The reptile search algorithm (RSA) is a bionic algorithm proposed by Abualigah. et al. in 2020. RSA simulates the whole process of crocodiles encircling and catching prey. Specifically, the encircling stage includes high walking and belly walking, and the hunting stage includes hunting coordination and cooperation. However, in the middle and later stages of the iteration, most search agents will move towards the optimal solution. However, if the optimal solution falls into local optimum, the population will fall into stagnation. Therefore, RSA cannot converge when solving complex problems. To enable RSA to solve more problems, this paper proposes a multi-hunting coordination strategy by combining Lagrange interpolation and teaching-learning-based optimization (TLBO) algorithm's student stage. Multi-hunting cooperation strategy will make multiple search agents coordinate with each other. Compared with the hunting cooperation strategy in the original RSA, the multi-hunting cooperation strategy has been greatly improved RSA's global capability. Moreover, considering RSA's weak ability to jump out of the local optimum in the middle and later stages, this paper adds the Lens pposition-based learning (LOBL) and restart strategy. Based on the above strategy, a modified reptile search algorithm with a multi-hunting coordination strategy (MRSA) is proposed. To verify the above strategies' effectiveness for RSA, 23 benchmark and CEC2020 functions were used to test MRSA's performance. In addition, MRSA's solutions to six engineering problems reflected MRSA's engineering applicability. It can be seen from the experiment that MRSA has better performance in solving test functions and engineering problems.
Citation: Di Wu, Changsheng Wen, Honghua Rao, Heming Jia, Qingxin Liu, Laith Abualigah. Modified reptile search algorithm with multi-hunting coordination strategy for global optimization problems[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10090-10134. doi: 10.3934/mbe.2023443
The reptile search algorithm (RSA) is a bionic algorithm proposed by Abualigah. et al. in 2020. RSA simulates the whole process of crocodiles encircling and catching prey. Specifically, the encircling stage includes high walking and belly walking, and the hunting stage includes hunting coordination and cooperation. However, in the middle and later stages of the iteration, most search agents will move towards the optimal solution. However, if the optimal solution falls into local optimum, the population will fall into stagnation. Therefore, RSA cannot converge when solving complex problems. To enable RSA to solve more problems, this paper proposes a multi-hunting coordination strategy by combining Lagrange interpolation and teaching-learning-based optimization (TLBO) algorithm's student stage. Multi-hunting cooperation strategy will make multiple search agents coordinate with each other. Compared with the hunting cooperation strategy in the original RSA, the multi-hunting cooperation strategy has been greatly improved RSA's global capability. Moreover, considering RSA's weak ability to jump out of the local optimum in the middle and later stages, this paper adds the Lens pposition-based learning (LOBL) and restart strategy. Based on the above strategy, a modified reptile search algorithm with a multi-hunting coordination strategy (MRSA) is proposed. To verify the above strategies' effectiveness for RSA, 23 benchmark and CEC2020 functions were used to test MRSA's performance. In addition, MRSA's solutions to six engineering problems reflected MRSA's engineering applicability. It can be seen from the experiment that MRSA has better performance in solving test functions and engineering problems.
| [1] |
