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Memetic Algorithm for Solving the Problem of Social Portfolio Using Outranking Model

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Recent Advances on Hybrid Intelligent Systems

Abstract

The government institutions at all levels, foundations with private funds or private companies that support social projects receiving public funds or budget to develop its own social projects often have to select the projects to support and allocate budget to each project. The choice is difficult when the available budget is insufficient to fund all projects or proposals whose budget requests have been received, together with the above it is expected that approved projects have a significant social impact. This problem is known as the portfolio selection problem of social projects. An important factor involved in the decision to make the best portfolio, is that the objectives set out projects that are generally intangible, such as the social, scientific and human resources training. Taking into account the above factors in this paper examines the use of multi objective methods leading to a ranking of quality of all selected projects and allocates resources according to priority ranking projects until the budget is exhausted. To verify the feasibility of ranking method for the solution of problem social portfolio constructed a population memetic evolutionary algorithm, which uses local search strategies and cross adapted to the characteristic of the problem. The experimental results show that the proposed algorithm has a competitive performance compared to similar algorithms reported in the literature and on the outranking model is a feasible option to recommend a portfolio optimum, when little information and the number of projects is between 20 and 70.

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References

  1. Punkka, A., Salo, A.: Rank-Based Sensitivity Analysis of Multiattribute Value Models. Abstract to INFORMS Annual Meeting 2008, Washington, DC, USA (2008)

    Google Scholar 

  2. Carazo, A.F., Gómez, T., Molina, J., Hernández-Díaz, A.G., Guerreo, F.M., Caballero, R.: Solving a comprehensive model for multiobjective project portfolio selection. Computers & Operations Research 37(4), 630–639 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  3. Castro, M.: Development and implementation of a framework for I&D in public organizations. Master´s thesis. Universidad Autónoma de Nuevo León (2007)

    Google Scholar 

  4. Coello Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Genetic and Evolutionary Computation. Springer (2007)

    Google Scholar 

  5. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester-New York-Weinheim-Brisbane-Singapore-Toronto (2001)

    Google Scholar 

  6. Durillo, J.J., Nebro, A.J., Coello Coello, C.A., García-Nieto, J., Luna, F., Alba, E.: A study of multi-objective metaheuristics when solving parameter scalable problems. IEEE Transactions on Evolutionary Computation 14(4), 618–635 (2010)

    Article  Google Scholar 

  7. Fernández, E., Navarro, J.: A genetic search for exploiting a fuzzy preference model of portfolio problems with public projects. Annals OR 117, 191–213 (2002)

    Article  MATH  Google Scholar 

  8. Fernández, E., López, E., Bernal, S., Coello Coello, C.A., Navarro, J.: Evolutionary multiobjective optimization using an outranking-based dominance generalization. Computers & Operations Research 37(2), 390–395 (2010a)

    Article  MATH  Google Scholar 

  9. Fernández, E., López, E., López, F., Coello Coello, C.A.: Increasing selective pressure towards the best compromise in evolutionary multiobjective optimization: The extended NOSGA method. Information Sciences 181(1), 44–56 (2010b)

    Article  Google Scholar 

  10. Fernández, E., Félix, L.F., Mazcorro, G.: Multi-objective optimization of an outranking model for public resources allocation on competing projects. Int. J. Operational Research 5(2), 190–210 (2009)

    Article  MATH  Google Scholar 

  11. García, R.: Hyper-Heuristic for solving social portfolio problem. Master´s Thesis, Instituto Tecnológico de Cd. Madero (2010)

    Google Scholar 

  12. Ghasemzadeh, F., Archer, N., Iyogun, P.: A zero-one model for project portfolio selection and scheduling. Journal of the Operational Research Society 50(7), 745–755 (1999)

    MATH  Google Scholar 

  13. Greco, S., Mousseau, V., Słowinski, R.: Ordinal regression revisited: multiple criteria ranking using a set of additive value functions. European Journal of Operational Research (2007) doi:10.1016/ j.ejor

    Google Scholar 

  14. Marakas, G.M.: Decision Support Systems in the 21th Century. Prentice Hall, New Jersey (1999)

    Google Scholar 

  15. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springuer Series Artificial Intelligence, p. 390. Springer, New York (1996)

    MATH  Google Scholar 

  16. Olmedo Pérez, R.A.: Avances en la modelación basada en Relaciones borrosas de Sobre-clasificación para sistemas de Apoyo a la decisión en grupo. Tesis para obtener el grado de Doctor en Ciencias de la Computación (2009)

    Google Scholar 

  17. Osyczka, A.: Multicriteria optimization for engineering design. En Design Optimization, pp. 155–183. Academic Press (1985)

    Google Scholar 

  18. Peñuela, C., Granada, M.: Optimización multiobjetivo usando un algoritmo genético y un operador elitista basado en un ordenamiento no dominado (NSGA-II). Scientia Et Technica 8(35), 175–180 (2007)

    Google Scholar 

  19. Reiter, P.: Metaheuristic Algorithms for Solving Multi-objective/Stochastic Scheduling and Routing Problems. Tesis Doctoral, University of Wien (2010)

    Google Scholar 

  20. Rivera Zárate, G.: Tesis de Doctorado “Solución a Gran Escala del Problema de Cartera de Proyectos Sociales”. Instituto Tecnológico de Ciudad Madero (2011)

    Google Scholar 

  21. Roy, B.: The Outranking Approach and the Foundations of ELECTRE methods. In: Reading in Multiple Criteria Decision Aid, pp. 155–183. Spinger (1990)

    Google Scholar 

  22. Roy, B., Slowinski, R.: Handling effects of reinforced preference and counter-veto in credibility of outranking. European Journal of Operational Research 188(1), 185–190 (2008)

    Article  MATH  Google Scholar 

  23. Wang, Y., Yang, Y.: Particle swarm optimization with preference order ranking for multi-objective optimization. Information Sciences 179(12), 1944–1959 (2009)

    Article  MathSciNet  Google Scholar 

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Correspondence to Claudia G. Gómez S. .

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Gómez S., C.G., Fernández Gonzalez, E.R., Cruz Reyes, L., Bastiani M., S.S., Rivera Z., G., Ruız M., V. (2013). Memetic Algorithm for Solving the Problem of Social Portfolio Using Outranking Model. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Recent Advances on Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33021-6_27

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  • DOI: https://doi.org/10.1007/978-3-642-33021-6_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33020-9

  • Online ISBN: 978-3-642-33021-6

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