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Performance Assessment of Natural Survivor Method-Based Metaheuristic Optimizers in Global Optimization and Engineering Design Problems

Year 2024, Volume: 7 Issue: 2, 227 - 243, 31.08.2024

Hüseyin Bakır

https://doi.org/10.35377/saucis...1474767

Abstract

This study presents the comparative performance analysis of Natural Survivor Method (NSM)-based algorithms in solving the IEEE CEC 2022 test suite benchmark problems and four real-world engineering design problems. Three different variants (Case1, Case2, Case3) of the NSM-TLABC, NSM-SFS and NSM-LSHADE-SPACMA algorithms were used in the study. The data obtained from the experimental studies were statistically analyzed using Friedman and Wilcoxon signed-rank tests. Based on the Friedman test results, NSM-LSHADE-SPACMA_Case2 showed the best performance with an average Friedman score of 3.96. The Wilcoxon signed-rank test showed that NSM-LSHADE-SPACMA_Case2 outperformed its competitors in 13 out of 16 experiments, achieving a success rate of 81.25%. NSM-LSHADE-SPACMA_Case2, which was found to be the most powerful of the NSM-based algorithms, is used to solve cantilever beam design, tension/compression spring design, pressure vessel design and gear train design problems. The optimization results are also compared with eight state-of-the-art metaheuristics, including Rime Optimization Algorithm (RIME), Nonlinear Marine Predator Algorithm (NMPA), Northern Goshawk Optimization (NGO), Kepler Optimization Algorithm (KOA), Honey Badger Algorithm (HBA), Artificial Gorilla Troops Optimizer (GTO), Exponential Distribution Optimization (EDO) and Hunger Games Search (HGS). Given that all results are together, it is seen that NSM-LSHADE-SPACMA_Case2 algorithm consistently produced the best results for the global and engineering design problems studied.

Keywords

Natural survivor method-based algorithms Global optimization IEEE CEC 2022 test suite Real-world engineering design problems

