Abstract
The Linear Ordering Problem (LOP) is a very popular NP-hard combinatorial optimization problem with many practical applications that may require the use of large instances. The Linear Ordering Library (LOLIB) gathers a set of standard benchmarks that are widely used in validating solvers for the LOP. Among them, xLOLIB2 collects some of the largest and most challenging instances in the current literature. In this work, we present new best-known solutions for each of the 200 complex instances that comprise xLOLIB2 and for the other 93 instances of the benchmarks with smaller sizes. This important advance in the field of LOP has been made possible due to the development of a novel memetic algorithm (MA) that was designed by taking into account some of the weaknesses of state-of-the-art LOP solvers. In particular, one of the keys to success is that the novel proposal allows for a gradual shift from exploration to exploitation. This is done using the novel Best Non-Penalized (BNP) replacement strategy. BNP selects the survivors by taking into account the quality, the Spearman’s footrule distance, the stopping criterion, and the elapsed period of execution simultaneously. The novel diversity-aware proposal is called the memetic algorithm with explicit diversity management (MA-EDM) and extensive comparisons against state-of-the-art techniques provide insights into the reasons for the superiority of MA-EDM.
Similar content being viewed by others
Notes
https://github.com/carlossegurag/LOP_MA-EDM. In addition to the source code, the datasets generated and/or analyzed during the current study are available in this repository in the RawData directory. Moreover, this repository contains the best-known solutions generated for each instance in the BKS.zip file.
References
Leontief WW (1936) Quantitative input and output relations in the economic systems of the United States. Rev Econ Stat 18(3):105–125
Chenery HB, Watanabe T (1958) International comparisons of the structure of production. Econometrica 26(4):487–521
Garey MR, Johnson DS (1979) Computers and Intractability: a guide to the theory of NP-completeness. W. H. Freeman & Co., Philadelphia, USA
Martí R, Reinelt G, Duarte A (2012) A benchmark library and a comparison of heuristic methods for the linear ordering problem. Comput Optim Appl 51:1297–1317
Martí R, Reinelt G (2011) The linear ordering problem - exact and heuristic methods in combinatorial optimization, p. 169. Springer, Springer Heidelberg Dordrecht London New York
Ceberio J, Mendiburu A, Lozano JA (2015) The linear ordering problem revisited. Eur J Oper Res 241(3):686–696
Črepinšek M, Liu S-H, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45(3):35–13533
Segura C, Hernández-Aguirre A, Luna F, Alba E (2016) Improving diversity in evolutionary algorithms: new best solutions for frequency assignment. IEEE Trans Evol Comput 21(4):539–553
Schiavinotto T, Stützle T (2004) The linear ordering problem: instances, search space analysis and algorithms. J Math Model Algor 3(4):367–402
Laguna M, Martí R, Campos V (1999) Intensification and diversification with elite tabu search solutions for the linear ordering problem. Comput Oper Res 26(12):1217–1230
Chanas S, Kobylański P (1996) A new heuristic algorithm solving the linear ordering problem. Comput Optim Appl 6:191–205
Campos V, Glover F, Laguna M, Martí R (2001) An experimental evaluation of a scatter search for the linear ordering problem. J Glob Optim 21:397–414
Grötschel M, Jünger M, Reinelt G (1984) A cutting plane algorithm for the linear ordering problem. Oper Res 32(6):1195–1220
García CG, Pérez-Brito D, Campos V, Martí R (2006) Variable neighborhood search for the linear ordering problem. Comput Oper Res 33(12):3549–3565
Charon I, Hudry O (2007) A survey on the linear ordering problem for weighted or unweighted tournaments. 4OR 5, 5–60
Charon I, Hudry O (2010) An updated survey on the linear ordering problem for weighted or unweighted tournaments. Ann Oper Res 175:107–158
Sakuraba CS, Yagiura M (2010) Efficient local search algorithms for the linear ordering problem. Intl Trans Op Res 17:711–737
Sakuraba CS, Ronconi DP, Birgin EG, Yagiura M (2015) Metaheuristics for large-scale instances of the linear ordering problem. Expert Syst Appl 42(9):4432–4442
Garcia E, Ceberio J, Lozano JA (2019) Hybrid heuristics for the linear ordering problem. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 1431–1438
Qian Y, Lin J, Li D, Hu H (2020) Block-insertion-based algorithms for the linear ordering problem. Comput Oper Res 115:104861
Santucci V, Ceberio J (2020) Using pairwise precedences for solving the linear ordering problem. Appl Soft Comput 87:105998
