The Traveling Salesman Problem with Draft Limits is a combinatorial optimization problem that consists in calculating routes to be taken by cargo ships without violating draft limits restrictions, so reducing transportation costs. Finding the best route solution, using exact computation, is a problem whose complexity grows exponentially with the number of routes and, therefore, is unfeasible for practical cases. Approximations to the best solution, computed using heuristis and metaheuristics, appear as promising and feasible alternatives to address this problem with reasonable accuracy. This paper exploits two metaheuristics, Genetic and Memetic Algorithms, under the perspective of Evolutionary Algorithms, to address the problem at hand. After they are implemented and applied over a route planning map, their effectiveness are compared against each other and also against the literature. Results suggest that the method based on Memetic Algorithm is slightly better (5.28% average error) in comparison with the Genetic-based approach (12.96%), which is shown to be competitive with respect to the literature.

The complexity and ubiquity achieved by software in the present world makes it imperative, more than ever, the availability of both technologies and sound methods to drive its development. Programming 'in--the--large', component--based programming and software architecture are popular expressions which embody this concern and correspond to driving forces in current software engineering. In such a context, this paper reports on the research, which constitutes the PhD project of the author, on a formal calculus for reasoning about and transforming component--based architectures.