Recommending Healthy Meal Plans by Optimising Nature-Inspired Many-Objective Diet Problem

Healthy eating is an important issue affecting a large part of the world population, so human diets are becoming increasingly popular, especially with the devastating consequences of Coronavirus Disease (Covid-19). A realistic and sustainable diet plan can help us to have a healthy eating habit since it considers most of the expectations from a diet without any restriction. In this study, the classical diet problem has been extended in terms of modelling, data sets and solution approach. Inspired by animals’ hunting strategies, it was re-modelled as a many-objective optimisation problem. In order to have realistic and applicable diet plans, cooked dishes are used. A well-known many-objective evolutionary algorithm is used to solve the diet problem. Results show that our approach can optimise specialised daily menus for different user types, depending on their preferences, age, gender and body index. Our approach can be easily adapted for users with health issues by adding new constraints and objectives. Our approach can be used individually or by dietitians as a decision support mechanism

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

Aquaculture production optimisation in multicage farms subject to commercial and operational constraints

The new advances in production methods have led to an increase in aquaculture production to the extent that the industry can now aid traditional fishing in meeting the growing global demand for fish within the context of the depletion of fisheries' resources. In this new context, market competition has increased and the complexity of managing industrial-scale production processes involving biological systems is still a growing problem. This has also led, in many cases, to a lack of management capacity that increases when it comes to setting long-term strategic plans. This study presents a methodology that aims to help aquaculture managers in decision making. It integrates a multi-criteria model and a Particle Swarm Optimisation (PSO) technique in order to provide a production strategy that optimises the value of multiple objectives at a fish farm with multiple cages, batches, feeding alternatives and products. This multi-criteria approach takes into account not only the effect of biological performance on economic profitability, but also the effect on environmental sustainability and aspects of product quality. In addition, it enables consideration of new operational and commercial constraints, such as the maximum volume of fish harvested per week, based on labour and marketing constraints, or the minimum necessary volume of fish harvested on specific dates to comply with commercial agreements. Results obtained demonstrate the utility of this novel approach to decision-making optimisation in aquaculture both when establishing overall strategic planning and when adopting new ways of producing.info:eu-repo/grantAgreement/EC/H2020/727315/EU/Mediterranean Aquaculture Integrated Development/MedAID

Novel Insulin Delivery Profiles for Mixed Meals in Basal-Bolus and Closed-Loop Artificial Pancreas Therapy for Type 1 Diabetes Mellitus

Traditional basal-bolus and closed-loop artificial pancreas therapy for type 1 diabetes mellitus were studied in the present work and novel insulin delivery profiles have been identified. Type 1 diabetes is a chronic condition resulting from autoimmune destruction of the pancreatic insulin producing β-cells. Inadequate insulin secretion prevents efficient glucose metabolism and is a serious health risk. Major available treatment modes are multiple daily injections of insulin and insulin pump therapy providing continuous subcutaneous infusion.General insulin regimens for low- and high-fat meals were studied in silico to improve current pump therapy for type 1 diabetes. This involved modifications of the FDA-accepted UVA/Padova metabolic simulation model for evaluations of meals with different absorption rates. Simulations of meals with varied fat content under this modified model demonstrated qualitative replications of published data. Subsequently, an insulin regimen library with optimized regimens under open- and closed-loop settings for a variety of meal compositions was constructed using the particle swarm optimization algorithm. Calculations show that the optimal open-loop insulin delivery profiles for low-fat meals comprise a normal bolus or short square wave depending on the size of the meal. The preferred delivery pattern for large meals is a short insulin wave due to the increased risk for hypoglycemia. Interestingly, the optimal open-loop regimens for high-fat meals are typically biphasic, but can extend to multiple phases for large slow absorbing meals. Furthermore, individual in silico optimizations revealed that patients with high insulin sensitivity could benefit from biphasic insulin deliveries when consuming high-fat meals. Preliminary investigations of the optimal closed-loop regimens under varied fat content also display bi- or triphasic patterns for high-fat meals and are primarily influenced by the carbohydrate content in the meal. The novel insulin delivery profiles identified in this work comprise new and unique waveforms that provide better control of postprandial glucose excursions than existing schemes. Furthermore, the novel regimens are also more or similarly robust to uncertainties in various parameter estimates with the closed-loop schemes displaying superior performance and robustness. The proposed closed-loop strategy does not rely on optimal basal therapy and is therefore a realistic approach that could have real-life applications in an artificial pancreas

Proceedings of 3. International Conference on Artificial Intelligence towards Industry 4.0 (ICAII4’2020)

Çevrimiçi ( XIV, 67 pages

Determination of feeding strategies in aquaculture farms using a multiple-criteria approach and genetic algorithms

Since the 1990s, fishing production has stagnated and aquaculture has experienced an exponential growth thanks to the production on an industrial scale. One of the major challenges facing aquaculture companies is the management of breeding activity affected by biological, technical, environmental and economic factors. In recent years, decision-making has also become increasingly complex due to the need for managers to consider aspects other than economic ones, such as product quality or environmental sustainability. In this context, there is an increasing need for expert systems applied to decision-making processes that maximize economic efficiency of the operational process. One of the production planning decisions more affected by these changes is the feeding strategy. The selection of the feed determines the growth of the fish, but also generates the greatest impact of the activity on the environment and determines the quality of the product. In addition, feed is the main production cost in finfish aquaculture. In order to address all these problems, the present work integrates a multiple-criteria methodology with a genetic algorithm that allows determining the best sequence of feeds to be used throughout the fattening period, depending on multiple optimization objectives. Results show its utility to generate and evaluate different alternatives and fulfill the initial hypothesis, demonstrating that the combination of several feeds at precise times may improve the results obtained by one feed strategies.This paper is part of the MedAID project which has received funding from the European Union's H2020 program under grant agreement 727315. The authors also wish to thank the Ibero-American Program for the Development of Science and Technology, CYTED, and the Red Iberoamericana BigDSSAgro (Ref. P515RT0123) for their support of this work

