Intelligent Local Search for an Optimal Control of Diabetic Population Dynamics

Diabetes is a chronic disease that affects millions of people in the world. This work has two objectives: (a) elaboration of a state of the art on the different dynamic systems with economic function proposed in the literature controlling diabetes in order to alleviate the socio-economic damage caused by it; (b) implementation of the best performing local search artificial intelligence methods (metaheuristics) to solve the kernel model dealing with the common compartments between all these models. The idea behind the use of metaheuristics is that these intelligent methods are able to provide an excellent approximation of a very economical control marked by its continuity and which does not completely exhaust the human and material resources programmed for the control of the studied phenomenon. Our work is marked by a richness of experiments allowing to analyze several interesting aspects of the phenomenon: quality of the control, cost of the control, and behavior of the different compartments in the presence of the control. In comparison with bees algorithm (BA), firefly algorithm (FA), particle swarm algorithm (PSO), genetic algorithm (GA), moth swarm algorithm (MSA), stochastic fractal search (SFS), wind driven optimization (WDO), and probabilistic bees algorithm (PBA). The stochastic fractal search (SFS) method has shown an unprecedented ability to produce continuous, economical controls capable of alleviating socio-economic damage against a reasonable budget.