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Algorithm Selection for Large-Scale Multi-objective Optimization

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Optimization and Learning (OLA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1824))

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Abstract

The present study applies Algorithm Selection to automatically specify the suitable algorithms for Large-Scale Multi-objective Optimization. Algorithm Selection has known to benefit from the strengths on multiple algorithm rather than relying one. This trait offers performance gain with limited or no contribution on the algorithm and instance side. As the target application domain, Multi-objective Optimization is a realistic way of approaching any optimization tasks. Most real-world problems are concerned with more than one objective/quality metric. This paper introduces a case study on an Algorithm Selection dataset composed of 4 Multi-objective Optimization algorithms on 63 Large-Scale Multi-objective Optimization problem benchmarks. The benchmarks involve the instances of 2 and 3 objectives with the number of variables changing between 46 and 1006, Hypervolume is the performance indicator used to quantify the solutions derived by each algorithm on every single problem instance. Since Algorithm Selection needs a suite of instance features, 4 simple features are introduced. With this setting, an existing Algorithm Selection system, i.e. ALORS, is accommodated to map these features to the candidate algorithms’ performance denoted in ranks. The empirical analysis showed that this basic setting with AS is able to offer better performance than those standalone algorithms. Further analysis realized on the algorithms and instances report similarities/differences between algorithms and instances while reasoning the instances’ hardness to be solved.

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References

  1. Chong, E.K., Zak, S.H.: An Introduction to Optimization. Wiley, Hoboken (2004)

    MATH  Google Scholar 

  2. Deb, K., Deb, K.: Multi-objective optimization. In: Burke, E., Kendall, G. (eds.) Search Methodologies, pp. 403–449. Springer, Boston (2014). https://doi.org/10.1007/978-1-4614-6940-7_15

    Chapter  Google Scholar 

  3. Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary many-objective optimization: a short review. In: IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 2419–2426. IEEE (2008)

    Google Scholar 

  4. Li, K., Wang, R., Zhang, T., Ishibuchi, H.: Evolutionary many-objective optimization: a comparative study of the state-of-the-art. IEEE Access 6, 26194–26214 (2018)

    Article  Google Scholar 

  5. Hansen, M.P., Jaszkiewicz, A.: Evaluating the quality of approximations to the non-dominated set. Citeseer (1994)

    Google Scholar 

  6. Brockhoff, D., Wagner, T., Trautmann, H.: On the properties of the R2 indicator. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, pp. 465–472 (2012)

    Google Scholar 

  7. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.P. (eds.) PPSN 1998. LNCS, pp. 292–301. Springer, Cham (1998). https://doi.org/10.1007/bfb0056872

    Chapter  Google Scholar 

  8. Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective evolutionary algorithm research: a history and analysis. Technical report, Department of Electrical and Computer Engineering Air Force Institute of Technology, OH, Technical Report TR-98-03 (1998)

    Google Scholar 

  9. Coello Coello, C.A., Reyes Sierra, M.: A study of the parallelization of a coevolutionary multi-objective evolutionary algorithm. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, pp. 688–697. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24694-7_71

    Chapter  Google Scholar 

  10. Ishibuchi, H., Masuda, H., Tanigaki, Y., Nojima, Y.: Modified distance calculation in generational distance and inverted generational distance. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C.C. (eds.) EMO 2015. LNCS, vol. 9019, pp. 110–125. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15892-1_8

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  12. Fonseca, C.M., Knowles, J.D., Thiele, L., Zitzler, E.: A tutorial on the performance assessment of stochastic multiobjective optimizers. In: Proceedings of the 3rd International Conference on Evolutionary Multi-Criterion Optimization (EMO), vol. 216, p. 240 (2005)

    Google Scholar 

  13. Deb, K.: Multi-objective optimisation using evolutionary algorithms: an introduction. In: Wang, L., Ng, A., Deb, K. (eds.) Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, pp. 3–34. Springer, London (2011). https://doi.org/10.1007/978-0-85729-652-8_1

    Chapter  Google Scholar 

  14. Deb, K.: Multi-objective evolutionary algorithms. In: Kacprzyk, J., Pedrycz, W. (eds.) Springer Handbook of Computational Intelligence, pp. 995–1015. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-43505-2_49

    Chapter  Google Scholar 

  15. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., et al. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45356-3_83

    Chapter  Google Scholar 

  16. Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the pareto archived evolution strategy. Evol. Comput. 8, 149–172 (2000)

    Article  Google Scholar 

  17. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2001)

    Google Scholar 

  18. Corne, D.W., Jerram, N.R., Knowles, J.D., Oates, M.J.: PESA-II: region-based selection in evolutionary multiobjective optimization. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 283–290. Morgan Kaufmann Publishers Inc. (2001)

    Google Scholar 

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

    Article  Google Scholar 

  20. Coello, C.C., Lechuga, M.S.: MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), vol. 2, pp. 1051–1056. IEEE (2002)

