Solution to the Social Portfolio Problem by Evolutionary Algorithms

The formation of project portfolio is a multi-objective problem that has a high impact on public and private organizations, and has generally been addressed by evolutionary algorithms. They often seek an approximation of the Pareto front, and then the decision maker must choose an only solution from the set. This is not a difficult task when you have to select a solution from a small set evaluated in two or three objectives. But when the set of solutions grows, or the number of objectives increases, the choice is often a complicated process. It is necessary to present the decision maker only the subset of the Pareto front according to your preferences. This paper describes an optimization algorithm that steers the search process towards such solutions. The performance of the algorithm is evaluated with respect to the most related algorithm found in the state of the art.

[1]  Eduardo Fernández,et al.  A Genetic Search for Exploiting a Fuzzy Preference Model of Portfolio Problems with Public Projects , 2002, Ann. Oper. Res..

[2]  Carlos A. Coello Coello,et al.  A Study of Multiobjective Metaheuristics When Solving Parameter Scalable Problems , 2010, IEEE Transactions on Evolutionary Computation.

[3]  Mauricio Granada Echeverri,et al.  Optimización multiobjetivo usando un algoritmo genético y un operador elitista basado en un ordenamiento no-dominado (NSGA-II) , 2007 .

[4]  Yujia Wang,et al.  Particle swarm optimization with preference order ranking for multi-objective optimization , 2009, Inf. Sci..

[5]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) , 2006 .

[6]  Eric Soubeiga,et al.  Development and application of hyperheuristics to personnel scheduling , 2003 .

[7]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[8]  Fereidoun Ghasemzadeh,et al.  A zero-one model for project portfolio selection and scheduling , 1999, J. Oper. Res. Soc..

[9]  Rafael Caballero,et al.  Solving a comprehensive model for multiobjective project portfolio selection , 2010, Comput. Oper. Res..

[10]  Andrzej Osyczka,et al.  7 – Multicriteria optimization for engineering design , 1985 .

[11]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[12]  Eduardo Fernández,et al.  Increasing selective pressure towards the best compromise in evolutionary multiobjective optimization: The extended NOSGA method , 2011, Inf. Sci..

[13]  Carlos A. Coello Coello,et al.  Evolutionary multiobjective optimization , 2011, WIREs Data Mining Knowl. Discov..

[14]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[15]  Graham Kendall,et al.  A Classification of Hyper-heuristic Approaches , 2010 .

[16]  Richard F. Hartl,et al.  Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection , 2004, Ann. Oper. Res..

[17]  Peter Manfred Siegfried Reiter Matheuristic algorithms for solving multi-objective/stochastic scheduling and routing problems , 2010 .

[18]  B. Roy THE OUTRANKING APPROACH AND THE FOUNDATIONS OF ELECTRE METHODS , 1991 .

[19]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[20]  Bernard Roy,et al.  Handling effects of reinforced preference and counter-veto in credibility of outranking , 2008, Eur. J. Oper. Res..