[1] T. Hadas and O. Schwartz, "Towards practical fast matrix multiplication based on trilinear aggregation," in
Proceedings of the 2023 International Symposium on Symbolic and Algebraic Computation, 2023, pp. 289-297, doi:
https://doi.org/10.1145/3597066.3597099.
[2] P. D. Khanh, B. S. Mordukhovich, and D. B. Tran, "A new inexact gradient descent method with applications to nonsmooth convex optimization,"
Optimization Methods and Software, pp. 1-29, 2024, doi:
https://doi.org/10.1080/10556788.2024.2322700.
[3] M. Lapucci and P. Mansueto, "A limited memory Quasi-Newton approach for multi-objective optimization,"
Computational Optimization and Applications, vol. 85, no. 1, pp. 33-73, 2023, doi:
https://doi.org/10.1007/s10589-023-00454-7.
[4] K. Barkalov, I. Lebedev, and E. Kozinov, "Acceleration of global optimization algorithm by detecting local extrema based on machine learning,"
Entropy, vol. 23, no. 10, p. 1272, 2021, doi:
https://doi.org/10.3390/e23101272.
[5] R. Jiang and A. Mokhtari, "Accelerated quasi-newton proximal extragradient: Faster rate for smooth convex optimization,"
Advances in Neural Information Processing Systems, vol. 36, 2024, doi:
https://doi.org/10.48550/arXiv.2306.02212.
[6] J. R. Martins and A. Ning, Engineering design optimization. Cambridge University Press, 2021.
[7] M. Sánchez, J. M. Cruz-Duarte, J. carlos Ortíz-Bayliss, H. Ceballos, H. Terashima-Marin, and I. Amaya, "A systematic review of hyper-heuristics on combinatorial optimization problems,"
IEEE Access, vol. 8, pp. 128068-128095, 2020, doi:
https://doi.org/10.1109/ACCESS.2020.3009318.
[8] L. Abualigah, "Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications,"
Neural Computing and Applications, vol. 33, no. 7, pp. 2949-2972, 2021, doi:
https://doi.org/10.1007/s00521-020-05107-y.
[9] M. Jain, V. Saihjpal, N. Singh, and S. B. Singh, "An overview of variants and advancements of PSO algorithm,"
Applied Sciences, vol. 12, no. 17, p. 8392, 2022, doi:
https://doi.org/10.3390/app12178392.
[10] A. Tharwat and W. Schenck, "A conceptual and practical comparison of PSO-style optimization algorithms,"
Expert Systems with Applications, vol. 167, p. 114430, 2021, doi:
https://doi.org/10.1016/j.eswa.2020.114430.
[11] T. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh, and S. Mirjalili, "Particle swarm optimization: A comprehensive survey,"
Ieee Access, vol. 10, pp. 10031-10061, 2022, doi:
https://doi.org/10.1109/ACCESS.2022.3142859.
[12] M. A. Shaheen, H. M. Hasanien, and A. Alkuhayli, "A novel hybrid GWO-PSO optimization technique for optimal reactive power dispatch problem solution,"
Ain Shams Engineering Journal, vol. 12, no. 1, pp. 621-630, 2021, doi:
https://doi.org/10.1016/j.asej.2020.07.011.
[13] Y. Wang and Z. Han, "Ant colony optimization for traveling salesman problem based on parameters optimization,"
Applied Soft Computing, vol. 107, p. 107439, 2021, doi:
https://doi.org/10.1016/j.asoc.2021.107439.
[14] L. Wu, X. Huang, J. Cui, C. Liu, and W. Xiao, "Modified adaptive ant colony optimization algorithm and its application for solving path planning of mobile robot,"
Expert Systems with Applications, vol. 215, p. 119410, 2023, doi:
https://doi.org/10.1016/j.eswa.2022.119410.
[15] H. Liang, J. Zou, K. Zuo, and M. J. Khan, "An improved genetic algorithm optimization fuzzy controller applied to the wellhead back pressure control system,"
Mechanical Systems and Signal Processing, vol. 142, p. 106708, 2020, doi:
https://doi.org/10.1016/j.ymssp.2020.106708.
