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A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search

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Abstract

Incorporation of a decision maker’s preferences into multi-objective evolutionary algorithms has become a relevant trend during the last decade, and several preference-based evolutionary algorithms have been proposed in the literature. Our research is focused on improvement of a well-known preference-based evolutionary algorithm R-NSGA-II by incorporating a local search strategy based on a single agent stochastic approach. The proposed memetic algorithm has been experimentally evaluated by solving a set of well-known multi-objective optimization benchmark problems. It has been experimentally shown that incorporation of the local search strategy has a positive impact to the quality of the algorithm in the sense of the precision and distribution evenness of approximation.

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References

  • Auger A, Bader J, Brockhoff D, Zitzler E (2009) Articulating user preferences in many-objective problems by sampling the weighted hypervolume. In: Proceedings of the 11th annual conference on genetic and evolutionary computation, ACM, pp 555–562

  • Caponio A, Neri F (2009) Integrating cross-dominance adaptation in multi-objective memetic algorithms. Springer, Berlin

    Book  Google Scholar 

  • Deb K (1999) Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evolut Comput 7:205–230

    Article  Google Scholar 

  • Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Hoboken

    Google Scholar 

  • Deb K, Kumar A (2007) Light beam search based multi-objective optimization using evolutionary algorithms. In: 2007 IEEE congress on evolutionary computation (CEC), pp 2125–2132

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182–197

    Article  Google Scholar 

  • Deb K, Sundar J, Udaya Bhaskara Rao N, Chaudhuri S (2006) Reference point based multi-objective optimization using evolutionary algorithms. Int J Comput Intell Res 2(3):273–286

    Article  Google Scholar 

  • Deb K, Thiele L, Laumanns M, Zitzler E (2002) Scalable multi-objective optimization test problems. In: Proceedings of the world on congress on computational intelligence, pp 825–830

  • Filatovas E, Kurasova O, Sindhya K (2015) Synchronous R-NSGA-II: an extended preference-based evolutionary algorithm for multi-objective optimization. Informatica 26(1):33–50

    Article  Google Scholar 

  • Goel T, Deb K (2002) Hybrid methods for multi-objective evolutionary algorithms. In: Proceedings of the fourth Asia-Pacific conference on simulated evolution and learning (SEAL02), pp 188–192

  • Gong M, Liu F, Zhang W, Jiao L, Zhang Q (2011) Interactive MOEA/D for multi-objective decision making. In: Proceedings of the 13th annual conference on genetic and evolutionary computation, ACM, pp 721–728

  • Ishibuchi H, Murata T (1998) A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Trans Syst Man Cybern Part C Appl Rev 28(3):392–403

    Article  Google Scholar 

  • Jain H, Deb K (2014) An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, part II: handling constraints and extending to an adaptive approach. IEEE Trans Evolut Comput 18(4):602–622

    Article  Google Scholar 

  • Knowles JD, Corne DW (2000) Approximating the nondominated front using the Pareto archived evolution strategy. Evolut Comput 8(2):149–172

    Article  Google Scholar 

  • Lančinskas A, Ortigosa PM, Žilinskas J (2013) Multi-objective single agent stochastic search in non-dominated sorting genetic algorithm. Nonlinear Anal Model Control 18(3):293–313

  • Lančinskas A, Žilinskas J, Ortigosa PM (2011) Local optimization in global multi-objective optimization algorithms. In: 2011 third world congress on nature and biologically inspired computing (NaBIC), pp 323–328

  • López-Jaimes A, Coello Coello CA (2014) Including preferences into a multiobjective evolutionary algorithm to deal with many-objective engineering optimization problems. Inf Sci 277:1–20

    Article  Google Scholar 

  • Mejía JAH, Schütze O, Deb K (2014) A memetic variant of R-NSGA-II for reference point problems. In: EVOLVE-A bridge between probability, set oriented numerics, and evolutionary computation V, Springer, pp 247–260

  • Miettinen K (1999) Nonlinear multiobjective optimization. Springer, Berlin

    Google Scholar 

  • Mohammadpour A, Dehghani A, Byagowi Z (2013) Using R-NSGA-II in the transmission expansion planning problem for a deregulated power system with wind farms. Int J Eng Pract Res 2(4):201–204