P. Toth, D. Vigo, An exact algorithm for the vehicle routing problem with backhauls, Transport. Sci., 31 (1997), 372–385. https://doi.org/10.1287/trsc.31.4.372 doi: 10.1287/trsc.31.4.372
|
| [2] |
J. R. Cash, A. H. Karp, A variable order Runge-Kutta method for initial value problems with rapidly varying right-hand sides, ACM T. Math. Software, 16 (1990), 201–222. https://doi.org/10.1145/79505.79507 doi: 10.1145/79505.79507
|
| [3] | Z. Beheshti, S. M. H. Shamsuddin, A review of population-based meta-heuristic algorithms, Int. J. Adv. Soft Comput. Appl., 5 (2013), 1–35. |
| [4] |
A. Dahou, M. A. Elaziz, S. A. Chelloug, M. A. Awadallah, M. A. Al-Betar, M. A. A. Al-qaness, et al., Intrusion detection system for iot based on deep learning and modified reptile search algorithm, Comput. Intell. Neurosc., 2022 (2022). https://doi.org/10.1155/2022/6473507 doi: 10.1155/2022/6473507
|
| [5] |
F. Agostino, Heuristic recommendation technique in internet of things featuring swarm intelligence approach, Expert Syst. Appl., 187 (2022), 115904. https://doi.org/10.1016/j.eswa.2021.115904 doi: 10.1016/j.eswa.2021.115904
|
| [6] |
F. Agostino, C. Mastroianni, G. Spezzano, Reorganization and discovery of grid information with epidemic tuning, Future Gener. Comp. Syst., 24 (2008), 788–797. https://doi.org/10.1016/j.future.2008.04.001 doi: 10.1016/j.future.2008.04.001
|
| [7] |
T. Fearn, Particle swarm optimization, NIR News, 25 (2014), 27. https://doi.org/10.1255/nirn.1476 doi: 10.1255/nirn.1476
|
| [8] |
S. Mirjalili, S. M. Mirjalili, A. Lewis, Grey wolf optimizer, Adv. Eng. Softw., 69 (2014), 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007 doi: 10.1016/j.advengsoft.2013.12.007
|
| [9] |
G. G. Wang, S. Deb, Z. Cui, Monarch butterfly optimization, Neural Comput. Appl., 31 (2019), 1995–2014. https://doi.org/10.1007/s00521-015-1923-y doi: 10.1007/s00521-015-1923-y
|
| [10] |
G. G. Wang, Moth search algorithm: A bio–inspired metaheuristic algorithm for global optimization problems, Memet. Comput., 10 (2018), 151–164. https://doi.org/10.1007/s12293-016-0212-3 doi: 10.1007/s12293-016-0212-3
|
| [11] |
Y. Yang, H. Chen, A. A. Heidari, A. H Gandomi, Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts, Expert Syst. Appl., 177 (2021), 114864. https://doi.org/10.1016/j.eswa.2021.114864 doi: 10.1016/j.eswa.2021.114864
|
| [12] |
J. Tu, H. Chen, M. Wang, A. H Gandomi, The colony predation algorithm, J. Bionic Eng., 18 (2021), 674–710. https://doi.org/10.1007/s42235-021-0050-y doi: 10.1007/s42235-021-0050-y
|
| [13] |
J. H. Holland, Genetic algorithms, Sci. Am., 267 (1992), 66–73. https://doi.org/10.1038/scientificamerican0792-66 doi: 10.1038/scientificamerican0792-66
|
| [14] |
A. K. Qin, V. L. Huang, P. N. Suganthan, Differential evolution algorithm with strategy adaptation for global numerical optimization, IEEE T. Evolut. Comput., 13 (2008), 398–417. https://doi.org/10.1109/TEVC.2008.927706 doi: 10.1109/TEVC.2008.927706
|
| [15] |
E. Rashedi, H. Nezamabadi–Pour, S. Saryazdi, GSA: A gravitational search algorithm, Inf. Sci., 179 (2009), 2232–2248. https://doi.org/10.1016/j.ins.2009.03.004 doi: 10.1016/j.ins.2009.03.004
|
| [16] |
S. Mirjalili, SCA: A sine cosine algorithm for solving optimization problems, Knowl. Based Syst., 96 (2016), 120–133. https://doi.org/10.1016/j.knosys.2015.12.022 doi: 10.1016/j.knosys.2015.12.022
|
| [17] |
L. Abualigah, A. Diabat, S. Mirjalili, M. A. Elaziz, A. H. Gandomi, The arithmetic optimization algorithm, Comput. Methods Appl. Mech. Eng., 376 (2021), 113609. https://doi.org/10.1016/j.cma.2020.113609 doi: 10.1016/j.cma.2020.113609
|
| [18] |