Integration of environmental sustainability and product quality criteria in the decision-making process for feeding strategies in seabream aquaculture companies

Economic criteria have traditionally been taken into account as the most important factor for the selection of the most suitable feed in aquaculture. However, currently, management decisions have become increasingly complex, taking into account issues such as environmental sustainability and product quality. In this regard, there is growing recognition that the quality of the environment in which an organization operates has a direct effect on its financial results. Unfortunately, the complex integration of all these factors, which are sometimes opposing, limits the ability of aquaculture producers to adapt their production strategy to cleaner production systems. In this context, the aim of this work is to address this problem with the development of a novel, multiple-criteria decision-making optimization methodology that allows producers to include different preferences in the design of feeding strategies. Here, this methodology is applied to gilthead seabream production. The results obtained show the utility of this methodology for integrating numerous criteria in the evaluation of various alternatives and for carrying out an efficient sensitivity analysis which test the impact of different hypotheses on stakeholders' preferences.This research was undertaken under the MedAID project, which has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no 727315 (http://www.medaid-h2020.eu/). The authors wish to thank the Ibero-American Program for the Development of Science and Technology (CYTED) and the Red Iberoamericana BigDSSAgro (Ref. P515RT0123) for their support of this work, and Juan B. Cabral for the package scikit-criteria for MCDM

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

Selection of Food Items for Diet Problem Using a Multi-objective Approach under Uncertainty

It is a problem that concerns us all: what should we eat on a day-to-day basis to meet our health goals? Scientists have been utilizing mathematical programming to answer this question. Through the use of operations research techniques, it is possible to find a list of foods that, in a certain quantity, can provide all nutrient recommendations in a day. In this research, a multi-objective programming model is provided to determine the selected food items for a diet problem. Two solution approaches are developed to solve this problem including weighted-sums and ε-constraint methods. Two sources of uncertainty have been considered in the model. To handle these sources, a scenario-based approach is utilized. The application of this model is shown using a case study in Canada. Using the proposed model and the solution approaches, the best food items can be selected and purchased to minimize the total cost and maximize health

Stand-alone solar-pv hydrogen energy systems incorporating reverse osmosis

The world’s increasing energy demand means the rate at which fossil fuels are consumed has increased resulting in greater carbon dioxide emissions. For many small (marginalised) or coastal communities, access to potable water is limited alongside good availability of renewable energy sources (solar or wind). One solution is to utilise small-scale renewably powered stand-alone energy systems to help supply power for everyday utilities and to operate desalination systems serving potable water (drinking) needs reducing diesel generator dependence. In such systems, on-site water production is essential so as to service electrolysis for hydrogen generation for Proton Exchange Membrane (PEM) fuel cells. Whilst small Reverse Osmosis (RO) units may function as a (useful) dump load, it also directly impacts the power management of stand-alone energy systems and affects operational characteristics. However, renewable energy sources are intermittent in nature, thus power generation from renewables may not be adequate to satisfy load demands. Therefore, energy storage and an effective Power Management Strategy (PMS) are vital to ensure system reliability. This thesis utilises a combination of experiments and modelling to analyse the performance of renewably powered stand-alone energy systems consisting of photovoltaic panels, PEM electrolysers, PEM fuel cells, batteries, metal hydrides and Reverse Osmosis (RO) under various scenarios. Laboratory experiments have been done to resolve time-resolved characteristics for these system components and ascertain their impact on system performance. However, the main objective of the study is to ascertain the differences between applying (simplistic) predictive/optimisation techniques compared to intelligent tools in renewable energy systems. This is achieved through applying intelligent tools such as Neural Networks and Particle Swarm Optimisation for different aspects that govern system design and operation as well as solar irradiance prediction. Results indicate the importance of device level transients, temporal resolution of available solar irradiance and type of external load profile (static or time-varying) as system performance is affected differently. In this regard, minute resolved simulations are utilised to account for all component transients including predicting the key input to the system, namely available solar resource which can be affected by various climatic conditions such as rainfall. System behaviour is (generally) more accurately predicted utilising Neural Network solar irradiance prediction compared to the ASHRAE clear sky model when benchmarked against measured irradiance data. Allowing Particle Swarm Optimisation (PSO) to further adjust specific control set-points within the systems PMS results in improvements in system operational characteristics compared to using simplistic rule-based design methods. In such systems, increasing energy storage capacities generally allow for more renewable energy penetration yet only affect the operational characteristics up to a threshold capacity. Additionally, simultaneously optimising system size and PMS to satisfy a multi-objective function, consisting of total Net Present Cost and CO2 emissions, yielded lower costs and carbon emissions compared to HOMER, a widely adopted sizing software tool. Further development of this thesis will allow further improvements in the development of renewably powered energy systems providing clean, reliable, cost-effective energy. All simulations are performed on a desktop PC having an Intel i3 processor using either MATLAB/Simulink or HOMER