Abstract
In this paper, a composite method for bound constrained optimization called Composite Evolutionary Strategy and Differential Evolution (CESDE) is described. This method combines two well-performing methods from the Congress on Evolutionary Computation Competitions. Through numerical investigation on the ICSI’2022 benchmark set, the favourite scheme for combining the two methods was determined, and it was found that CESDE outperforms both of its “parental” methods on all studied instances.
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References
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) (2021)
Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2010)
Hadi, A.A., Mohamed, A.W., Jambi, K.M.: Single-objective real-parameter optimization: enhanced LSHADE-SPACMA algorithm. In: Yalaoui, F., Amodeo, L., Talbi, E.-G. (eds.) Heuristics for Optimization and Learning. SCI, vol. 906, pp. 103–121. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-58930-1_7
Ji, Y., Xing, Y.: An optimized three-sub-step composite time integration method with controllable numerical dissipation. Comput. Struct. 231, 106210 (2020)
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)
Kudela, J., Popela, P.: Two-stage stochastic facility location problem: GA with benders decomposition. In: Mendel, vol. 21, pp. 53–58 (2015)
Kudela, J., Matousek, R.: New benchmark functions for single-objective optimization based on a zigzag pattern. IEEE Access 10, 8262–8278 (2022)
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)
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)
Matousek, R., Popela, P., Kudela, J.: Heuristic approaches to stochastic quadratic assignment problem: VaR and CVaR cases. In: Mendel, vol. 23, pp. 73–78 (2017)
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)
Schröder, D., Vermetten, D., Wang, H., Doerr, C., Bäck, T.: Chaining of numerical black-box algorithms: warm-starting and switching points. arXiv preprint arXiv:2204.06539 (2022)
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)
Yifan, L.: Definitions for the ICSI optimization competition 2022 on single objective bounded optimization problems. Key Laboratory of Machine Perception, Peking University, China, Technical report (2022)
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)
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)
Žufan, P., Bidlo, M.: Advances in evolutionary optimization of quantum operators. MENDEL J. 27(2), 12–22 (2021)
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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Kudela, J., Nevoral, T., Holoubek, T. (2022). Composite Evolutionary Strategy and Differential Evolution Method for the ICSI’2022 Competition. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13345. Springer, Cham. https://doi.org/10.1007/978-3-031-09726-3_39
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DOI: https://doi.org/10.1007/978-3-031-09726-3_39
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