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A master-apprentice evolutionary algorithm for maximum weighted set K-covering problem

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Abstract

The maximum weighted set k-covering problem (MWKCP) is a fundamental optimization problem, which depicts the application scenario of resource-constrained environment and user preference selection. In this paper, the mathematical formulation of MWKCP is given for the first time. Then, a novel master-apprentice evolutionary algorithm (MAE) is proposed for solving this NP-hard optimization problem. In order to make MAE applicable to MWKCP, a path re-linking operator is designed as the mutual learning process of two individuals, and a bare bones fireworks algorithm with explosion amplitude adaptation is adopted as the self-learning stage. Experimental results on 150 classical instances show that the proposed algorithm performs best among all competitors including an exact solver and three heuristic algorithms.

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References

  1. Máximo V R, Nascimento MCV, Carvalho ACPLF (2017) Intelligent-guided adaptive search for the maximum covering location problem. Comput Oper Res 78:129–137

    Article  MathSciNet  MATH  Google Scholar 

  2. Heil J, Hoffmann K, Buscher U (2020) Railway crew scheduling: Models, methods and applications. Eur J Oper Res 283(2):405–425

    Article  MathSciNet  MATH  Google Scholar 

  3. Zhang X-Y, Zhang J, Gong Y-J, Zhan Z-H, Chen W-N, Li Y (2015) Kuhn–munkres parallel genetic algorithm for the set cover problem and its application to large-scale wireless sensor networks. IEEE Trans Evol Comput 20(5):695–710

    Article  Google Scholar 

  4. Anagnostopoulos A, Becchetti L, Bordino I, Leonardi S, Mele I, Sankowski P (2015) Stochastic query covering for fast approximate document retrieval. ACM Trans Inf Syst (TOIS) 33(3):1–35

    Article  Google Scholar 

  5. Chierichetti F, Kumar R, Tomkins A (2010) Max-cover in map-reduce. In: Proceedings of the 19th international conference on World wide web, pp 231–240

  6. Kritter J, Brévilliers M, Lepagnot J, Idoumghar L (2019) On the optimal placement of cameras for surveillance and the underlying set cover problem. Appl Soft Comput 74:133–153

    Article  Google Scholar 

  7. Johnson D S (1985) The np-completeness column: an ongoing guide. J Algorithm 6(3):434–451

    Article  MathSciNet  MATH  Google Scholar 

  8. Yu H, Yuan D (2013) Set coverage problems in a one-pass data stream. In: Proceedings of the 2013 SIAM international conference on data mining. SIAM, pp 758–766

  9. McGregor A, Vu H T (2019) Better streaming algorithms for the maximum coverage problem. Theory Comput Syst 63(7):1595–1619

    Article  MathSciNet  MATH  Google Scholar 

  10. Indyk P, Vakilian A (2019) Tight trade-offs for the maximum k-coverage problem in the general streaming model. In: Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pp 200–217

  11. Wang Y, Ouyang D, Yin M, Zhang L, Zhang Y (2018) A restart local search algorithm for solving maximum set k-covering problem. Neural Comput Appl 29(10):755–765

    Article  Google Scholar 

  12. Lin G, Guan J (2018) Solving maximum set k-covering problem by an adaptive binary particle swarm optimization method. Knowl-Based Syst 142:95–107

    Article  Google Scholar 

  13. Lin G, Xu H, Chen X, Guan J (2020) An effective binary artificial bee colony algorithm for maximum set k-covering problem. Expert Syst Appl 161:113717

    Article  Google Scholar 

  14. Song Y, Wang F, Chen X (2019) An improved genetic algorithm for numerical function optimization. Appl Intell 49(5):1880–1902

    Article  Google Scholar 

  15. Yu J, You X, Liu S (2021) Dynamic reproductive ant colony algorithm based on piecewise clustering. Appl Intell:1–21

  16. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Glob Optim 39(3):459–471

    Article  MathSciNet  MATH  Google Scholar 

  17. Chen W-N, Tan D-Z (2018) Set-based discrete particle swarm optimization and its applications: a survey. Front Comput Sci 12(2):203–216

    Article  Google Scholar 

  18. Wang C-F, Song W-X (2019) A novel firefly algorithm based on gender difference and its convergence. Appl Soft Comput 80:107–124

    Article  Google Scholar 

  19. Duan H, Qiu H (2019) Advancements in pigeon-inspired optimization and its variants. Sci China Inf Sci 62(7):70201–1

    Article  MathSciNet  Google Scholar 

  20. Ding J, Lü Z, Li C-M, Shen L, Xu L, Glover F (2019) A two-individual based evolutionary algorithm for the flexible job shop scheduling problem. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 33, pp 2262–2271

