Document Type : Research Article
Authors
Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.
Abstract
Keywords
Main Subjects
| [1] | P. N. Suganthan and N. Hansen and J. J. Liang and K. Deb and Y.-P. Chen and A. Auger and S. Tiwari. Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report. https://www.academia.edu/download/33925834/Tech-Report-May-30-05.pdf. Nanyang Technological University Singapore. 2005. |
| [2] | M. Friedman. A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics. https://www.jstor.org/stable/2235971. 11(1): 86--92, JSTOR. 1940. |
| [3] | W. J. Gutjahr. Convergence Analysis of Metaheuristics. 159--187, Springer US. 2010. [DOI ] |
| [4] | Z. Zhang and Y. Gao and W. Zuo. A dual biogeography-based optimization algorithm for solving high-dimensional global optimization problems and engineering design problems. IEEE Access. 10: 55988--56016, IEEE. 2022. [DOI ] |
| [5] | K. Benidis and Y. Feng and D. P. Palomar. Sparse portfolios for high-dimensional financial index tracking. IEEE Transactions on signal processing. 66(1): 155--170, IEEE. 2017. [DOI ] |
| [6] | J. A. Carrillo and S. Jin and L. Li and Y. Zhu. A consensus-based global optimization method for high dimensional machine learning problems. ESAIM: Control and Optimisation and Calculus of Variations. 27: S5, EDP Sciences. 2021. [DOI ] |
| [7] | F. MiarNaeimi and G. Azizyan and M. Rashki. Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems. Knowledge-Based Systems. 213: 106711, Elsevier. 2021. [DOI ] |
| [8] | A. Singh and N. Dulal. A survey on metaheuristics for solving large scale optimization problems. Int. J. Comput. Appl. 170(5): 1--7, 2017. [DOI ] |
| [9] | N. Aslimani and R. Ellaia. A new hybrid algorithm combining a new chaos optimization approach with gradient descent for high dimensional optimization problems. Computational and Applied Mathematics. 37: 2460--2488, Springer. 2018. [DOI ] |
| [10] | E. K. Nyarko and R. Cupec and D. Filko. A comparison of several heuristic algorithms for solving high dimensional optimization problems. International journal of electrical and computer engineering systems. https://hrcak.srce.hr/134402. 5(1.): 1--8, 2014. |
| [11] | L. Abualigah and D. Yousri and M. Abd Elaziz and A. A. Ewees and M. A. Al-Qaness and A. H. Gandomi. Aquila optimizer: a novel meta-heuristic optimization algorithm. Computers and Industrial Engineering. 157: 107250, Elsevier. 2021. [DOI ] |
| [12] | S. Mahajan and L. Abualigah and A. K. Pandit and M. Altalhi. Hybrid {Aquila} optimizer with arithmetic optimization algorithm for global optimization tasks. Soft Computing. 26(10): 4863--4881, Springer. 2022. [DOI ] |
| [13] | P. Jiang and Q. Zhou and X. Shao and P. Jiang and Q. Zhou and X. Shao. Surrogate Model-Based Engineering Design and Optimization. Springer. 2020. [DOI ] |
| [14] | G. Chen and K. Zhang and X. Xue and L. Zhang and C. Yao and J. Wang and J. Yao. A radial basis function surrogate model assisted evolutionary algorithm for high-dimensional expensive optimization problems. Applied Soft Computing. 116: 108353, Elsevier. 2022. [DOI ] |
| [15] | J. O. Agushaka and A. E. Ezugwu and L. Abualigah. Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer. Neural Computing and Applications. 35(5): 4099--4131, Springer. 2023. [DOI ] |
| [16] | L. Abualigah and M. Abd Elaziz and P. Sumari and Z. W. Geem and A. H. Gandomi. Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Systems with Applications. 191: 116158, Elsevier. 2022. [DOI ] |
| [17] | A. E. Ezugwu and J. O. Agushaka and L. Abualigah and S. Mirjalili and A. H. Gandomi. Prairie dog optimization algorithm. Neural Computing and Applications. 34(22): 20017--20065, Springer. 2022. [DOI ] |
| [18] | F. A. Hashim and R. R. Mostafa and A. G. Hussien and S. Mirjalili and K. M. Sallam. Fick’s Law Algorithm: A physical law-based algorithm for numerical optimization. Knowledge-Based Systems. 260: 110146, Elsevier. 2023. [DOI ] |
| [19] | X. Zhang and B. Yang and T. Yu and L. Jiang. Dynamic Surrogate Model based optimization for MPPT of centralized thermoelectric generation systems under heterogeneous temperature difference. IEEE Transactions on Energy Conversion. 35(2): 966--976, IEEE. 2020. [DOI ] |
| [20] | M. Shi and L. Lv and W. Sun and X. Song. A multi-fidelity surrogate model based on support vector regression. Structural and Multidisciplinary Optimization. 61: 2363--2375, Springer. 2020. [DOI ] |
| [21] | R. S. Tabar and K. Warmefjord and R. Soderberg. A new surrogate model--based method for individualized spot welding sequence optimization with respect to geometrical quality. The International Journal of Advanced Manufacturing Technology. 106: 2333--2346, Springer. 2020. [DOI ] |
