In this paper, a good portfolio is found through an ant colony algorithm (including a local search) that approximates the Pareto front regarding some kind of project categorization, cardinalities, discrepancies with priorities given by the ranking, and the average rank of supported projects; this approach is an improvement towards a proper modeling of preferences. The available information is only projects' ranking and costs, and usually, resource allocation follows the ranking priorities until they are depleted. Results show that our proposal outperforms previous approaches.

Characterizing the preferences of a decision maker in a multicriteria decision is a complex task that becomes even harder if the information available is limited. This paper addresses a particular case of project portfolio selection; in this case, the measures of project impacts are not assumed, and the available information is only projects' ranking and costs. Usually, resource allocation follows the ranking priorities until they are depleted. This action leads to a feasible solution, but not necessarily to a good portfolio. In this paper, a good portfolio is found by solving a multiobjective problem. To effectively address such dimensionality, the decision maker's preferences in the form of a fuzzy relational system are incorporated in an ant-colony algorithm. The Region of Interest is approached by solving a surrogate triobjective problem. The results show that the reduction of the dimensionality supports the decision maker in choosing the best portfolio.

Selecting project portfolios Decision-Maker usually starts with limited information about projects and portfolios. One of the challenges involved in analyzing, searching and selecting the best portfolio is having a method to evaluate the impact of every project and portfolio in order to compare them.This paper develops a model for composing publicoriented project portfolios. Information concerning the quality of the projects is in the form of a project-ranking, which can be obtained by the application of a proper multi-criteria method; however the ranking does not assume an appropriate evaluation. A best portfolio is primarily found through a multi-objective optimization that regards the impact indicators that reflect the quality of the projects in the portfolio and competent portfolios' cardinalities. Overall good solutions are obtained by developing an evolutionary method, which is found to perform well in some test examples.

One of the most important problems faced by any organization is make decisions about how to invest and manage the resources to get more benefits; however, the organizations resources are not enough to support all portfolios proposals. To these problems that face the executives of the big organizations, is known as Select Portfolio Problem. In this work is developed an ant colony algorithm, which is an especially effective meta-heuristic, this meta-heuristic is hybridized with a multi-objective local search, this strategy allows using knowledge of the ant, to build potential solutions, knowledge is obtained through the pheromone trail left by ants when find good solutions, for that the algorithm does not converge prematurely an evaporation strategy is implemented. The strategy metaheuristic include an optimization model for portfolio selection called discrepancies model, this model is implemented when the information concerned to the quality of the projects is in form of ranking, besides help to evaluate portfolios through ten criteria to maximize the impact of the portfolio. This approach allowed reaching privileged areas of Pareto's front, where identified solutions that reflect the