Abstract
Bilevel optimization has been recognized as one of the most difficult and challenging tasks to deal with because a solution to the upper level problem may be feasible only if it is also an optimal solution to the lower level problem. In recent years, evolutionary bilevel optimization has attracted increasing interest. In this paper, an efficient self-adaptive bilevel differential evolution (SABiLDE) with k-nearest neighbors (kNN) based interpolation is proposed to solve bilevel optimization problems. The kNN approximation is applied to estimate the optimal lower level variables for any newly generated upper candidates to improve the computational efficiency. A similarity based self-adaptive strategy for the dynamic control of lower level population size and search radius is introduced to further enhance the efficiency of the lower level function evaluations. A test set with 10 standard test problems and the SMD suite with controllable complexities are used to evaluate the performance of the proposed approach. Compared with four recent state-of-the-art methods, the numerical results produced by the proposed method are promising and show great potential for solving generic bilevel optimization problems.
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References
Benth FE, Dahl G, Mannino C (2012) Computing optimal recovery policies for financial markets. Oper Res 60(6):1373–1388
Chiou SW (2009) A bi-level programming for logistics network design with system-optimized flows. Inf Sci 179:2434–2441
Zhang G, Gao Y, Lu J (2011) Competitive strategic bidding optimization in electricity markets using bilevel programming and swarm technique. IEEE Trans Ind Electron 58:2138–2146
Calvete HI, Galé C, Oliveros MJ (2011) Bilevel model for production distribution planning solved by using ant colony optimization. Comput Oper Res 38:320–327
Kuo RJ, Han YS (2011) A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem – a case study on supply chain model. Appl Math Model 35:3905–3917
Koh A (2007) Solving transportation bi-level programs with differential evolution. In IEEE Congress on Evolutionary Computation. IEEE, pp. 2243–2250
Sinha A, Malo P, and Deb K (2015) Transportation policy formulation as a multi-objective bilevel optimization problem. In 2015 IEEE Congress on Evolutionary Computation (CEC-2015)
Wein L (2009) Homeland security: from mathematical models to policy implementation: the 2008 Philip McCord Morse lecture. Oper Res 57(4):801–811
Shabde VS, Hoo KA (2008) Optimum controller design for a spray drying process. Control Eng Pract 16:541–552
Lu J, Han J, Hu Y, Zhang G (2016) Multilevel decision-making: A survey. Inf Sci 346–347:463–487
Sinha A, Malo P, and Deb K (2013) Efficient evolutionary algorithm for single-objective bilevel optimization. CoRR, abs/1303.3901
Sinha A, Malo P, Deb K, Korhonen P, Wallenius J (2016) Solving bilevel multi-criterion optimization problems with lower level decision uncertainty. IEEE Trans Evol Comput 20(2):199–217
Hansen P, Jaumard B, Savard G (1992) New branch-and-bound rules for linear bilevel programming. SIAM J Sci and Statis Comput 13(5):1194–1217
Sinha A, Malo P, and Deb K (2014) An improved bilevel evolutionary algorithm based on quadratic approximations. In 2014 IEEE Congress on Evolutionary Computation (CEC-2014). IEEE, pp. 1870–1877
Colson B, Marcotte P, Savard G (2007) An overview of bilevel optimization. Ann Oper Res 153:235–256
Storn R, Price K (1977) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359
Angelo J S, Krempser E, Barbosa H J C (2014) Differential evolution assisted by a surrogate model for bilevel programming problems. In 2014 IEEE Congress on Evolutionary Computation (CEC-2014). pp. 1784–1791
Sinha A, Malo P, and Deb K (2012) Unconstrained scalable test problems for single-objective bilevel optimization. In 2012 IEEE World Congress on Computational Intelligence, 2012
Sinha A, Malo P, Deb K (2014) Test problem construction for single-objective bilevel optimization. Evol Comput 22(3):439–477
Deb K, Sinha A (2010) An efficient and accurate solution methodology for bilevel multi-objective programming problems using a hybrid evolutionary-local-search algorithm. Evol Comput 18(3):403–449
Sinha A, Malo P, and Deb K (2015) Towards understanding bilevel multi-objective optimization with deterministic lower level decisions. In Proceedings of the Eighth International Conference on Evolutionary Multi-Criterion Optimization (EMO-2015). Springer-Verlag, 2015.
