DataVXJournal of Bionic EngineeringPaper
journal article·2024-01

Feedback Mechanism-driven Mutation Reptile Search Algorithm for Optimizing Interpolation Developable Surfaces

Gang Hu, Jiao Wang, Xiaoni Zhu, Muhammad Abbas

Journal of Bionic Engineering· Volume 21· Issue 1
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OpenAlex topics: Advanced Numerical Analysis Techniques · Laser and Thermal Forming Techniques · 3D Shape Modeling and Analysis

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References (78)

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1. Frey, W., & Bindschadler, D. (1993). Computer aided design of a class of developable Bézier surfaces (p. 8057). General Motors R & D Publication.
2. Chu, C. H., Wang, C. C., & Tsai, C. R. (2008). Computer aided geometric design of strip using developable Bézier patches. Computers in Industry, 59(6), 601–611.10.1016/j.compind.2008.03.001 ↗
3. Hu, G., Wu, J. L., Li, H. N., & Hu, X. (2020). Shape optimization of generalized developable H-Bézier surfaces using adaptive cuckoo search algorithm. Advances in Engineering Software, 149, 102889.10.1016/j.advengsoft.2020.102889 ↗
4. Aumann, G. (1991). Interpolation with developable Bézier patches. Computer Aided Geometric Design, 8(5), 409–420.10.1016/0167-8396(91)90014-3 ↗
5. Maekawa, T., & Chalfant, J. S. (1998). Design and tessellation of B-spline developable surfaces. Journal of Mechanical Design, 120(3), 453–461. https://doi.org/10.1115/1.282917310.1115/1.2829173 ↗
6. Aumann, G. (2003). A simple algorithm for designing developable Bézier surfaces. Computer Aided Geometric Design, 20(8), 601–619. https://doi.org/10.1016/j.cagd.2003.07.00110.1016/j.cagd.2003.07.001 ↗
7. Bodduluri, R., & Ravani, B. (1993). Design of developable surfaces using duality between plane and point geometries. Computer-Aided Design, 25(10), 621–632. https://doi.org/10.1016/0010-4485(93)90017-I10.1016/0010-4485(93)90017-I ↗
8. Bodduluri, R., & Ravani, B. (1994). Geometric design and fabrication of developable Bézier and B-spline surfaces. Journal of Mechanical Design, 116(4), 1042–1048. https://doi.org/10.1115/1.291948510.1115/1.2919485 ↗
9. Pottmann, H., & Wallner, J. (1999). Approximation algorithms for developable surfaces. Computer Aided Geometric Design, 16(6), 539–556. https://doi.org/10.1016/S0167-8396(99)00012-610.1016/S0167-8396(99)00012-6 ↗
10. Zhou, M., Yang, J. Q., Zheng, H. C., & Song, W. J. (2013). Design and shape adjustment of developable surfaces. Applied Mathematical Modelling, 37(6), 3789–3801. https://doi.org/10.1016/j.apm.2012.07.05010.1016/j.apm.2012.07.050 ↗
11. Hu, G., Wu, J. L., & Qin, X. Q. (2018). A new approach in designing of local controlled developable H-Bézier surfaces. Advances in Engineering Software, 121, 26–38.10.1016/j.advengsoft.2018.03.003 ↗
12. Hu, G., Zhu, X. N., Wei, G., & Chang, C. T. (2021). An improved marine predators algorithm for shape optimization of developable ball surfaces. Engineering Applications of Artificial Intelligence, 105, 104417. https://doi.org/10.1016/j.engappai.2021.10441710.1016/j.engappai.2021.104417 ↗
13. Maekawa, T., Wolter, F. E., & Patrikalakis, N. M. (1996). Umbilics and lines of curvature for shape interrogation. Computer Aided Geometric Design, 13(2), 133–161. https://doi.org/10.1016/0167-8396(95)00018-610.1016/0167-8396(95)00018-6 ↗
14. Patrikalakis, N. M., & Maekawa, T. (2002). Shape interrogation for computer aided design and manufacturing. Springer.10.1007/978-3-642-04074-0 ↗
15. Zhang, X. P., Che, W. J., & Paul, J. C. (2009). Computing lines of curvature for implicit surfaces. Computer Aided Geometric Design, 26(9), 923–940. https://doi.org/10.1016/j.cagd.2009.07.00410.1016/j.cagd.2009.07.004 ↗
