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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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
Deb K (1999) Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evolut Comput 7:205–230
Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Hoboken
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
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
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
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
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
Knowles JD, Corne DW (2000) Approximating the nondominated front using the Pareto archived evolution strategy. Evolut Comput 8(2):149–172
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
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
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
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
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
Talbi EG (2009) Metaheuristics: from design to implementation, vol 74. Wiley, Hoboken
Thiele L, Miettinen K, Korhonen PJ, Molina J (2009) A preference-based evolutionary algorithm for multi-objective optimization. Evolut Comput 17(3):411–436
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
Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evolut Comput 11(6):712–731
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
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
This research is funded by a Grant (No. MIP-051/2014) from the Research Council of Lithuania.
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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
- Multi-objective optimization
- Preference-based evolutionary algorithms
- Memetic algorithm
- Stochastic local search