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Comparing the Brain Storm Optimization Algorithm on the Ambiguous Benchmark Set

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Advances in Swarm Intelligence (ICSI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13344))

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Abstract

In the field of evolutionary computation, benchmarking has a pivotal place in both the development of novel algorithms, and in performing comparisons between existing techniques. In this paper, the computational comparison of the Brain Storm Optimization (BSO) algorithm (a swarm intelligence paradigm inspired by the behaviors of the human process of brainstorming) was performed. A selected representative of the BSO algorithms (namely, BSO20) was compared with other selected methods, which were a mix of canonical methods (both swarm intelligence and evolutionary algorithms) and state-of-the-art techniques. As a test bed, the ambiguous benchmark set was employed. The results showed that even though BSO is not among the best algorithms on this test bed, it is still a well performing method comparable to some state-of-the-art algorithms.

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References

  1. Auger, A., Hansen, N.: A restart CMA evolution strategy with increasing population size. In: 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1769–1776 (2005)

    Google Scholar 

  2. Awad, N., Ali, M., Liang, J., Qu, B., Suganthan, P.: Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective bound constrained real-parameter numerical optimization. Technical report, Nanyang Technological University, Singapore (2017)

    Google Scholar 

  3. Biswas, S., Saha, D., De, S., Cobb, A.D., Das, S., Jalaian, B.A.: Improving differential evolution through Bayesian hyperparameter optimization. In: 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 832–840 (2021)

    Google Scholar 

  4. Bujok, P.: Three steps to improve jellyfish search optimiser. MENDEL J. 27(1), 29–40 (2021)

    Article  Google Scholar 

  5. Cai, Z., Gao, S., Yang, X., Yang, G., Cheng, S., Shi, Y.: Alternate search pattern-based brain storm optimization. Knowl.-Based Syst. 238, 107896 (2022)

    Article  Google Scholar 

  6. Cheng, S., Qin, Q., Chen, J., Shi, Y.: Brain storm optimization algorithm: a review. Artif. Intell. Rev. 46(4), 445–458 (2016). https://doi.org/10.1007/s10462-016-9471-0

    Article  Google Scholar 

  7. Doerr, C., Wang, H., Ye, F., van Rijn, S., Bäck, T.: IOHprofiler: a benchmarking and profiling tool for iterative optimization heuristics. arXiv e-prints arXiv:1810.05281, October 2018

  8. Hellwig, M., Beyer, H.G.: Benchmarking evolutionary algorithms for single objective real-valued constrained optimization - a critical review. Swarm Evol. Comput. 44, 927–944 (2019)

    Article  Google Scholar 

  9. Kazikova, A., Pluhacek, M., Senkerik, R.: Why tuning the control parameters of metaheuristic algorithms is so important for fair comparison? MENDEL J. 26(2), 9–16 (2020)

    Article  Google Scholar 

  10. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 - International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  11. Kudela, J.: Novel zigzag-based benchmark functions for bound constrained single objective optimization. In: 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 857–862. IEEE (2021)

    Google Scholar 

  12. Kudela, J., Matousek, R.: New benchmark functions for single-objective optimization based on a zigzag pattern. IEEE Access 10, 8262–8278 (2022)

    Article  Google Scholar 

  13. Matousek, R., Dobrovsky, L., Kudela, J.: How to start a heuristic? Utilizing lower bounds for solving the quadratic assignment problem. Int. J. Ind. Eng. Comput. 13(2), 151–164 (2022)

    Google Scholar 

  14. Matousek, R., Hulka, T.: Stabilization of higher periodic orbits of the chaotic logistic and Hénon maps using meta-evolutionary approaches. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 1758–1765. IEEE (2019)

    Google Scholar 

  15. Matousek, R., Popela, P., Kudela. J.: Heuristic approaches to stochastic quadratic assignment problem: VaR and CVar cases. Mendel. 23, 73–78 (2017)

    Google Scholar 

  16. Mohamed, A.W., Hadi, A.A., Mohamed, A.K., Agrawal, P., Kumar, A., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2021 special session and competition on single objective bound constrained numerical optimization. Technical report, Cairo University, Egypt (2020)

    Google Scholar 

  17. Mohamed, A.W., Hadi, A.A., Mohamed, A.K., Awad, N.H.: Evaluating the performance of adaptive gaining sharing knowledge based algorithm on CEC 2020 benchmark problems. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2020)

    Google Scholar 

  18. Pluháček, M., Kazikova, A., Kadavy, T., Viktorin, A., Senkerik, R.: Relation of neighborhood size and diversity loss rate in particle swarm optimization with ring topology. MENDEL J. 27(2), 74–79 (2021)

    Article  Google Scholar 

  19. Sallam, K.M., Elsayed, S.M., Chakrabortty, R.K., Ryan, M.J.: Improved multi-operator differential evolution algorithm for solving unconstrained problems. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2020)

    Google Scholar 

  20. Shi, Y.: Brain storm optimization algorithm. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011. LNCS, vol. 6728, pp. 303–309. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21515-5_36

    Chapter  Google Scholar 

  21. Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  22. Tanabe, R., Fukunaga, A.S.: Improving the search performance of shade using linear population size reduction. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1658–1665 (2014)

    Google Scholar 

  23. Veček, N., Črepinšek, M., Mernik, M., Hrnčič, D.: A comparison between different chess rating systems for ranking evolutionary algorithms. In: 2014 Federated Conference on Computer Science and Information Systems, pp. 511–518. IEEE (2014)

    Google Scholar 

  24. Xu, P., Luo, W., Lin, X., Cheng, S., Shi, Y.: BSO20: efficient brain storm optimization for real-parameter numerical optimization. Complex Intell. Syst. 7, 2415–2436 (2021)

    Article  Google Scholar 

  25. Young, S.R., Rose, D.C., Karnowski, T.P., Lim, S.H., Patton, R.M.: Optimizing deep learning hyper-parameters through an evolutionary algorithm. In: Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments, pp. 1–5 (2015)

    Google Scholar 

  26. Zhang, G., Shi, Y.: Hybrid sampling evolution strategy for solving single objective bound constrained problems. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–7 (2018)

    Google Scholar 

  27. Žufan, P., Bidlo, M.: Advances in evolutionary optimization of quantum operators. MENDEL J. 27(2), 12–22 (2021)

    Article  Google Scholar 

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Acknowledgments

This work was supported by internal grant agency of BUT: FME-S-20-6538 “Industry 4.0 and AI methods”, FIT/FSI-J-22-7980, and FEKT/FSI-J-22-7968.

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Authors and Affiliations

  1. Institute of Automation and Computer Science, Brno University of Technology, Brno, Czech Republic

    Jakub Kudela, Tomas Nevoral & Tomas Holoubek

Authors
  1. Jakub Kudela
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  2. Tomas Nevoral
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  3. Tomas Holoubek
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Corresponding author

Correspondence to Jakub Kudela .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

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Kudela, J., Nevoral, T., Holoubek, T. (2022). Comparing the Brain Storm Optimization Algorithm on the Ambiguous Benchmark Set. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_31

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  • DOI: https://doi.org/10.1007/978-3-031-09677-8_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09676-1

  • Online ISBN: 978-3-031-09677-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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