References

  • [1] L. Abualigah, D. Yousri, M. Abd Elaziz, A. A. Ewees, M. A. Al-Qaness, and A. H. Gandomi, “Aquila optimizer: a novel meta-heuristic optimization algorithm,” Computers & Industrial Engineering, vol. 157, 107250, 2021.
  • [2] O. Altay, “Chaotic slime mould optimization algorithm for global optimization,” Artificial Intelligence Review, vol. 55, no. 5, pp. 3979-4040, 2022.
  • [3] P. Civicioglu and E. Besdok, “Bezier Search Differential evolution algorithm for numerical function optimization: A comparative study with CRMLSP, MVO, WA, SHADE and LSHADE,” Expert Systems with Applications, vol. 165, 113875, 2021.
  • [4] A. Özkış and A. Babalık, “Solving constrained engineering design problems with multi-objective artificial algae algorithm,” Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 29, no. 2, pp. 183-193, 2023.
  • [5] F. A. Hashim, E. H. Houssein, K. Hussain, M. S. Mabrouk, and W. Al-Atabany, “Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems,” Mathematics and Computers in Simulation, vol. 192, pp. 84-110, 2022.
  • [6] H. T. Kahraman, M. Katı, S. Aras, and D. A. Taşci, “Development of the Natural Survivor Method (NSM) for designing an updating mechanism in metaheuristic search algorithms,” Engineering Applications of Artificial Intelligence, vol. 122, 106121, 2023.
  • [7] V. Hayyolalam and A. A. P. Kazem, “Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems,” Engineering Applications of Artificial Intelligence, vol. 87, 103249, 2020.
  • [8] K. Zervoudakis and S. Tsafarakis, “A mayfly optimization algorithm,” Computers & Industrial Engineering, vol. 145, 106559, 2020.
  • [9] Y. Zhang and Z. Jin, “Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems,” Expert Systems with Applications, vol. 148, 113246, 2020.
  • [10] I. Ahmadianfar, O. Bozorg-Haddad, and X. Chu, “Gradient-based optimizer: A new metaheuristic optimization algorithm,” Information Sciences, vol. 540, pp. 131-159, 2020.
  • [11] M. H. Qais, H. M. Hasanien, and S. Alghuwainem, “Transient search optimization: a new meta-heuristic optimization algorithm,” Applied Intelligence, vol. 50, 3926-3941, 2020.
  • [12] M. S. Braik, “Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems,” Expert Systems with Applications, vol. 174, 114685, 2021.
  • [13] F. MiarNaeimi, G. Azizyan, and M. Rashki, “Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems,” Knowledge-Based Systems, vol. 213, 106711, 2021.
  • [14] M. Braik, A. Sheta, and H. Al-Hiary, “A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm,” Neural Computing and Applications, vol. 33, no.7, pp. 2515-2547, 2021.
  • [15] F. A. Hashim, K. Hussain, E. H. Houssein, M. S. Mabrouk, and W. Al-Atabany, “Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems,” Applied Intelligence, vol. 51, pp. 1531-1551, 2021.
  • [16] J. O.Agushaka, A. E. Ezugwu, and L. Abualigah, “Dwarf mongoose optimization algorithm”. Computer Methods in Applied Mechanics and Engineering, vol. 391, 114570, 2022.
  • [17] J. S. Pan, L. G. Zhang, R. B. Wang, V. Snášel, and S. C. Chu, “Gannet optimization algorithm: A new metaheuristic algorithm for solving engineering optimization problems,” Mathematics and Computers in Simulation, vol. 202, pp. 343-373, 2022.
  • [18] D. Połap and M. Woźniak, “Red fox optimization algorithm,” Expert Systems with Applications, vol. 166, 114107, 2021.
  • [19] M. Dehghani, Š. Hubálovský, and P. Trojovský, “Tasmanian devil optimization: a new bio-inspired optimization algorithm for solving optimization algorithm,” IEEE Access, vol. 10, 19599-19620, 2022.
  • [20] T. S. Ayyarao, N. S. S. Ramakrishna, R. M. Elavarasan, N. Polumahanthi, M. Rambabu, G. Saini, B. Khan, and B. Alatas, “War strategy optimization algorithm: a new effective metaheuristic algorithm for global optimization,” IEEE Access, vol. 10, pp. 25073-25105, 2022.
  • [21] I. Naruei and F. Keynia, “Wild horse optimizer: A new meta-heuristic algorithm for solving engineering optimization problems,” Engineering with Computers, vol. 38, pp. 3025-3056, 2022.
  • [22] M. Dehghani, Z. Montazeri, E. Trojovská, and P. Trojovský, “Coati Optimization Algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems,” Knowledge-Based Systems, vol. 259, 110011, 2023.
  • [23] M. Abdel-Basset, R. Mohamed, M. Jameel, and M. Abouhawwash, “Nutcracker optimizer: A novel nature-inspired metaheuristic algorithm for global optimization and engineering design problems,” Knowledge-Based Systems, vol. 262, 110248, 2023.
  • [24] S. Xian and X. Feng, “Meerkat optimization algorithm: A new meta-heuristic optimization algorithm for solving constrained engineering problems,” Expert Systems with Applications, vol. 231, 120482, 2023.
  • [25] M. Azizi, U. Aickelin, H. A. Khorshidi, and M. B. Shishehgarkhane, “Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization,” Scientific Reports, vol. 13, no. 1, 2023.
  • [26] Q. Zhang, H. Gao, Z. H. Zhan, J. Li, and H. Zhang, “Growth Optimizer: A powerful metaheuristic algorithm for solving continuous and discrete global optimization problems,” Knowledge-Based Systems, vol. 261, 110206, 2023.
