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Analysis of statistical model-based optimization enhancements in Generalized Self-Adapting Particle Swarm Optimization framework


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[1] Bartz-Beielstein T. and Zaefferer M. Model-based methods for continuous and discrete global optimization. Applied Soft Computing, 55:154–167, 2017.10.1016/j.asoc.2017.01.039Search in Google Scholar

[2] Cai X., Qiu H., Gao L., Jiang C., and Shao X. An efficient surrogate-assisted particle swarm optimization algorithm for high-dimensional expensive problems. Knowledge-Based Systems, 184:104901, nov 2019.10.1016/j.knosys.2019.104901Search in Google Scholar

[3] Chugh T., Rahat A., Volz V., and Zaefferer M. Towards Better Integration of Surrogate Models and Optimizers. In High-Performance Simulation-Based Optimization, pages 137–163. 2020.10.1007/978-3-030-18764-4_7Search in Google Scholar

[4] Chugh T., Sun C., Wang H., and Jin Y. Surrogate-Assisted Evolutionary Optimization of Large Problems, pages 165–187. Springer International Publishing, Cham, 2020.10.1007/978-3-030-18764-4_8Search in Google Scholar

[5] Clerc M. Standard particle swarm optimisation. 2012.Search in Google Scholar

[6] Das S., Abraham A., and Konar A. Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspectives. In Advances of computational intelligence in industrial systems, pages 1–38. Springer, 2008.10.1007/978-3-540-78297-1_1Search in Google Scholar

[7] Guttman A. R-trees: A dynamic index structure for spatial searching. In Proceedings of the 1984 ACM SIGMOD international conference on Management of data, pages 47–57, 1984.10.1145/971697.602266Search in Google Scholar

[8] Hansen N. The CMA Evolution Strategy: A Comparing Review. In Towards a New Evolutionary Computation: Advances in the Estimation of Distribution Algorithms, pages 75–102. Springer Berlin Heidelberg, Berlin, Heidelberg, 2006.10.1007/3-540-32494-1_4Search in Google Scholar

[9] Hansen N., Brockho D., Mersmann O., Tusar T., Tusar D., ElHara O. A., Sampaio P. R., Atamna A., Varelas K., Batu U., Nguyen D. M., Matzner F., and Auger A. COmparing Continuous Optimizers: numbbo/COCO on Github, 2019.Search in Google Scholar

[10] Jin Y. A comprehensive survey of fitness approximation in evolutionary computation. Soft computing, 9(1):3–12, 2005.10.1007/s00500-003-0328-5Search in Google Scholar

[11] Kennedy J. and Eberhart R. C. Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks. IV, pages 1942–1948, 1995.Search in Google Scholar

[12] Kleijnen J. P. C. Simulation Optimization Through Regression or Kriging Meta-models. In High-Performance Simulation-Based Optimization, pages 115–135. 2020.10.1007/978-3-030-18764-4_6Search in Google Scholar

[13] Nepomuceno F. V. and Engelbrecht A. P. A Self-adaptive Heterogeneous PSO Inspired by Ants. In International Conference on Swarm Intelligence, pages 188–195. Springer, 2012.10.1007/978-3-642-32650-9_17Search in Google Scholar

[14] Okulewicz M. and Mańdziuk J. Application of Particle Swarm Optimization Algorithm to Dynamic Vehicle Routing Problem. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7895:547–558, 2013.10.1007/978-3-642-38610-7_50Search in Google Scholar

[15] Okulewicz M. and Mańdziuk J. Two-phase multi-swarm PSO and the dynamic vehicle routing problem. In 2014 IEEE Symposium on Computational Intelligence for Human-like Intelligence (CIHLI), pages 1–8, Orlando, Fl, USA, dec 2014. IEEE.10.1109/CIHLI.2014.7013391Search in Google Scholar

[16] Okulewicz M., Zaborski M., and Mańdziuk J. Generalized Self-Adapting Particle Swarm Optimization algorithm with archive of samples, 2020. preprint, available at: https://arxiv.org/pdf/2002.12485.Search in Google Scholar

[17] Pitra Z., Bajer L., and Holeňa M. Doubly Trained Evolution Control for the Surrogate CMA-ES. In Parallel Problem Solving from Nature – PPSN XIV, pages 59–68. Springer International Publishing, Cham, 2016.10.1007/978-3-319-45823-6_6Search in Google Scholar

[18] Poaík P. and Klema V. JADE, an adaptive differential evolution algorithm, benchmarked on the BBOB noiseless testbed. In Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion - GECCO Companion ’12, page 197, New York, New York, USA, 2012. ACM Press.10.1145/2330784.2330814Search in Google Scholar

[19] Poli R. An analysis of publications on particle swarm optimization applications. Technical report, Technical Report CSM-469, Department of Computer Science, University of Essex, 2007.Search in Google Scholar

[20] Storn R. and Price K. Differential Evolution – A Simple and E cient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4):341–359, 1997.10.1023/A:1008202821328Search in Google Scholar

[21] Uliński M., ˙Zychowski A., Okulewicz M., Zaborski M., and Kordulewski H. Generalized Self-adapting Particle Swarm Optimization Algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 3242, pages 29–40. Springer, Cham, 2018.10.1007/978-3-319-99253-2_3Search in Google Scholar

[22] Yamaguchi T. and Akimoto Y. Benchmarking the novel CMA-ES restart strategy using the search history on the BBOB noiseless testbed. In GECCO ’17 Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 1780–1787, 2017.10.1145/3067695.3084203Search in Google Scholar

[23] Zaborski M., Okulewicz M., and Mańdziuk J. Generalized Self-Adapting Particle Swarm Optimization algorithm with model-based optimization enhancements. In 2nd PP-RAI Conference (PPRAI-19), pages 380–383, Wrocław, Poland, 2019. Wrocław University of Science and Technology.Search in Google Scholar

[24] Zhang J. and Sanderson A. C. Jade: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation, 13(5):945–958, 2009.10.1109/TEVC.2009.2014613Search in Google Scholar

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