement and the cost of food supplies and generate a varied menu solution, contemplating a time horizon of 15 days. Therefore, an algorithm considering the PSO, the estimated preferences, and the restrictions specified by the school, is developed, generating solutions for the previously stipulated age ranges according to the result of hierarchical clustering. Finally, an application designed under the ISO / IEC 25010 standard facilitates the entry of data and the visualization of the results.Ingeniero (a) IndustrialPregrad

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms