Algoritmo evolutivo para o problema do caixeiro viajante com demandas heterogêneas

Abstract

The work proposed in this dissertation is the field of combinatorial optimization, which aims to find a solution to these types of problems at a low computational time and effectively. The combinatorial optimization studies a set of discrete solutions, which have a finite number of elements, to find the best viable solution to the problems of this magnitude. One of the main approaches that area is the Traveling Salesman Problem (TSP), mainly due to the size of possible solutions to the problem, so that is intractable computation by exhaustive search methods. Given all these features, this work is to study and develop evolutionary strategies for the resolution of the Problem of Traveling Salesman with Heterogeneous Demands (TSPHD), a variation of the classic TSP. The evolutionary strategies belong to the class of evolutionary computation, and methods of search based on the theory of the evolution of species, where the best individuals compete for survival. The evolutionary strategies differ from other optimization techniques, as the search is conducted in a population of solutions, not a single point. To solve the problem are proposed four evolutionary algorithms, using heuristics techniques and metaheurísticas for its implementation. The results were obtained from tests using instances of low density (low connection), and compared with the exact solution (optimal solution) and other progressive methods in the literature. These results are evaluated on the basis of their quality and time for its implementation.