  • [27] J. O. Agushaka, A. E. Ezugwu, and L. Abualigah, “Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer,” Neural Computing and Applications, vol. 35, no. 5, pp. 4099-4131, 2023.
  • [28] M. Kaveh, M. S. Mesgari, and B. Saeidian, “Orchard Algorithm (OA): A new meta-heuristic algorithm for solving discrete and continuous optimization problems,” Mathematics and Computers in Simulation, vol. 208, pp. 95-135, 2023.
  • [29] M. Han, Z. Du, K. Yuen, H. Zhu, Y. Li, and Q. Yuan, “Walrus Optimizer: A novel nature-inspired metaheuristic algorithm,” Expert Systems with Applications, vol. 239, 122413, 2023.
  • [30] D. Zhu, S. Wang, C. Zhou, S. Yan, and J. Xue “Human memory optimization algorithm: A memory-inspired optimizer for global optimization problems,” Expert Systems with Applications, vol. 237, 121597, 2024.
  • [31] E. S. M. El-kenawy, N. Khodadadi, S. Mirjalili, A. A. Abdelhamid, M. M. Eid, and A. Ibrahim, “Greylag Goose Optimization: Nature-inspired optimization algorithm,” Expert Systems with Applications, vol. 238, 122147, 2023.
  • [32] S. B. Aydemir, “A novel arithmetic optimization algorithm based on chaotic maps for global optimization,” Evolutionary Intelligence, vol. 16, no. 3, pp. 981-996, 2023.
  • [33] S. Ekinci, D. Izci, R. A. Zitar, A. R. Alsoud, and L. Abualigah, “Development of Lévy flight-based reptile search algorithm with local search ability for power systems engineering design problems,” Neural Computing and Applications, vol. 34, no. 22, pp. 20263-20283, 2022.
  • [34] H. Bakir, U. Guvenc, H. T. Kahraman, and S. Duman, “Improved Lévy flight distribution algorithm with FDB-based guiding mechanism for AVR system optimal design,” Computers & Industrial Engineering, vol. 168, 108032, 2022.
  • [35] C. Zhong, G. Li, Z. Meng, and W. He, “Opposition-based learning equilibrium optimizer with Levy flight and evolutionary population dynamics for high-dimensional global optimization problems,” Expert Systems with Applications, vol. 215, 119303, 2023.
  • [36] H. Bakır, S. Duman, U. Guvenc, and H. T. Kahraman, “Improved adaptive gaining-sharing knowledge algorithm with FDB-based guiding mechanism for optimization of optimal reactive power flow problem,” Electrical Engineering, vol. 105, no. 5, pp. 3121-3160, 2023.
  • [37] X. Chen, B. Xu, C. Mei, Y. Ding, and K. Li, “Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation,” Applied Energy, vol. 212, pp. 1578-1588, 2018.
  • [38] H. Salimi, “Stochastic fractal search: a powerful metaheuristic algorithm,” Knowledge-Based Systems, vol. 75, pp. 1-18, 2015.
  • [39] A. W. Mohamed, A. A. Hadi, A. M. Fattouh, and K. M. Jambi, “LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems,” In 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 145-152. IEEE, 2017.
  • [40] A. Kumar, K. V. Price, A. W. Mohamed, A. A. Hadi, and P. N. Suganthan, “Problem Definitions and Evaluation Criteria for the 2022 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization Nanyang Technological University,” Tech. Rep, 2022.
  • [41] L. Wang, Q. Cao, Z. Zhang, S. Mirjalili, and W. Zhao, “Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems,” Engineering Applications of Artificial Intelligence, vol. 114, 105082, 2022.
  • [42] S. García, A. Fernández, J. Luengo, and F. Herrera, “Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power,” Information Sciences, vol. 180, no. 10, pp. 2044-2064, 2010.
  • [43] J. Derrac, S. García, D. Molina, and F. Herrera, “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms,” Swarm and Evolutionary Computation, vol. 1, no. 1, pp. 3-18, 2011.
  • [44] H. Su, D. Zhao, A. A. Heidari, L. Liu, X. Zhang, M. Mafarja, and H. Chen, “RIME: A physics-based optimization”. Neurocomputing, vol. 532, pp. 183-214, 2023.
  • [45] A. S. Sadiq, A. A. Dehkordi, S. Mirjalili, and Q. V. Pham, “Nonlinear marine predator algorithm: A cost-effective optimizer for fair power allocation in NOMA-VLC-B5G networks,” Expert Systems with Applications, vol. 203, 117395, 2022.
  • [46] M. Dehghani, Š. Hubálovský, and P. Trojovský, “Northern goshawk optimization: a new swarm-based algorithm for solving optimization problems,” IEEE Access, vol. 9, pp. 162059-162080, 2021.
  • [47] M. Abdel-Basset, R. Mohamed, S. A. A. Azeem, M. Jameel, and M. Abouhawwash, “Kepler optimization algorithm: A new metaheuristic algorithm inspired by Kepler’s laws of planetary motion,” Knowledge-Based Systems, vol. 268, 110454, 2023.
  • [48] B. Abdollahzadeh, F. S. Gharehchopogh, and S. Mirjalili “Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems,” International Journal of Intelligent Systems, vol. 36, no. 10, pp. 5887-5958, 2021.
  • [49] M. Abdel-Basset, D. El-Shahat, M. Jameel, and M. Abouhawwash, “Exponential distribution optimizer (EDO): a novel math-inspired algorithm for global optimization and engineering problems,” Artificial Intelligence Review, vol. 56, no. 9, pp. 9329-9400, 2023.
  • [50] Y. Yang, H. Chen, A. A. Heidari, and A. H. Gandomi, “Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts,” Expert Systems with Applications, vol. 177, 114864, 2021.