I. Ahmadianfar, A. A. Heidari, S. Noshadian, A. H Gandomi, INFO: An efficient optimization algorithm based on weighted mean of vectors, Expert Syst. Appl., 195 (2022), 116516. https://doi.org/10.1016/j.eswa.2022.116516 doi: 10.1016/j.eswa.2022.116516
|
| [19] |
Z. Z. Liu, D. H. Chu, C. Song, X. Xue, B. Y. Lu, Social learning optimization (SLO) algorithm paradigm and its application in QoS–aware cloud service composition, Inform. Sci., 326 (2016), 315–333. https://doi.org/10.1016/j.ins.2015.08.004 doi: 10.1016/j.ins.2015.08.004
|
| [20] |
Y. Zhang, Z. Jin, Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems, Expert Syst. Appl., 148 (2020), 113246. https://doi.org/10.1016/j.eswa.2020.113246 doi: 10.1016/j.eswa.2020.113246
|
| [21] |
D. H. Wolpert, W. G. Macready, No free lunch theorems for optimization, IEEE Trans. Evol. Comput., 1 (1997), 67–82. https://doi.org/10.1109/4235.585893 doi: 10.1109/4235.585893
|
| [22] |
S. Gupta, K. Deep, A hybrid self-adaptive sine cosine algorithm with opposition based learning, Expert Syst. Appl., 119 (2019), 210–230. https://doi.org/10.1016/j.eswa.2018.10.050 doi: 10.1016/j.eswa.2018.10.050
|
| [23] |
E. H. Houssein, B. E. Helmy, H. Rezk, A. M. Nassef, An enhanced Archimedes optimization algorithm based on local escaping operator and orthogonal learning for PEM fuel cell parameter identification, Eng. Appl. Artif. Intell., 103 (2021), 104309. https://doi.org/10.1016/j.engappai.2021.104309 doi: 10.1016/j.engappai.2021.104309
|
| [24] |
H. Dong, J. He, H. Huang, W. Hou, Evolutionary programming using a mixed mutation strategy, Inform. Sci., 1 (2007), 312–327. https://doi.org/10.1016/j.ins.2006.07.014 doi: 10.1016/j.ins.2006.07.014
|
| [25] | Z. M. Gao, J Zhao, Y. J. Zhang, Review of chaotic mapping enabled nature-inspired algorithms, Math. Biosci. Eng., 19 (2022), 8215–8258. |
| [26] | Y. J. Zhang, J. Zhao, Z. M. Gao, Hybridized improvement of the chaotic Harris Hawk optimization algorithm and Aquila optimizer, in International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 12172 (2022), 327–332. https://doi.org/10.1117/12.2634395 |
| [27] |
Y. J. Zhang, Y. X. Yan, J. Zhao, Z. M. Gao, AOAAO: The hybrid algorithm of arithmetic optimization algorithm with aquila optimizer, IEEE Access, 10 (2022), 10907–10933. https://doi.org/10.1109/ACCESS.2022.3144431 doi: 10.1109/ACCESS.2022.3144431
|
| [28] |
Y. J. Zhang, Y. X. Yan, J. Zhao, Z. M. Gao, CSCAHHO: Chaotic hybridization algorithm of the Sine Cosine with Harris Hawk optimization algorithms for solving global optimization problems, Plos one, 17 (2022), e0263387. https://doi.org/10.1371/journal.pone.0263387 doi: 10.1371/journal.pone.0263387
|
| [29] |
J. Zhao, Z. M. Gao, Y. J. Zhang, Piecewise linear map enabled Harris Hawk optimization algorithm, J. Phys. Conf. Ser., 1994 (2021), 012038. https://doi.org/10.1088/1742-6596/1994/1/012038 doi: 10.1088/1742-6596/1994/1/012038
|
| [30] |
M. M. Emam, E. H. Houssein, R. M. Ghoniem, A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images, Comput. Biol. Med., 152 (2023), 106404. https://doi.org/10.1016/j.compbiomed.2022.106404 doi: 10.1016/j.compbiomed.2022.106404
|
| [31] |
S. Chakraborty, A K Saha, S Nama, S. Debnath, COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction, Comput. Biol. Med., 139 (2021), 104984. https://doi.org/10.1016/j.compbiomed.2021.104984 doi: 10.1016/j.compbiomed.2021.104984
|
| [32] |
G. I. Sayed, M. M. Soliman, A. E. Hassanien, A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization, Comput. Biol. Med., 136 (2021), 104712. https://doi.org/10.1016/j.compbiomed.2021.104712 doi: 10.1016/j.compbiomed.2021.104712
|
| [33] |
J. Piri, P. Mohapatra, An analytical study of modified multi-objective Harris Hawk Optimizer towards medical data feature selection, Comput. Biol. Med., 135 (2021), 104558. https://doi.org/10.1016/j.compbiomed.2021.104558 doi: 10.1016/j.compbiomed.2021.104558