    Google Scholar 

  21. Ding, L.P., Feng, Y.X., Tan, J.R., Gao, Y.C.: A new multi-objective ant colony algorithm for solving the disassembly line balancing problem. Int. J. Adv. Manuf. Technol. 48, 761–771 (2010)

    Article  Google Scholar 

  22. Mashwani, W.K.: MOEA/D with DE and PSO: MOEA/D-DE+PSO. In: Bramer, M., Petridis, M., Nolle, L. (eds.) SGAI 2011, pp. 217–221. Springer, London (2011). https://doi.org/10.1007/978-1-4471-2318-7_16

    Chapter  Google Scholar 

  23. Ke, L., Zhang, Q., Battiti, R.: MOEA/D-ACO: a multiobjective evolutionary algorithm using decomposition and antcolony. IEEE Trans. Cybern. 43, 1845–1859 (2013)

    Article  Google Scholar 

  24. Alhindi, A., Zhang, Q.: MOEA/D with tabu search for multiobjective permutation flow shop scheduling problems. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 1155–1164. IEEE (2014)

    Google Scholar 

  25. Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997)

    Article  Google Scholar 

  26. Kerschke, P., Hoos, H.H., Neumann, F., Trautmann, H.: Automated algorithm selection: survey and perspectives. Evol. Comput. 27, 3–45 (2019)

    Article  Google Scholar 

  27. Gomes, C., Selman, B.: Algorithm portfolios. Artif. Intell. 126, 43–62 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  28. Loreggia, A., Malitsky, Y., Samulowitz, H., Saraswat, V.A.: Deep learning for algorithm portfolios. In: Proceedings of the 13th Conference on Artificial Intelligence (AAAI), pp. 1280–1286 (2016)

    Google Scholar 

  29. Xu, L., Hutter, F., Hoos, H., Leyton-Brown, K.: SATzilla: portfolio-based algorithm selection for SAT. J. Artif. Intell. Res. 32, 565–606 (2008)

    Article  MATH  Google Scholar 

  30. Yun, X., Epstein, S.L.: Learning algorithm portfolios for parallel execution. In: Hamadi, Y., Schoenauer, M. (eds.) LION 2012. LNCS, pp. 323–338. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34413-8_23

    Chapter  Google Scholar 

  31. Kerschke, P., Trautmann, H.: Automated algorithm selection on continuous black-box problems by combining exploratory landscape analysis and machine learning. Evol. Comput. 27, 99–127 (2019)

    Article  Google Scholar 

  32. Messelis, T., De Causmaecker, P., Vanden Berghe, G.: Algorithm performance prediction for nurse rostering. In: Proceedings of the 6th Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2013), pp. 21–38 (2013)

    Google Scholar 

  33. Musliu, N., Schwengerer, M.: Algorithm selection for the graph coloring problem. In: Nicosia, G., Pardalos, P. (eds.) LION 2013. LNCS, vol. 7997, pp. 389–403. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-44973-4_42

    Chapter  Google Scholar 

  34. Kotthoff, L., Kerschke, P., Hoos, H., Trautmann, H.: Improving the state of the art in inexact TSP solving using per-instance algorithm selection. In: Dhaenens, C., Jourdan, L., Marmion, M.-E. (eds.) LION 2015. LNCS, vol. 8994, pp. 202–217. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19084-6_18

    Chapter  Google Scholar 

  35. Wagner, M., Lindauer, M., Mısır, M., Nallaperuma, S., Hutter, F.: A case study of algorithm selection for the traveling thief problem. J. Heuristics 24, 295–320 (2018)

    Article  Google Scholar 

  36. Stephenson, M., Renz, J.: Creating a hyper-agent for solving angry birds levels. In: AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (2017)

    Google Scholar 

  37. Bischl, B., et al.: ASlib: a benchmark library for algorithm selection. Artif. Intell. 237, 41–58 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  38. Xu, L., Hoos, H., Leyton-Brown, K.: Hydra: automatically configuring algorithms for portfolio-based selection. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI), pp. 210–216 (2010)

    Google Scholar 

  39. Mısır, M., Handoko, S.D., Lau, H.C.: OSCAR: online selection of algorithm portfolios with case study on memetic algorithms. In: Dhaenens, C., Jourdan, L., Marmion, M.-E. (eds.) LION 2015. LNCS, vol. 8994, pp. 59–73. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19084-6_6

    Chapter  Google Scholar 

  40. Mısır, M., Handoko, S.D., Lau, H.C.: ADVISER: a web-based algorithm portfolio deviser. In: Dhaenens, C., Jourdan, L., Marmion, M.-E. (eds.) LION 2015. LNCS, vol. 8994, pp. 23–28. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19084-6_3