Year 2024, Volume: 7 Issue: 2, 227 - 243, 31.08.2024

Hüseyin Bakır

https://doi.org/10.35377/saucis...1474767

Abstract

References

  • [1] L. Abualigah, D. Yousri, M. Abd Elaziz, A. A. Ewees, M. A. Al-Qaness, and A. H. Gandomi, “Aquila optimizer: a novel meta-heuristic optimization algorithm,” Computers & Industrial Engineering, vol. 157, 107250, 2021.
  • [2] O. Altay, “Chaotic slime mould optimization algorithm for global optimization,” Artificial Intelligence Review, vol. 55, no. 5, pp. 3979-4040, 2022.
  • [3] P. Civicioglu and E. Besdok, “Bezier Search Differential evolution algorithm for numerical function optimization: A comparative study with CRMLSP, MVO, WA, SHADE and LSHADE,” Expert Systems with Applications, vol. 165, 113875, 2021.
  • [4] A. Özkış and A. Babalık, “Solving constrained engineering design problems with multi-objective artificial algae algorithm,” Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 29, no. 2, pp. 183-193, 2023.
  • [5] F. A. Hashim, E. H. Houssein, K. Hussain, M. S. Mabrouk, and W. Al-Atabany, “Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems,” Mathematics and Computers in Simulation, vol. 192, pp. 84-110, 2022.
  • [6] H. T. Kahraman, M. Katı, S. Aras, and D. A. Taşci, “Development of the Natural Survivor Method (NSM) for designing an updating mechanism in metaheuristic search algorithms,” Engineering Applications of Artificial Intelligence, vol. 122, 106121, 2023.
  • [7] V. Hayyolalam and A. A. P. Kazem, “Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems,” Engineering Applications of Artificial Intelligence, vol. 87, 103249, 2020.
  • [8] K. Zervoudakis and S. Tsafarakis, “A mayfly optimization algorithm,” Computers & Industrial Engineering, vol. 145, 106559, 2020.
  • [9] Y. Zhang and Z. Jin, “Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems,” Expert Systems with Applications, vol. 148, 113246, 2020.
  • [10] I. Ahmadianfar, O. Bozorg-Haddad, and X. Chu, “Gradient-based optimizer: A new metaheuristic optimization algorithm,” Information Sciences, vol. 540, pp. 131-159, 2020.
  • [11] M. H. Qais, H. M. Hasanien, and S. Alghuwainem, “Transient search optimization: a new meta-heuristic optimization algorithm,” Applied Intelligence, vol. 50, 3926-3941, 2020.
  • [12] M. S. Braik, “Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems,” Expert Systems with Applications, vol. 174, 114685, 2021.
  • [13] F. MiarNaeimi, G. Azizyan, and M. Rashki, “Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems,” Knowledge-Based Systems, vol. 213, 106711, 2021.
  • [14] M. Braik, A. Sheta, and H. Al-Hiary, “A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm,” Neural Computing and Applications, vol. 33, no.7, pp. 2515-2547, 2021.
  • [15] F. A. Hashim, K. Hussain, E. H. Houssein, M. S. Mabrouk, and W. Al-Atabany, “Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems,” Applied Intelligence, vol. 51, pp. 1531-1551, 2021.
  • [16] J. O.Agushaka, A. E. Ezugwu, and L. Abualigah, “Dwarf mongoose optimization algorithm”. Computer Methods in Applied Mechanics and Engineering, vol. 391, 114570, 2022.
  • [17] J. S. Pan, L. G. Zhang, R. B. Wang, V. Snášel, and S. C. Chu, “Gannet optimization algorithm: A new metaheuristic algorithm for solving engineering optimization problems,” Mathematics and Computers in Simulation, vol. 202, pp. 343-373, 2022.
  • [18] D. Połap and M. Woźniak, “Red fox optimization algorithm,” Expert Systems with Applications, vol. 166, 114107, 2021.
  • [19] M. Dehghani, Š. Hubálovský, and P. Trojovský, “Tasmanian devil optimization: a new bio-inspired optimization algorithm for solving optimization algorithm,” IEEE Access, vol. 10, 19599-19620, 2022.