|
| [34] |
L. Abualigah, M. A. Elaziz, P. Sumari, Z. W. Geem, A. H. Gandomi, Reptile search algorithm (RSA): A nature-inspired meta-heuristic optimizer, Expert Syst. Appl., 191(2021), 116158. https://doi.org/10.1016/j.eswa.2021.116158 doi: 10.1016/j.eswa.2021.116158
|
| [35] |
K. H. Almotairi, L. Abualigah, Hybrid reptile search algorithm and remora optimization algorithm for optimization tasks and data clustering, Symmetry, 14 (2022), 458. https://doi.org/10.3390/sym14030458 doi: 10.3390/sym14030458
|
| [36] |
L. Huang, Y. Wang, Y. Guo, G. Hu, An improved reptile search algorithm based on lévy flight and interactive crossover strategy to engineering application, Mathematics, 10 (2022), 2329. https://doi.org/10.3390/math10132329 doi: 10.3390/math10132329
|
| [37] |
T. Sauer, Y. Xu, On multivariate Lagrange interpolation, Math. Comput., 64 (1995), 1147–1170. https://doi.org/10.1090/S0025-5718-1995-1297477-5 doi: 10.1090/S0025-5718-1995-1297477-5
|
| [38] |
R. V. Rao, V. J. Savsani, D. P. Vakharia, Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems, Comput. Aided Design., 43 (2011), 303–315. https://doi.org/10.1016/j.cad.2010.12.015 doi: 10.1016/j.cad.2010.12.015
|
| [39] |
Q. Liu, N. Li, H. Jia, Q. Qi, L. Abualigah, Modified remora optimization algorithm for global optimization and multilevel thresholding image segmentation, Mathematics, 10 (2022), 1014. https://doi.org/10.3390/math10071014 doi: 10.3390/math10071014
|
| [40] |
H. Zhang, Z. Wang, W. Chen, A. A. Heidari, M. Wang, X. Zhao, et al., Ensemble mutation-driven salp swarm algorithm with restart mechanism: Framework and fundamental analysis, Expert Syst. Appl., 165 (2021), 113897. https://doi.org/10.1016/j.eswa.2020.113897 doi: 10.1016/j.eswa.2020.113897
|
| [41] |
H. Rao, H. Jia, D. Wu, C. Wen, S. Li, Q. Liu, L. Abualigah, A modified group teaching optimization algorithm for solving constrained engineering optimization problems, Mathematics, 10 (2022), 3765. https://doi.org/10.3390/math10203765 doi: 10.3390/math10203765
|
| [42] |
Z. Tongyi, W. Luo, An enhanced lightning attachment procedure optimization with quasi–opposition-based learning and dimensional search strategies, Comput. Intell. Neurosc., 2019 (2019). https://doi.org/10.1155/2019/1589303 doi: 10.1155/2019/1589303
|
| [43] |
F. Y. Arini, S. Chiewchanwattana, C. Soomlek, K. Sunat, Joint opposite selection (JOS): A premiere joint of selective leading opposition and dynamic opposite enhanced Harris' hawks optimization for solving single–objective problems, Expert Syst. Appl., 188 (2022), 116001. https://doi.org/10.1016/j.eswa.2021.116001 doi: 10.1016/j.eswa.2021.116001
|
| [44] |
H. Jia, X. Peng, C. Lang, Remora optimization algorithm, Expert Syst. Appl., 185 (2021), 115665. https://doi.org/10.1016/j.eswa.2021.115665 doi: 10.1016/j.eswa.2021.115665
|
| [45] |
H. A. Alsattar, A. A. Zaidan, B. B. Zaidan, Novel meta-heuristic bald eagle search optimisation algorithm, Artif. Intell. Rev., 53 (2020), 2237–2264. https://doi.org/10.1007/s10462-019-09732-5 doi: 10.1007/s10462-019-09732-5
|
| [46] |
F. MiarNaeimi, G. Azizyan, M. Rashki, Horse herd optimization algorithm: A nature-inspired algorithm for high–dimensional optimization problems, Knowl. Based Syst., 213 (2021), 106711. https://doi.org/10.1016/j.knosys.2020.106711 doi: 10.1016/j.knosys.2020.106711
|
| [47] |
A. Seyyedabbasi, F. Kiani, Sand cat swarm optimization: A nature-inspired algorithm to solve global optimization problems, Eng. Comput., (2022), 1–25. https://doi.org/10.1007/s00366-022-01604-x doi: 10.1007/s00366-022-01604-x
|
| [48] |