16. Li, C. Y., Wang, R. H., & Zhu, C. G. (2011). Parametric representation of a surface pencil with a common line of curvature. Computer-Aided Design, 43(9), 1110–1117. https://doi.org/10.1016/j.cad.2011.05.00110.1016/j.cad.2011.05.001 ↗
17. Li, C. Y., Wang, R. H., & Zhu, C. G. (2013). An approach for designing a developable surface through a given line of curvature. Computer-Aided Design, 45(3), 621–627. https://doi.org/10.1016/j.cad.2012.11.00110.1016/j.cad.2012.11.001 ↗
18. Hu, G., Wu, J. L., & Wang, X. F. (2021). Constructing local controlled developable H-Bézier surfaces by interpolating characteristic curves. Computational and Applied Mathematic, 40(6), 216. https://doi.org/10.1007/s40314-021-01587-310.1007/s40314-021-01587-3 ↗
19. Terzopoulos, D., Platt, J., Barr, A., & Fleischer, K. (1987). Elastically deformable models. Acm Siggraph Computer Graphics, 21(4), 205–214. https://doi.org/10.1145/37401.3742710.1145/37401.37427 ↗
20. Celniker, G., & Gossard, D. (1991). Deformable curve and surface finite-elements for free-form shape design. Acm Siggraph Computer Graphics, 25(4), 257–266. https://doi.org/10.1145/127719.12274610.1145/127719.122746 ↗
21. Hu, S. M., Li, Y. F., Ju, T., & Zhu, X. (2001). Modifying the shape of NURBS surfaces with geometric constraints. Computer-Aided Design, 33(12), 903–912. https://doi.org/10.1016/S0010-4485(00)00115-910.1016/S0010-4485(00)00115-9 ↗
22. Li, J. C., Yan, L. L., & Liu, C. Z. (2017). Quintic composite spline with adjustable shape and parameter selection. Journal of Image and Graphics, 22(02), 197–204. (in Chinese).
23. Liu, X. (2015). Filling n-sided holes with trimmed b-spline surfaces based on energy-minimization method. Journal of Computing and Information Science in Engineering, 15(1), 011001.10.1115/1.4028632 ↗
24. Wang, M., & Chen, H. (2019). Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Applied Soft Computing, 88, 105946. https://doi.org/10.1016/j.asoc.2019.10594610.1016/j.asoc.2019.105946 ↗
25. Nautiyal, B., Prakash, R., Vimal, V., Liang, G., & Chen, H. (2021). Improved salp swarm algorithm with mutation schemes for solving global optimization and engineering problems. Engineering with Computers, 4, 1–23. https://doi.org/10.1007/s00366-020-01252-z10.1007/s00366-020-01252-z ↗
26. Hu, G., Du, B., Wang, X. F., & Wei, G. (2022). An enhanced black widow optimization algorithm for feature selection. Knowledge-Based Systems, 235, 107638. https://doi.org/10.1016/j.knosys.2021.10763810.1016/j.knosys.2021.107638 ↗
27. Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66–73.10.1038/scientificamerican0792-66 ↗
28. Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248. https://doi.org/10.1016/j.ins.2009.03.00410.1016/j.ins.2009.03.004 ↗
29. Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2015). Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495–513. https://doi.org/10.1007/s00521-015-1870-710.1007/s00521-015-1870-7 ↗
30. Faramarzi, A., Heidarinejad, M., Stephens, B. E., & Mirjalili, S. (2020). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems, 191, 105190.10.1016/j.knosys.2019.105190 ↗
31. Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2012). Teaching-learning-based optimization: An optimization method for continuous non-linear large scale problems. Information Sciences: An International Journal, 183(1), 1–15. https://doi.org/10.1016/j.ins.2011.08.00610.1016/j.ins.2011.08.006 ↗
32. Moghdani, R., & Salimifard, K. (2018). Volleyball premier league algorithm. Applied Soft Computing, 64, 161–185. https://doi.org/10.1016/j.asoc.2017.11.04310.1016/j.asoc.2017.11.043 ↗
33. Eberhart, R., & Kennedy, J. (1995). Particle swarm optimization. Proceedings of The IEEE International Conference on Neural Networks, Perth, Australia, 4(8), 1942–1948.
34. Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Cybernetics, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29–41.10.1109/3477.484436 ↗
35. Yang, X. S., & Gandomi, A. H. (2012). Bat algorithm: A novel approach for global engineering optimization. Engineering Computations: International Journal for Computer-Aided Engineering and Software, 29(5), 464–483.10.1108/02644401211235834 ↗
36. Gandomi, A. H., Yang, X., & Alavi, A. H. (2013). Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), 17–35. https://doi.org/10.1007/s00366-011-0241-y10.1007/s00366-011-0241-y ↗
37. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.00710.1016/j.advengsoft.2013.12.007 ↗
38. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.00810.1016/j.advengsoft.2016.01.008 ↗
39. Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.00210.1016/j.advengsoft.2017.07.002 ↗
40. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872. https://doi.org/10.1016/j.future.2019.02.02810.1016/j.future.2019.02.028 ↗
41. Abualigah, L., Elaziz, M. A., Sumari, P., Geem, Z. W., & Gandomi, A. H. (2022). Reptile search algorithm : A nature-inspired meta-heuristic optimizer. Expert Systems with Applications, 191(11), 116158. https://doi.org/10.1016/j.eswa.2021.11615810.1016/j.eswa.2021.116158 ↗
42. Li, S. M., Chen, H. L., Wang, M. J., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300–323. https://doi.org/10.1016/j.future.2020.03.05510.1016/j.future.2020.03.055 ↗
43. Braik, M. S. (2021). Chameleon swarm algorithm: A bio-inspired optimizer for solving engineering design problems. Expert Systems with Applications, 174(1), 114685. https://doi.org/10.1016/j.eswa.2021.11468510.1016/j.eswa.2021.114685 ↗
44. Naruei, I., & Keynia, F. (2021). Wild horse optimizer: a new meta-heuristic algorithm for solving engineering optimization problems. Engineering with Computers. https://doi.org/10.1007/s00366-021-01438-z10.1007/s00366-021-01438-z ↗
45. Abualigah, L., Yousri, D., Elaziz, M. A., Ewees, A. A., Al-qaness, M. A. A., & Gandomi, A. (2021). Matlab code of aquila optimizer: A novel meta-heuristic optimization algorithm. Computers & Industrial Engineering, 157, 107250. https://doi.org/10.1016/j.cie.2021.10725010.1016/j.cie.2021.107250 ↗
46. Dhiman, G., Garg, M., Nagar, A., Kumar, V., & Dehghani, M. (2021). A novel algorithm for global optimization: rat swarm optimizer. Journal of Ambient Intelligence and Humanized Computing, 12, 8457–8482. https://doi.org/10.1007/s12652-020-02580-010.1007/s12652-020-02580-0 ↗
47. Hu, G., Wang, J., Li, M., Hussien, A. G., & Abbas, M. (2023). EJS: Multi-strategy enhanced jellyfish search algorithm for engineering applications. Mathematics, 11(4), 851. https://doi.org/10.3390/math1104085110.3390/math11040851 ↗
48. Mirjalili, S. (2016). SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120–133. https://doi.org/10.1016/j.knosys.2015.12.02210.1016/j.knosys.2015.12.022 ↗
49. Sulaiman, M. H., Mustaffa, Z., Saari, M. M., & Daniyal, H. (2020). Barnacles mating optimizer: A new bio-inspired algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87, 103330. https://doi.org/10.1016/j.engappai.2019.10333010.1016/j.engappai.2019.103330 ↗
50. Houssein, H. E., Saad, M. R., Hashim, F. A., Shaban, H., & Hassaballah, M. (2020). Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 94, 103731. https://doi.org/10.1016/j.engappai.2020.10373110.1016/j.engappai.2020.103731 ↗
51. Hashim, F. A., Hussain, K., Houssein, E. H., Mai, S. M., & Al-Atabany, W. (2021). Archimedes optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51(3), 1531–1551. https://doi.org/10.1007/s10489-020-01893-z10.1007/s10489-020-01893-z ↗
52. Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020). Tunicate swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541. https://doi.org/10.1016/j.engappai.2020.10354110.1016/j.engappai.2020.103541 ↗
53. Yang, Y. T., Chen, H. L., Heidari, A. A., & Gandomi, A. H. (2021). Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, 114864. https://doi.org/10.1016/j.eswa.2021.11486410.1016/j.eswa.2021.114864 ↗
54. Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S., & Al-Atabany, W. (2022). Honey badger algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation (MATCOM), 192, 84–110. https://doi.org/10.1016/j.matcom.2021.08.01310.1016/j.matcom.2021.08.013 ↗
55. Ezugwu, A. E., Agushaka, J. O., Abualigah, L., Mirjalili, S., & Gandomi, A. H. (2022). Prairie dog optimization algorithm. Neural Computing and Applications, 34(22), 20017–20065. https://doi.org/10.1007/s00521-022-07530-910.1007/s00521-022-07530-9 ↗
56. Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 391, 114570. https://doi.org/10.1016/j.cma.2022.11457010.1016/j.cma.2022.114570 ↗
57. Oyelade, O. N., Ezugwu, A. E. S., Mohamed, T. I. A., & Abualigah, L. (2022). Ebola optimization search algorithm: a new nature-inspired metaheuristic optimization algorithm. IEEE Access, 10, 16150–16177.10.1109/ACCESS.2022.3147821 ↗
58. Shinawi, A. E., Ibrahim, R. A., Abualigah, L., Zelenakova, M., & Abd Elaziz, M. (2021). Enhanced adaptive neuro-fuzzy inference system using reptile search algorithm for relating swelling potentiality using index geotechnical properties: A case study at El Sherouk City. Mathematics, 9(24), 1–13. https://doi.org/10.3390/math924329510.3390/math9243295 ↗
59. Almotairi, K. H., & Abualigah, L. (2022). Hybrid reptile search algorithm and remora optimization algorithm for optimization tasks and data clustering. Symmetry, 14(3), 458.10.3390/sym14030458 ↗
60. Al-Shourbaji, I., Helian, N., Sun, Y., Alshathri, S., & Abd Elaziz, M. (2022). Boosting ant colony optimization with reptile search algorithm for churn prediction. Mathematics, 10(7), 1031.10.3390/math10071031 ↗
61. Almotairi, K. H., & Abualigah, L. (2022). Improved reptile search algorithm with novel mean transition mechanism for constrained industrial engineering problems. Neural Computing and Applications, 34(20), 17257–17277.10.1007/s00521-022-07369-0 ↗
62. Emam, M. M., Houssein, E. H., & Ghoniem, R. M. (2022). A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images. Computers in Biology and Medicine, 152, 106404.10.1016/j.compbiomed.2022.106404 ↗
63. Abualigah, L., & Diabat, A. (2022). Chaotic binary reptile search algorithm and its feature selection applications. Journal of Ambient Intelligence and Humanized Computing, 2022, 1–17.