  • [20] T. S. Ayyarao, N. S. S. Ramakrishna, R. M. Elavarasan, N. Polumahanthi, M. Rambabu, G. Saini, B. Khan, and B. Alatas, “War strategy optimization algorithm: a new effective metaheuristic algorithm for global optimization,” IEEE Access, vol. 10, pp. 25073-25105, 2022.
  • [21] I. Naruei and F. Keynia, “Wild horse optimizer: A new meta-heuristic algorithm for solving engineering optimization problems,” Engineering with Computers, vol. 38, pp. 3025-3056, 2022.
  • [22] M. Dehghani, Z. Montazeri, E. Trojovská, and P. Trojovský, “Coati Optimization Algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems,” Knowledge-Based Systems, vol. 259, 110011, 2023.
  • [23] M. Abdel-Basset, R. Mohamed, M. Jameel, and M. Abouhawwash, “Nutcracker optimizer: A novel nature-inspired metaheuristic algorithm for global optimization and engineering design problems,” Knowledge-Based Systems, vol. 262, 110248, 2023.
  • [24] S. Xian and X. Feng, “Meerkat optimization algorithm: A new meta-heuristic optimization algorithm for solving constrained engineering problems,” Expert Systems with Applications, vol. 231, 120482, 2023.
  • [25] M. Azizi, U. Aickelin, H. A. Khorshidi, and M. B. Shishehgarkhane, “Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization,” Scientific Reports, vol. 13, no. 1, 2023.
  • [26] Q. Zhang, H. Gao, Z. H. Zhan, J. Li, and H. Zhang, “Growth Optimizer: A powerful metaheuristic algorithm for solving continuous and discrete global optimization problems,” Knowledge-Based Systems, vol. 261, 110206, 2023.
  • [27] J. O. Agushaka, A. E. Ezugwu, and L. Abualigah, “Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer,” Neural Computing and Applications, vol. 35, no. 5, pp. 4099-4131, 2023.
  • [28] M. Kaveh, M. S. Mesgari, and B. Saeidian, “Orchard Algorithm (OA): A new meta-heuristic algorithm for solving discrete and continuous optimization problems,” Mathematics and Computers in Simulation, vol. 208, pp. 95-135, 2023.
  • [29] M. Han, Z. Du, K. Yuen, H. Zhu, Y. Li, and Q. Yuan, “Walrus Optimizer: A novel nature-inspired metaheuristic algorithm,” Expert Systems with Applications, vol. 239, 122413, 2023.
  • [30] D. Zhu, S. Wang, C. Zhou, S. Yan, and J. Xue “Human memory optimization algorithm: A memory-inspired optimizer for global optimization problems,” Expert Systems with Applications, vol. 237, 121597, 2024.
  • [31] E. S. M. El-kenawy, N. Khodadadi, S. Mirjalili, A. A. Abdelhamid, M. M. Eid, and A. Ibrahim, “Greylag Goose Optimization: Nature-inspired optimization algorithm,” Expert Systems with Applications, vol. 238, 122147, 2023.
  • [32] S. B. Aydemir, “A novel arithmetic optimization algorithm based on chaotic maps for global optimization,” Evolutionary Intelligence, vol. 16, no. 3, pp. 981-996, 2023.
  • [33] S. Ekinci, D. Izci, R. A. Zitar, A. R. Alsoud, and L. Abualigah, “Development of Lévy flight-based reptile search algorithm with local search ability for power systems engineering design problems,” Neural Computing and Applications, vol. 34, no. 22, pp. 20263-20283, 2022.
  • [34] H. Bakir, U. Guvenc, H. T. Kahraman, and S. Duman, “Improved Lévy flight distribution algorithm with FDB-based guiding mechanism for AVR system optimal design,” Computers & Industrial Engineering, vol. 168, 108032, 2022.
  • [35] C. Zhong, G. Li, Z. Meng, and W. He, “Opposition-based learning equilibrium optimizer with Levy flight and evolutionary population dynamics for high-dimensional global optimization problems,” Expert Systems with Applications, vol. 215, 119303, 2023.