Y. J. Zhang, Y. F. Wang, Y. X. Yan, J. Zhao, Z. M. Gao, LMRAOA: An improved arithmetic optimization algorithm with multi–leader and high-speed jumping based on opposition–based learning solving engineering and numerical problems, Alex. Eng. J., 61 (2022), 12367–12403. https://doi.org/10.1016/j.aej.2022.06.017 doi: 10.1016/j.aej.2022.06.017
|
| [49] |
S. Mirjalili, A. Lewis, The whale optimization algorithm, Adv. Eng. Softw., 95 (2016), 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008 doi: 10.1016/j.advengsoft.2016.01.008
|
| [50] |
A. Kaveh, M. Khayatazad, A new meta-heuristic method: Ray optimization, Comput. Struct., 112 (2012), 283–294. https://doi.org/10.1016/j.compstruc.2012.09.003 doi: 10.1016/j.compstruc.2012.09.003
|
| [51] |
A. Faramarzi, M. Heidarinejad, S. Mirjalili, A. H. Gandomi, Marine Predators Algorithm: A Nature-inspired Metaheuristic, Expert Syst. Appl., 152 (2020), 113377. https://doi.org/10.1016/j.eswa.2020.113377 doi: 10.1016/j.eswa.2020.113377
|
| [52] |
S. Mirjalili, S. M. Mirjalili, A. Hatamlou, Multi-verse optimizer: A nature-inspired algorithm for global optimization, Neural Comput. Appl., 27 (2015), 495–513. https://doi.org/10.1007/s00521-015-1870-7 doi: 10.1007/s00521-015-1870-7
|
| [53] |
A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, Harris hawks optimization: Algorithm and applications, Future Gener. Comp. Syst., 97 (2019), 849–872. https://doi.org/10.1016/j.future.2019.02.028 doi: 10.1016/j.future.2019.02.028
|
| [54] |
M. Mahdavi, M. Fesanghary, E. Damangir, An improved harmony search algorithm for solving optimization problems, Appl. Math. Comput., 188 (2007), 1567–1579. https://doi.org/10.1016/j.amc.2006.11.033 doi: 10.1016/j.amc.2006.11.033
|
| [55] |
G. Dhiman, V. Kumar, Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications, Adv. Eng. Softw., 114 (2017), 48–70. https://doi.org/10.1016/j.advengsoft.2017.05.014 doi: 10.1016/j.advengsoft.2017.05.014
|
| [56] |
S. Li, H. Chen, M. Wangm, A. A. Heidari, S. Mirjalili, Slime mould algorithm: A new method for stochastic optimization, Future Gener. Comput. Syst., 111 (2020), 300–323. https://doi.org/10.1016/j.future.2020.03.055 doi: 10.1016/j.future.2020.03.055
|
| [57] |
Q. He, L. Wang, A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization, Appl. Math. Comput., 186 (2007), 1407–1422. https://doi.org/10.1016/j.amc.2006.07.134 doi: 10.1016/j.amc.2006.07.134
|
| [58] |
A. Kaveh, N. Farhoudi, A new optimization method: Dolphin echolocation, Adv. Eng. Softw., 59 (2013), 53–70. https://doi.org/10.1016/j.advengsoft.2013.03.004 doi: 10.1016/j.advengsoft.2013.03.004
|
| [59] |
I. Naruei, F. Keynia, A new optimization method based on COOT bird natural life mode, Expert Syst. Appl., 183 (2021), 115352. https://doi.org/10.1016/j.eswa.2021.115352 doi: 10.1016/j.eswa.2021.115352
|
| [60] |
W. Zhao, L. Wang, Z. Zhang, Artificial ecosystem-based optimization: A novel nature-inspired meta-heuristic algorithm, Neural Comput. Appl., 32 (2020), 9383–9425. https://doi.org/10.1007/s00521-019-04452-x doi: 10.1007/s00521-019-04452-x
|
| [61] |
A. Kaveh, S. Talatahari, A novel heuristic optimization method: Charged system search, Acta Mech., 213 (2010), 267–289. https://doi.org/10.1007/s00707-009-0270-4 doi: 10.1007/s00707-009-0270-4
|
| [62] |
F. Huang, L. Wang, Q. He, An effective co-evolutionary differential evolution for constrained optimization, Appl. Math. Comput., 186 (2007), 340–356. https://doi.org/10.1016/j.amc.2006.07.105 doi: 10.1016/j.amc.2006.07.105
|
| [63] |
H. Yapici, N. Cetinkaya, A new meta-heuristic optimizer: Pathfinder algorithm, Appl. Soft Comput., 78 (2019), 545–568. https://doi.org/10.1016/j.asoc.2019.03.012 doi: 10.1016/j.asoc.2019.03.012
|
| [64] |
