Multiobjective Optimization to Optimal Moroccan Diet Using Genetic Algorithm

Proper glucose control is designed to prevent or delay the complications of diabetes. Various contexts can lead to a fluctuation of the blood sugar level to a greater or lesser extent. It can be, for example, eating habits, treatment, intense physical activity, etc. The feeding problem interpolated by a minimum cost function is well-known in the literature. The main goal of this paper is to introduce a multiobjective programming model with constraints for the diet problem with two objective functions, the first of which is the total glycemic load of the diet while the second objective function is the cost of the diet. the MOGA (multiobjective Genetic Algorithm) algorithm was used to resolve the proposed model. The experimental results show that our system ([proposed model – MOGA]) is able to produce adequate diets that can settle glycemic load and cost while respecting the patient\u27s requirements

Intelligent Local Search Optimization Methods to Optimal Morocco Regime

In this paper, we compare three well-known swarm algorithms on optimal regime based on our mathematical optimization model introduced recently. Different parameters of this latter are estimated based on 176 foods and on who’s the nutrients values are calculated for 100 g. The daily nutrients needs are estimated based on the expert’s knowledge. Different experimentations are realized for different configurations of the considered swarm algorithms. Compared to Stochastic Fractal Search (SFS) and Particle Swarm Optimization Algorithm (PSO), the Firefly Algorithm (FA) produces the main suitable regimes

Multi-objectives optimization and convolution fuzzy C-means: control of diabetic population dynamic

The optimal control models proposed in the literature to control a population of diabetics are all single-objective which limits the identification of alternatives and potential opportunities for different reasons: the minimization of the total does not necessarily imply the minimization of different terms and two patients from two different compartments may not support the same intensity of exercise or the same severity of regime. In this work, we propose a multi-objectives optimal control model to control a population of diabetics taking into account the specificity of each compartment such that each objective function involves a single compartment and a single control. In addition, the Pontryagin’s maximum principle results in expansive control that devours all resources because of max-min operators and the control formula is very complex and difficult to assimilate by the diabetologists. In our case, we use a multi-objectives heuristic method, NSGA-II, to estimate the optimal control based on our model. Since the objective functions are conflicting, we obtain the Pareto optimal front formed by the non-dominated solutions and we use fuzzy C-means to determine the important main strategies based on a typical characterization. To limit human intervention, during the control period, we use the convolution operator to reduce hyper-fluctuations using kernels with different size. Several experiments were conducted and the proposed system highlights four feasible control strategies capable of mitigating socio-economic damages for a reasonable budget