  • [36] H. Bakır, S. Duman, U. Guvenc, and H. T. Kahraman, “Improved adaptive gaining-sharing knowledge algorithm with FDB-based guiding mechanism for optimization of optimal reactive power flow problem,” Electrical Engineering, vol. 105, no. 5, pp. 3121-3160, 2023.
  • [37] X. Chen, B. Xu, C. Mei, Y. Ding, and K. Li, “Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation,” Applied Energy, vol. 212, pp. 1578-1588, 2018.
  • [38] H. Salimi, “Stochastic fractal search: a powerful metaheuristic algorithm,” Knowledge-Based Systems, vol. 75, pp. 1-18, 2015.
  • [39] A. W. Mohamed, A. A. Hadi, A. M. Fattouh, and K. M. Jambi, “LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems,” In 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 145-152. IEEE, 2017.
  • [40] A. Kumar, K. V. Price, A. W. Mohamed, A. A. Hadi, and P. N. Suganthan, “Problem Definitions and Evaluation Criteria for the 2022 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization Nanyang Technological University,” Tech. Rep, 2022.
  • [41] L. Wang, Q. Cao, Z. Zhang, S. Mirjalili, and W. Zhao, “Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems,” Engineering Applications of Artificial Intelligence, vol. 114, 105082, 2022.
  • [42] S. García, A. Fernández, J. Luengo, and F. Herrera, “Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power,” Information Sciences, vol. 180, no. 10, pp. 2044-2064, 2010.
  • [43] J. Derrac, S. García, D. Molina, and F. Herrera, “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms,” Swarm and Evolutionary Computation, vol. 1, no. 1, pp. 3-18, 2011.
  • [44] H. Su, D. Zhao, A. A. Heidari, L. Liu, X. Zhang, M. Mafarja, and H. Chen, “RIME: A physics-based optimization”. Neurocomputing, vol. 532, pp. 183-214, 2023.
  • [45] A. S. Sadiq, A. A. Dehkordi, S. Mirjalili, and Q. V. Pham, “Nonlinear marine predator algorithm: A cost-effective optimizer for fair power allocation in NOMA-VLC-B5G networks,” Expert Systems with Applications, vol. 203, 117395, 2022.
  • [46] M. Dehghani, Š. Hubálovský, and P. Trojovský, “Northern goshawk optimization: a new swarm-based algorithm for solving optimization problems,” IEEE Access, vol. 9, pp. 162059-162080, 2021.
  • [47] M. Abdel-Basset, R. Mohamed, S. A. A. Azeem, M. Jameel, and M. Abouhawwash, “Kepler optimization algorithm: A new metaheuristic algorithm inspired by Kepler’s laws of planetary motion,” Knowledge-Based Systems, vol. 268, 110454, 2023.
  • [48] B. Abdollahzadeh, F. S. Gharehchopogh, and S. Mirjalili “Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems,” International Journal of Intelligent Systems, vol. 36, no. 10, pp. 5887-5958, 2021.
  • [49] M. Abdel-Basset, D. El-Shahat, M. Jameel, and M. Abouhawwash, “Exponential distribution optimizer (EDO): a novel math-inspired algorithm for global optimization and engineering problems,” Artificial Intelligence Review, vol. 56, no. 9, pp. 9329-9400, 2023.
  • [50] Y. Yang, H. Chen, A. A. Heidari, and A. H. Gandomi, “Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts,” Expert Systems with Applications, vol. 177, 114864, 2021.
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Details

Primary Language English
Subjects Empirical Software Engineering
Journal Section Articles
Authors

Hüseyin Bakır 0000-0001-5473-5158

Early Pub Date August 26, 2024
Publication Date August 31, 2024
Submission Date April 28, 2024
Acceptance Date May 29, 2024
Published in Issue Year 2024Volume: 7 Issue: 2

Cite

IEEE H. Bakır, “Performance Assessment of Natural Survivor Method-Based Metaheuristic Optimizers in Global Optimization and Engineering Design Problems”, SAUCIS, vol. 7, no. 2, pp. 227–243, 2024, doi: 10.35377/saucis...1474767.
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