Proposed Working Title: Population-based Techniques for Multi-objective Optimization

The use of population-based techniques to solve optimization problems is continuously increasing, as these kind of problems appears in a number of situations in real life. Many of these problems are of a combinatorial nature, this means that we have to find the ideal permutation of the parameters involved to reach the optimal solution. As the number of parameters increases, the difficulty to find the optimal solution becomes harder, and if we are conscious that a large amount of problems have more than one objective to optimize, this task grows to be too much harder. Population-based techniques, as any other heuristic, do not guarantee to obtain the optimal solutions, but good approximations, as they have the ability to find not only one, but a set of solutions in a single run. This is the reason why these methods have turned out to be a very successful and popular tool nowadays. This is also the cause why we have considered them for solving the combinatorial problem called Vehicle Routing Problem, which has many applications in real world problems and has several variants that lack of investigation. In this document we are proposing the design of at least two algorithms based in such techniques to solve a couple of those variants, considering some topics that current publications have excluded and we believe they are relevant.