Hejazi S, Memariani A, Jahanshahloo G, Sepehri M (2002) Linear bilevel programming solution by genetic algorithm. Comput Oper Res 29(13):1913–1925
Wan Z, Wang G, Sun B (2013) A hybrid intelligent algorithm by combining particle swarm optimization with chaos searching technique for solving nonlinear bilevel programming problems. Swarm and Evol Comput 8:26–32
Wan Z, Mao L, Wang G (2014) Estimation of distribution algorithm for a class of nonlinear bilevel programming problems. Inf Sci 256:184–196
Wang Y, Jiao YC, Li H (2005) An evolutionary algorithm for solving nonlinear bilevel programming based on a new constraint-handling scheme. IEEE Trans Sys Man and Cyber Part C: Appl and Reviews 35(2):221–232
Jiang Y, Li X, Huang C, Wu X (2013) Application of particle swarm optimization based on CHKS smoothing function for solving nonlinear bilevel programming problem. Appl Math Comput 219:4332–4339
Li H (2015) A genetic algorithm using a finite search space for solving nonlinear/linear fractional bilevel programming problems. Ann Oper Res 235:543–558
Mathieu R, Pittard L, Anandalingam G (1994) Genetic algorithm based approach to bi-level linear programming. Oper Res 28(1):1–21
Yin Y (2000) Genetic algorithm based approach for bilevel programming models. J Transport Eng 126(2):115–120
Zhu X, Yu Q, and Wang X (2006) A hybrid differential evolution algorithm for solving nonlinear bilevel programming with linear constraints. In the 5th IEEE International Conference on Cognitive Informatics. IEEE, pp. 126–131
Koh A (2007) Solving transportation bi-level programs with differential evolution. In IEEE Congress on Evol Comput. IEEE, pp. 2243–2250
Islam M M, Singh H K and Ray T (2015) A memetic algorithm for solving single objective bilevel optimization problems. In 2015 IEEE Congress on Evolutionary Computation (CEC-2015). IEEE, pp. 1643–1650
Gao Y, Zhang G, Lu J, Wee HM (2011) Particle swarm optimization for bi-level pricing problems in supply chains. J Global Optim 51:245–254
Zhao L, Wei JX (2019) A nested particle swarm algorithm based on sphere mutation to solve bi-level optimization. Soft Comput 23:11331–11341
Sinha A, Malo P, Frantsev A, Deb K (2014) Finding optimal strategies in a multi-period multi-leader-follower stackelberg game using an evolutionary algorithm. Comput Oper Res 41:374–385
Angelo J S, Krempser E, Barbosa H J C (2013) Differential evolution for bilevel programming. In 2013 IEEE Congress on Evolutionary Computation (CEC-2013). IEEE, pp. 470–477
He X, Zhou Y, Chen Z (2018) Evolutionary bilevel optimization based on covariance matrix adaptation. IEEE Trans Evol Comput 23(2):258–272
Huang PQ, Wang Y (2020) A framework for scalable bilevel optimization: identifying and utilizing the interactions between upper-level and lower-level variables. IEEE Trans Evol Comput 24(6):1150–1163
Oduguwa V and Roy R (2002) Bi-level optimization using genetic algorithm. In Proceedings of the 2002 IEEE International Conference on Artificial Intelligence Systems. IEEE, pp.123–128
Legillon F, Liefooghe A, and Talbi E G (2012) Cobra: a cooperative coevolutionary algorithm for bi-level optimization. In 2012 IEEE Congress on Evolutionary Computation (CEC-2012). IEEE, 2012
Chaabani A, Bechikh S, Said L B (2015) A co-evolutionary decomposition-based algorithm for bi-level combinatorial optimization. In IEEE Congress on Evolutionary Computation. IEEE, pp. 1659–1666
Chaabani A, Bechikh S, Said LB (2018) A co-evolutionary hybrid decomposition-based algorithm for bi-level combinatorial optimization problems. Appl Intelligence 48:2847–2872
Li H, Fang L (2014) Co-evolutionary algorithm: an efficient approach for bilevel programming problem. Eng Optim 46(3):361–374
Said R, Elarbi M, Bechikh S, Said LB (2021) Solving combinatorial bi-level optimization problems using multiple populations and migration schemes. Oper Res. https://doi.org/10.1007/s12351-020-00616-z
Sinha A, Lu Z, Deb K, Malo P (2020) Bilevel optimization based on iterative approximation of multiple mappings. J Heuristics 26:151–185
Islam M, Singh HK, Ray T (2017) A surrogate assisted approach for single-objective bilevel optimization. IEEE Trans Evol Comput 21(5):681–696
Singh HK, Islam M, Ray T, Ryan MJ (2019) Nested evolutionary algorithms for computationally expensive bilevel optimization problems: Variants and their systematic analysis. Swarm Evol Comput 48:329–344
Shepard D (1968) A two-dimensional interpolation function for irregularly-spaced data. In Proc. of the 23rd ACM National Conference. ACM, pp. 517–524
Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31
Qin AK, Huang VL, Sugannthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417
Mezura-Montes E, Velázquez-Reyes J, and Coello Coello C A (2006) A comparative study of differential evolution variants for global optimization. In Proc. Genet. Evol. Comput. Conf. pp. 485–492
Derrac J, GarcíaS MD (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1(1):3–18
Alcalá-Fdez J, Sánchez L, García S et al (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318
Acknowledgements
This work was partly supported by the National Natural Science Foundation of P. R. China (Grant no. 61203309, 61773390), Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/I011056/1 and Platform Grant EP/H00453X/1, and Natural and Environment Research Council (NERC) under the grant NE-V002511, National Defense Basic Research Program of China (Grant no. JCKY2019403D006), Hunan Provincial Natural Science Foundation of China (Grant No. 2018JJ2137, 2018RS3081), Hunan Provincial Science and Technology Plan of China (Grant no. 2017XK2302) and Hunan Provincial Innovation Foundation for Postgraduate (CX20190807).
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Wu, L., Liu, Z., Wei, HL. et al. An efficient bilevel differential evolution algorithm with adaptation of lower level population size and search radius. Memetic Comp. 13, 227–247 (2021). https://doi.org/10.1007/s12293-021-00335-8
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DOI: https://doi.org/10.1007/s12293-021-00335-8