Santucci V, Ceberio J, Baioletti M (2020) Gradient search in the space of permutations: an application for the linear ordering problem, pp. 1704–1711. Association for Computing Machinery, New York, NY, USA
Baioletti M, Milani A, Santucci V (2020) Variable neighborhood algebraic differential evolution: an application to the linear ordering problem with cumulative costs. Inf Sci 507:37–52
Fernandes IF, Silva IRdM, Goldbarg EFG, Maia SM, Goldbarg MC (2020) A PSO-inspired architecture to hybridise multi-objective metaheuristics. Memetic Comp 12(3):235–249
Falcón-Cardona JG, Hernández Gómez R, Coello Coello CA, Castillo Tapia MG (2021) Parallel multi-objective evolutionary algorithms: a comprehensive survey. Swarm Evol Comput 67:100960
Sun L, Pan Q-K, Jing X-L, Huang J-P (2021) A light-robust-optimization model and an effective memetic algorithm for an open vehicle routing problem under uncertain travel times. Memetic Comp 13(2):149–167
Asadujjaman M, Rahman HF, Chakrabortty RK, Ryan MJ (2021) A memetic algorithm for concurrent project scheduling, materials ordering and suppliers selection problem. Procedia Computer Science 192, 717–726. Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 25th International Conference KES2021
Amaya JE, Cotta C, Fernández-Leiva AJ, García-Sánchez P (2020) Deep memetic models for combinatorial optimization problems: application to the tool switching problem. Memetic Comp 12(1):3–22
Hernando L, Mendiburu A, Lozano JA (2020) Journey to the center of the linear ordering problem. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO 2020, pp. 201–209. Association for Computing Machinery, New York, NY, USA
Hernández Constantino O, Segura C (2021) A parallel memetic algorithm with explicit management of diversity for the job shop scheduling problem. Appl Intell, 1–13
Sevaux M, Sörensen K, et al. Permutation distance measures for memetic algorithms with population management. In: Proceedings of 6th Metaheuristics International Conference. MIC’05, pp. 832–838
Neri F, Cotta C, Moscato P (2011) Handbook of memetic algorithms. Springer, Berlin, Heidelberg
Tsai H-K, Yang J-M, Tsai Y-F, Kao C-Y (2004) An evolutionary algorithm for large traveling salesman problems. IEEE Trans Syst Man Cybern Syst 34(4):1718–1729
Merz P, Freisleben B (2000) Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Trans Evol Comput 4(4):337–352
Davis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold Company, New York, USA
Song J, Zhao H, Zhou T, Tao Y, Lü Z (2019) Solving the linear ordering problem via a memetic algorithm. In: Arai, K., Bhatia, R., Kapoor, S. (eds.) Proceedings of the future technologies conference (FTC) 2018, pp. 421–430. Springer, Cham
Mitchell JE, Borchers B (2000) Solving linear ordering problems with a combined interior point/simplex cutting plane algorithm. In: Frenk H, Roos K, Terlaky T, Zhang S (eds) High performance optimization. Springer, Boston, MA, pp 349–366
Knuth D (1993) The stanford graphbase: a platform for combinatorial computing. ACM Press, New York, USA
Pérez A, Ceberio J (2018) Creating difficult instances of the linear ordering problem. In: XIII Congreso Español en Metaheurísticas Y Algoritmos Evolutivos Y Bioinspirados, pp. 733–738
Calvo B, Santafé G (2016) scmamp: statistical comparison of multiple algorithms in multiple problems. R Found 8(1):248–256
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M (2011) Édouard Duchesnay: Scikit-learn: machine Learning in Python. J Mach Learn Res 12(85):2825–2830
García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf Sci 180(10):2044–2064
Eiben AE, Smith JE (2015) Introduction to evolutionary computing. Natural Computing Series. Springer, Berlin, Heidelberg
Zhang Y-A, Ma Q, Sakamoto M, Furutani H (2010) Effects of population size on the performance of genetic algorithms and the role of crossover. Artif life Robot 15(2):239–243
del Amo IG, Pelta DA (2013) SRCS: a technique for comparing multiple algorithms under several factors in dynamic optimization problems. Metaheuristics for dynamic optimization. Springer, Berlin, Heidelberg, pp 61–77
Radulescu A, López-Ibáñez M, Stützle T (2013) Automatically improving the anytime behaviour of multiobjective evolutionary algorithms. In: Purshouse RC, Fleming PJ, Fonseca CM, Greco S, Shaw J (eds) Evolutionary multi-criterion optimization. Springer, Berlin, Heidelberg, pp 825–840
Acknowledgements
Authors acknowledge the support from “Laboratorio de Supercómputo del Bajio” through the project 300832 from CONACyT.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Lugo, L., Segura, C. & Miranda, G. A diversity-aware memetic algorithm for the linear ordering Problem. Memetic Comp. 14, 395–409 (2022). https://doi.org/10.1007/s12293-022-00378-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12293-022-00378-5