[16] H. Moayedi, M. Raftari, A. Sharifi, W. A. W. Jusoh, and A. S. A. Rashid, "Optimization of ANFIS with GA and PSO estimating α ratio in driven piles,"
Engineering with Computers, vol. 36, no. 1, pp. 227-238, 2020, doi:
https://doi.org/10.1007/s00366-018-00694-w.
[17] H. Chung and K.-s. Shin, "Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction,"
Neural Computing and Applications, vol. 32, no. 12, pp. 7897-7914, 2020, doi:
https://doi.org/10.1007/s00521-019-04236-3.
[18] H. Alibrahim and S. A. Ludwig, "Hyperparameter optimization: Comparing genetic algorithm against grid search and bayesian optimization," in
2021 IEEE Congress on Evolutionary Computation (CEC), 2021: IEEE, pp. 1551-1559, doi:
https://doi.org/10.1109/CEC45853.2021.9504761.
[19] L. Liu, X. Su, L. Chen, S. Wang, J. Li, and S. Liu, "Elite Genetic Algorithm based self-sufficient Energy Management System for Integrated Energy Station,"
IEEE Transactions on Industry Applications, 2023, doi:
https://doi.org/10.1109/TIA.2023.3292326.
[20] G. Papazoglou and P. Biskas, "Review and comparison of genetic algorithm and particle swarm optimization in the optimal power flow problem,"
Energies, vol. 16, no. 3, p. 1152, 2023, doi:
https://doi.org/10.3390/en16031152.
[21] A. Nemirovski, Introduction to linear optimization. World Scientific, 2024.
[22] C. Darwin, "Origin of the Species," in British Politics and the Environment in the Long Nineteenth Century: Routledge, 2023, pp. 47-55.
[23] S. Fidanova and S. Fidanova, "Ant colony optimization,"
Ant Colony Optimization and Applications, pp. 3-8, 2021. [Online]. Available:
https://books.google.com/books?id=SoogEAAAQBAJ&lr=&source=gbs_navlinks_s.
[24] M. Danilova et al., "Recent theoretical advances in non-convex optimization," in High-Dimensional Optimization and Probability: With a View Towards Data Science: Springer, 2022, pp. 79-163.
[25] T. Osa, "Multimodal trajectory optimization for motion planning,"
The International Journal of Robotics Research, vol. 39, no. 8, pp. 983-1001, 2020, doi:
https://doi.org/10.1177/0278364920918296.
[26] R. Jin, P. Rocco, and Y. Geng, "Cartesian trajectory planning of space robots using a multi-objective optimization,"
Aerospace Science and Technology, vol. 108, p. 106360, 2021, doi:
https://doi.org/10.1016/j.ast.2020.106360.
[27] H. Chung and K.-s. Shin, "Genetic algorithm-optimized long short-term memory network for stock market prediction,"
Sustainability, vol. 10, no. 10, p. 3765, 2018, doi:
https://doi.org/10.3390/su10103765.
[28] D. C. Montgomery and G. C. Runger, Applied statistics and probability for engineers. John wiley & sons, 2020.
[29] M. Baron, Probability and statistics for computer scientists. Chapman and Hall/CRC, 2019.
[30] J. Peng, L. Li, and Y. Y. Tang, "Maximum likelihood estimation-based joint sparse representation for the classification of hyperspectral remote sensing images,"
IEEE transactions on neural networks and learning systems, vol. 30, no. 6, pp. 1790-1802, 2018, doi:
https://doi.org/10.1109/TNNLS.2018.2874432.
[31] K. Hopf and S. Reifenrath, "Filter Methods for Feature Selection in Supervised Machine Learning Applications--Review and Benchmark,"
arXiv preprint arXiv:2111.12140, 2021, doi:
https://doi.org/10.48550/arXiv.2111.12140.