    Chapter  Google Scholar 

  41. Lau, H., Mısır, M., Xiang, L., Lingxiao, J.: ADVISER\(^+\): toward a usable web-based algorithm portfolio deviser. In: Proceedings of the 12th Metaheuristics International Conference (MIC), Barcelona, Spain, pp. 592–599 (2017)

    Google Scholar 

  42. Gunawan, A., Lau, H.C., Mısır, M.: Designing and comparing multiple portfolios of parameter configurations for online algorithm selection. In: Festa, P., Sellmann, M., Vanschoren, J. (eds.) LION 2016. LNCS, vol. 10079, pp. 91–106. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50349-3_7

    Chapter  Google Scholar 

  43. Kadioglu, S., Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Algorithm selection and scheduling. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 454–469. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23786-7_35

    Chapter  Google Scholar 

  44. Lindauer, M., Hoos, H.H., Hutter, F., Schaub, T.: AutoFolio: an automatically configured algorithm selector. J. Artif. Intell. Res. 53, 745–778 (2015)

    Article  Google Scholar 

  45. Misir, M.: Cross-domain algorithm selection: algorithm selection across selection hyper-heuristics. In: 2022 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 22–29. IEEE (2022)

    Google Scholar 

  46. Mısır, M., Sebag, M.: ALORS: an algorithm recommender system. Artif. Intell. 244, 291–314 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  47. Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)

    Article  Google Scholar 

  48. Mısır, M.: Data sampling through collaborative filtering for algorithm selection. In: The 16th IEEE Congress on Evolutionary Computation (CEC), pp. 2494–2501. IEEE (2017)

    Google Scholar 

  49. Mısır, M.: Active matrix completion for algorithm selection. In: Nicosia, G., Pardalos, P., Umeton, R., Giuffrida, G., Sciacca, V. (eds.) LOD 2019. LNCS, vol. 11943, pp. 321–334. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37599-7_27

    Chapter  Google Scholar 

  50. Golub, G.H., Reinsch, C.: Singular value decomposition and least squares solutions. Numer. Math. 14, 403–420 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  51. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42, 30–37 (2009)

    Article  Google Scholar 

  52. Mısır, M.: Matrix factorization based benchmark set analysis: a case study on HyFlex. In: Shi, Y., et al. (eds.) SEAL 2017. LNCS, vol. 10593, pp. 184–195. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68759-9_16

    Chapter  Google Scholar 

  53. Mısır, M.: Benchmark set reduction for cheap empirical algorithmic studies. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (2021)

    Google Scholar 

  54. Zille, H., Mostaghim, S.: Comparison study of large-scale optimisation techniques on the LSMOP benchmark functions. In: IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8. IEEE (2017)

    Google Scholar 

  55. Nebro, A., Durillo, J., García-Nieto, J., Coello Coello, C., Luna, F., Alba, E.: SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MCDM 2009), pp. 66–73. IEEE Press (2009)

    Google Scholar 

  56. Ma, X., et al.: A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables. IEEE Trans. Evol. Comput. 20, 275–298 (2015)

    Article  Google Scholar 

  57. Zhang, X., Tian, Y., Cheng, R., Jin, Y.: A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization. IEEE Trans. Evol. Comput. 22, 97–112 (2016)

    Article  Google Scholar 

  58. Zille, H., Ishibuchi, H., Mostaghim, S., Nojima, Y.: A framework for large-scale multiobjective optimization based on problem transformation. IEEE Trans. Evol. Comput. 22, 260–275 (2017)

    Article  Google Scholar 

  59. Auger, A., Bader, J., Brockhoff, D., Zitzler, E.: Hypervolume-based multiobjective optimization: theoretical foundations and practical implications. Theor. Comput. Sci. 425, 75–103 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  60. Cheng, R., Jin, Y., Olhofer, M., et al.: Test problems for large-scale multiobjective and many-objective optimization. IEEE Trans. Cybern. 47, 4108–4121 (2016)

    Article  Google Scholar 

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Authors and Affiliations

  1. Duke Kunshan University, Kunshan, China

    Mustafa Mısır

  2. Dalian University of Technology, Dalian, China

    Xinye Cai

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  1. Mustafa Mısır
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  2. Xinye Cai
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Correspondence to Mustafa Mısır .

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Editors and Affiliations

  1. University of Cadiz, Cadiz, Spain

    Bernabé Dorronsoro

  2. University of Malaga, Malaga, Spain

    Francisco Chicano

  3. University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Gregoire Danoy

  4. University of Lille, Lille, France

    El-Ghazali Talbi

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Mısır, M., Cai, X. (2023). Algorithm Selection for Large-Scale Multi-objective Optimization. In: Dorronsoro, B., Chicano, F., Danoy, G., Talbi, EG. (eds) Optimization and Learning. OLA 2023. Communications in Computer and Information Science, vol 1824. Springer, Cham. https://doi.org/10.1007/978-3-031-34020-8_3

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  • DOI: https://doi.org/10.1007/978-3-031-34020-8_3

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