  21. Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. In: International conference in swarm intelligence. Springer, pp 355–364

  22. Wang Y, Lü Z, Punnen A P (2021) A fast and robust heuristic algorithm for the minimum weight vertex cover problem. IEEE Access 9:31932–31945

    Article  Google Scholar 

  23. Tan Y, Yu C, Zheng S, Ding K (2013) Introduction to fireworks algorithm. Int J Swarm Intell Res (IJSIR) 4(4):39–70

    Article  Google Scholar 

  24. Li J, Tan Y (2018) The bare bones fireworks algorithm: A minimalist global optimizer. Appl Soft Comput 62:454–462

    Article  Google Scholar 

  25. Yu J, Takagi H, Tan Y (2018) Multi-layer explosion based fireworks algorithm. J Swarm Intel Evol Comput 7(173):2

    Google Scholar 

  26. Li J, Tan Y (2019) A comprehensive review of the fireworks algorithm. ACM Comput Surv (CSUR) 52(6):1–28

    Article  Google Scholar 

  27. Peng B, Zhang Y, Cheng TCE, Lü Z, Punnen A P (2020) A two-individual based path-relinking algorithm for the satellite broadcast scheduling problem. Knowl-Based Syst 196:105774

    Article  Google Scholar 

  28. Lai X, Hao J-K (2016) Iterated variable neighborhood search for the capacitated clustering problem. Eng Appl Artif Intell 56:102–120

    Article  Google Scholar 

  29. Qiu Y, Wang L, Xu X, Fang X, Pardalos P M (2018) A variable neighborhood search heuristic algorithm for production routing problems. Appl Soft Comput 66:311–318

    Article  Google Scholar 

  30. Hansen P, Mladenović N (2001) Variable neighborhood search: Principles and applications. Eur J Oper Res 130(3):449–467

    Article  MathSciNet  MATH  Google Scholar 

  31. Li X, Gao L, Pan Q, Wan L, Chao K-M (2018) An effective hybrid genetic algorithm and variable neighborhood search for integrated process planning and scheduling in a packaging machine workshop. IEEE Trans Syst Man Cybern Syst 49(10):1933–1945

    Article  Google Scholar 

  32. Gao C, Yao X, Weise T, Li J (2015) An efficient local search heuristic with row weighting for the unicost set covering problem. Eur J Oper Res 246(3):750–761

    Article  MathSciNet  MATH  Google Scholar 

  33. Yang P, Tang K, Yao X (2017) Turning high-dimensional optimization into computationally expensive optimization. IEEE Trans Evol Comput 22(1):143–156

    Article  Google Scholar 

  34. Wei F-F, Chen W-N, Yang Q, Deng J, Luo X-N, Jin H, Zhang J (2020) A classifier-assisted level-based learning swarm optimizer for expensive optimization. IEEE Trans Evol Comput 25(2):219–233

    Article  Google Scholar 

  35. Hettmansperger T P, McKean J W (2010) Robust nonparametric statistical methods. CRC Press

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Acknowledgements

This work is supported by the Fundamental Research Funds for the Central Universities (2412020FZ030, 2412018QD022), NSFC under Grant No. (61806050, 61972063, 61976050,61972384), Jilin Science and Technology Association QT202005, and and Jilin Provincial Science and Technology Department under Grant No. 20190302109GX.

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Authors and Affiliations

  1. College of Information Science and Technology, Northeast Normal University, Changchun, China

    Yupeng Zhou, Xiaofan Liu, Xin Xu, Yiyuan Wang & Minghao Yin

  2. Key Laboratory of Applied Statistics of MOE, Northeast Normal University, Changchun, China

    Yupeng Zhou, Xiaofan Liu, Xin Xu, Yiyuan Wang & Minghao Yin

  3. School of Science, Beijing University of Posts and Telecommunications, Beijing, China

    Mingjie Fan

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  1. Yupeng Zhou
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  2. Mingjie Fan
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  3. Xiaofan Liu
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  4. Xin Xu
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  5. Yiyuan Wang
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  6. Minghao Yin
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Correspondence to Xin Xu.

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Zhou, Y., Fan, M., Liu, X. et al. A master-apprentice evolutionary algorithm for maximum weighted set K-covering problem. Appl Intell 53, 1912–1944 (2023). https://doi.org/10.1007/s10489-022-03531-2

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