V. K. Kamboj, A. Nandi, A. Bhadoria, S. Sehgal, An intensify harris hawks optimizer for numerical and engineering optimization problems, Appl. Soft Comput., 89 (2020), 106018. https://doi.org/10.1016/j.asoc.2019.106018 doi: 10.1016/j.asoc.2019.106018
|
| [65] |
C. Wen, H. Jia, D. Wu, H. Rao, S. Li, Q. Liu, et al., Modified remora optimization algorithm with multistrategies for global optimization problem, Mathematics, 10 (2022), 3604. https://doi.org/10.3390/math10193604 doi: 10.3390/math10193604
|
| [66] |
J. M. Czerniak, H. Zarzycki, D. Ewald, Aao as a new strategy in modeling and simulation of constructional problems optimization, Simul. Model. Pract. Theory, 76 (2017), 22–33. https://doi.org/10.1016/j.simpat.2017.04.001 doi: 10.1016/j.simpat.2017.04.001
|
| [67] |
N. B. Guedria, Improved accelerated pso algorithm for mechanical engineering optimization problems, Appl. Soft Comput., 40 (2016), 455–467. https://doi.org/10.1016/j.asoc.2015.10.048 doi: 10.1016/j.asoc.2015.10.048
|
| [68] |
A. G. Hussien, M. Amin, M. Aziz, A comprehensive review of moth-flame optimisation: Variants, hybrids, and applications, J. Exp. Theor. Artif. Intell., 32 (2020), 705–725. https://doi.org/10.1080/0952813X.2020.1737246 doi: 10.1080/0952813X.2020.1737246
|
| [69] |
A. Baykasoglu, S. Akpinar, Weighted superposition attraction (wsa): A swarm intelligence algorithm for optimization problems-part2: Constrained optimization, Appl. Soft Comput., 37 (2015), 396–415. https://doi.org/10.1016/j.asoc.2015.08.052 doi: 10.1016/j.asoc.2015.08.052
|
| [70] |
A. H. Gandomi, X. S. Yang, A. H. Alavi, Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems, Eng. Comput., 29 (2013), 17–35. https://doi.org/10.1007/s00366-011-0241-y doi: 10.1007/s00366-011-0241-y
|
| [71] |
A. E. Ezugwu, J. O. Agushaka, L. Abualigah, S. Mirjalili, A. H. Gandomi, Prairie dog optimization algorithm, Neural Comput. Appl., 34 (2022), 20017–20065. https://doi.org/10.1007/s00521-022-07530-9 doi: 10.1007/s00521-022-07530-9
|
| [72] |
J. O. Agushaka, A. E. Ezugwu, L. Abualigah, Dwarf mongoose optimization algorithm, Comput. Methods Appl. Mech. Eng., 391 (2022), 114570. https://doi.org/10.1016/j.cma.2022.114570 doi: 10.1016/j.cma.2022.114570
|
| [73] | M. J. Kazemzadeh-Parsi, A modified firefly algorithm for engineering design optimization problems, IJST-T. Mech. Eng., 38 (2014), 403. |
| [74] |
J. O. Agushaka, A. E. Ezugwu, Advanced arithmetic optimization algorithm for solving mechanical engineering design problems, PLoS ONE, 16 (2021), e0255703. https://doi.org/10.1371/journal.pone.0255703 doi: 10.1371/journal.pone.0255703
|
| [75] |
H. Eskandar, A. Sadollah, A. Bahreininejad, M. Hamdi, Water cycle algorithm-a novel metaheuristic optimization method for solving constrained engineering optimization problems, Comput. Struct., 110 (2012), 151–166. https://doi.org/10.1016/j.compstruc.2012.07.010 doi: 10.1016/j.compstruc.2012.07.010
|
| [76] |
G. I. Sayed, A. Darwish, A. E. Hassanien, A new chaotic multi-verse optimization algorithm for solving engineering optimization problems, J. Exp. Theor. Artif. Intell., 30 (2018), 293–317. https://doi.org/10.1080/0952813X.2018.1430858 doi: 10.1080/0952813X.2018.1430858
|
| [77] |
C. Yu, A. A. Heidari, H. Chen, A quantum–behaved simulated annealing algorithm–based moth–flame optimization method, Appl. Math. Model., 87 (2020), 1–19. https://doi.org/10.1016/j.apm.2020.04.019 doi: 10.1016/j.apm.2020.04.019
|
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Di Wu, Changsheng Wen, Honghua Rao, Heming Jia, Qingxin Liu, Laith Abualigah. Modified reptile search algorithm with multi-hunting coordination strategy for global optimization problems[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10090-10134. doi: 10.3934/mbe.2023443
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