| [22] | K. Bamdad and M. E. Cholette and J. Bell. Building energy optimization using surrogate model and active sampling. Journal of Building Performance Simulation. 13(6): 760--776, Taylor and Francis. 2020. [DOI ] |
| [23] | H. A. Pedrozo and S. R. Reartes and Q. Chen and M. S. Diaz and I. E. Grossmann. Surrogate-model based MILP for the optimal design of ethylene production from shale gas. Computers and Chemical Engineering. 141: 107015, Elsevier. 2020. [DOI ] |
| [24] | J. Muller. {SOCEMO}: surrogate optimization of computationally expensive multiobjective problems. INFORMS Journal on Computing. 29(4): 581--596, INFORMS. 2017. [DOI ] |
| [25] | J. C. Garcia-Garcia and R. Garcia-Rodenas and E. Codina. A surrogate-based cooperative optimization framework for computationally expensive black-box problems. Optimization and Engineering. 21(3): 1053--1093, Springer. 2020. [DOI ] |
| [26] | J. Yi and Y. Shen and C. A. Shoemaker. A multi-fidelity {RBF} surrogate-based optimization framework for computationally expensive multi-modal problems with application to capacity planning of manufacturing systems. Structural and Multidisciplinary Optimization. 62: 1787--1807, Springer. 2020. [DOI ] |
| [27] | S. Akyol. A new hybrid method based on Aquila optimizer and tangent search algorithm for global optimization. Journal of Ambient Intelligence and Humanized Computing. 14(6): 8045--8065, Springer. 2023. [DOI ] |
| [28] | E. Demiral and C. Sahin and K. Arslan. Aerodynamic Shape Optimization Using Multi-fidelity Surrogate-based Approach for Computationally Expensive Problems. AIAA AVIATION 2022 Forum. 4161, 2022. [DOI ] |
| [29] | M. H. Nadimi-Shahraki and S. Taghian and S. Mirjalili and L. Abualigah. Binary aquila optimizer for selecting effective features from medical data: A COVID-19 case study. Mathematics. 10(11): 1929, MDPI. 2022. [DOI ] |
| [30] | H. Yu and H. Jia and J. Zhou and A. Hussien. Enhanced {Aquila} optimizer algorithm for global optimization and constrained engineering problems. Mathematical Biosciences and Engineering. 19(12): 14173--14211, AMER INST MATHEMATICAL SCIENCES-AIMS. 2022. [DOI ] |
| [31] | Y. Xiao and Y. Guo and H. Cui and Y. Wang and J. Li and Y. Zhang. {IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems}. Mathematical Biosciences and Engineering. 19(11): 10963--11017, 2022. [DOI ] |
| [32] | M. Li and J. Tang and X. Meng. Multiple Surrogate-Model-Based Optimization Method Using the Multimodal Expected Improvement Criterion for Expensive Problems. Mathematics. 10(23): 4467, MDPI. 2022. [DOI ] |
| [33] | H. Liu and X. Zhang and H. Zhang and C. Li and Z. Chen. A reinforcement learning-based hybrid {Aquila} Optimizer and improved Arithmetic Optimization Algorithm for global optimization. Expert Systems with Applications. 224: 119898, Elsevier. 2023. [DOI ] |
| [34] | M. Wu and J. Xu and L. Wang and C. Zhang and H. Tang. Adaptive Multi-Surrogate and Module-based Optimization Algorithm for High-dimensional and Computationally Expensive Problems. Information Sciences. 119308, Elsevier. 2023. [DOI ] |
| [35] | E. Bacs. Binary Aquila Optimizer for 0--1 knapsack problems. Engineering Applications of Artificial Intelligence. 118: 105592, Elsevier. 2023. [DOI ] |
| [36] | A. A. Abd El-Mageed and A. A. Abohany and A. Elashry. Effective Feature Selection Strategy for Supervised Classification based on an Improved Binary Aquila Optimization Algorithm. Computers and Industrial Engineering. 181: 109300, Elsevier. 2023. [DOI ] |
| [37] | H. Liu and J. Tian and Q. Yu and X. Liu and G. Wang. A Historical Surrogate Model Ensemble Assisted Bayesian Evolutionary Optimization Algorithm for Solving Expensive Many-Objective Problems. Available at SSRN 4537543. [DOI ] |
| [38] | M. Ahmadipour and M. M. Othman and R. Bo and M. S. Javadi and H. M. Ridha and M. Alrifaey. Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer. Expert Systems with Applications. 235: 121212, Elsevier. 2024. [DOI ] |
| [39] | B. Deng. Aquila optimizer with dynamic group strategy for global optimization tasks. Concurrency and Computation: Practice and Experience. e7971, Wiley Online Library. 2023. [DOI ] |
| [40] | L. Abualigah and K. H. Almotairi. Dynamic evolutionary data and text document clustering approach using improved Aquila optimizer based arithmetic optimization algorithm and differential evolution. Neural Computing and Applications. 34(23): 20939--20971, Springer. 2022. [DOI ] |
| [41] | L. Zeng and M. Li and J. Shi and S. Wang. Spiral Aquila optimizer based on dynamic gaussian mutation: Applications in global optimization and engineering. Neural Processing Letters. 55(8): 11653--11699, Springer. 2023. [DOI ] |
| [42] | F. Wilcoxon. Individual comparisons by ranking methods. Breakthroughs in Statistics: Methodology and Distribution. 196--202, Springer. 1992. [DOI ] |
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