    Google Scholar 

  • Murata T, Nozawa H, Tsujimura Y, Gen M, Ishibuchi H (2002) Effect of local search on the performance of cellular multiobjective genetic algorithms for designing fuzzy rule-based classification systems. In: Proceedings of the world on congress on computational intelligence, vol 1, IEEE, pp 663–668

  • Purshouse RC, Deb K, Mansor MM, Mostaghim S, Wang R (2014) A review of hybrid evolutionary multiple criteria decision making methods. In: 2014 IEEE congress on evolutionary computation (CEC), pp 1147–1154

  • Ray T, Asafuddoula M, Isaacs A (2013) A steady state decomposition based quantum genetic algorithm for many objective optimization. In: 2013 IEEE congress on evolutionary computation (CEC), pp 2817–2824

  • Ruiz AB, Saborido R, Luque M (2015) A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm. J Glob Optim 62(1):101–129

  • Siegmund F, Bernedixen J, Pehrsson L, Ng AH, Deb K (2012) Reference point-based evolutionary multi-objective optimization for industrial systems simulation. In: Proceedings of the winter simulation conference

  • Siegmund F, Ng AH, Deb K (2012) Finding a preferred diverse set of Pareto-optimal solutions for a limited number of function calls. In: 2012 IEEE congress on evolutionary computation (CEC), pp 1–8

  • Sindhya K, Miettinen K, Deb K (2013) A hybrid framework for evolutionary multi-objective optimization. IEEE Trans Evolut Comput 17(4):495–511

    Article  Google Scholar 

  • Sindhya K, Ruiz AB, Miettinen K (2011) A preference based interactive evolutionary algorithm for multi-objective optimization: PIE. In: 6th international conference on evolutionary multi-criterion optimization, EMO 2011, Springer, pp 212–225

  • Sindhya K, Sinha A, Deb K, Miettinen K (2009) Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems. In: 2009 IEEE congress on evolutionary computation (CEC), IEEE, pp 2919–2926

  • Solis F, Wets B (1981) Minimization by random search techniques. Math Oper Res 6(1):19–30

    Article  Google Scholar 

  • Talbi EG (2009) Metaheuristics: from design to implementation, vol 74. Wiley, Hoboken

    Book  Google Scholar 

  • Thiele L, Miettinen K, Korhonen PJ, Molina J (2009) A preference-based evolutionary algorithm for multi-objective optimization. Evolut Comput 17(3):411–436

    Article  Google Scholar 

  • Zavala GR, Nebro AJ, Luna F, Coello CAC (2014) A survey of multi-objective metaheuristics applied to structural optimization. Struct Multidiscip Optim 49(4):537–558

    Article  Google Scholar 

  • Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evolut Comput 11(6):712–731

    Article  Google Scholar 

  • Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evolut Comput 1(1):32–49

    Article  Google Scholar 

  • Zitzler E, Künzli S (2004) Indicator-based selection in multiobjective search. In: Parallel problem solving from nature-PPSN VIII, Springer, pp 832–842

  • Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength Pareto evolutionary algorithm. Tech rep

  • Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Parallel problem solving from nature-PPSN V, Springer, pp 292–301

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Acknowledgments

This research is funded by a Grant (No. MIP-051/2014) from the Research Council of Lithuania.

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Authors and Affiliations

  1. Institute of Mathematics and Informatics, Vilnius University, Akademijos str. 4, 08663, Vilnius, Lithuania

    Ernestas Filatovas, Algirdas Lančinskas, Olga Kurasova & Julius Žilinskas

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  1. Ernestas Filatovas
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  2. Algirdas Lančinskas
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  3. Olga Kurasova
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  4. Julius Žilinskas
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Correspondence to Ernestas Filatovas.

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Filatovas, E., Lančinskas, A., Kurasova, O. et al. A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search. Cent Eur J Oper Res 25, 859–878 (2017). https://doi.org/10.1007/s10100-016-0443-x

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  • DOI: https://doi.org/10.1007/s10100-016-0443-x

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