ABSTRACT
Traditionally, the parameters used in heuristic optimization algorithms areconsidered constant during the evolutionary process. Although this characteristic simplifies the computational codes and despite the good quality of results presented in the literature, the use of constant parameters does not avoid the occurrence of premature convergence and other difficulties related to parameters sensitivity. In this context, this study aims at developing a self-adaptive heuristic algorithm based on rate of convergence and population diversity concepts, which are used by the Differential Evolution algorithm. The methodology proposed is applied to the minimization of mathematical functions and to the determination of a protocol for drug administration in patients with cancer, through the formulation and solution of a multi-objective optimal control problem. In the present study the minimization of the number of cancerous cells and the minimization of the concentration of drugs that are administered to the patient represent the considered objective functions. The Pareto's Curve provides a set of optimized protocols, among which an efficient solution for drug administration can be chosen through a given criterion, aiming at practical applications.
Keywords:
self-adaptive algorithm; differential evolution algorithm; tumor treatment; multi-objective optimal control problem