64. Huang, L., Wang, Y., Guo, Y., & Hu, G. (2022). An improved reptile search algorithm based on lévy flight and interactive crossover strategy to engineering application. Mathematics, 10(13), 2329.10.3390/math10132329 ↗
65. Wang, G. G., Guo, L. H., Gandomi, A. H., Hao, G. S., & Wang, H. (2014). Chaotic krill herd algorithm. Information Sciences, 274, 17–34. https://doi.org/10.1016/j.ins.2014.02.12310.1016/j.ins.2014.02.123 ↗
66. Ewees, A. A., & Elaziz, M. A. (2020). Performance analysis of chaotic multi-verse harris hawks optimization: a case study on solving engineering problems. Engineering Applications of Artificial Intelligence, 88, 103370. https://doi.org/10.1016/j.engappai.2019.10337010.1016/j.engappai.2019.103370 ↗
67. Li, C. Y., Li, J., Chen, H. L., & Heidari, A. A. (2021). Memetic harris hawks optimization: Developments and perspectives on project scheduling and QoS-aware web service composition. Expert Systems with Applications, 171, 114529. https://doi.org/10.1016/j.eswa.2020.11452910.1016/j.eswa.2020.114529 ↗
68. Rather, S., & Bala, P. (2020). Swarm-based chaotic gravitational search algorithm for solving mechanical engineering design problems. World Journal of Engineering, 17(1), 97–114. https://doi.org/10.1108/WJE−09-2019-025410.1108/WJE−09-2019-0254 ↗
69. Jing, K., Zhao, X. G., Zhang, X. Y., & Liu, D. (2018). Ant lion optimizer with levy variation and adaptive elite competition mechanism. CAAI Transactions on Intelligent Systems, 13(2), 236–242. https://doi.org/10.11992/tis.20170609110.11992/tis.201706091 ↗
70. Coelho, L. (2010). Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Systems with Applications, 37(2), 1676–1683. https://doi.org/10.1016/j.eswa.2009.06.04410.1016/j.eswa.2009.06.044 ↗
71. Gupta, S., & Deep, K. (2019). Improved sine cosine algorithm with crossover scheme for global optimization. Knowledge-Based Systems, 165, 374–406. https://doi.org/10.1016/j.knosys.2018.12.00810.1016/j.knosys.2018.12.008 ↗
72. Wu, X. L., Hu, S., & Cheng, W. (2021). Multi-objective signal timing optimization based on improved whale optimization algorithm. Journal of kunming university of science and technology (Natural science), 46(1), 134–141. (in Chinese).
73. Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249. https://doi.org/10.1016/j.knosys.2015.07.00610.1016/j.knosys.2015.07.006 ↗
74. Ahmadianfar, I., Bozorg-Haddad, O., & Chu, X. (2020). Gradient-based optimizer: A new metaheuristic optimization algorithm. Information Sciences, 540, 131–159. https://doi.org/10.1016/j.ins.2020.06.03710.1016/j.ins.2020.06.037 ↗
75. Youn, B. D., Choi, K. K., Yang, R. J., & Gu, L. (2004). Reliability-based design optimization for crashworthiness of vehicle side impact. Structural & Multidisciplinary Optimization, 26(3–4), 272–283. https://doi.org/10.1007/s00158-003-0345-010.1007/s00158-003-0345-0 ↗
76. Hu, G., Zhong, J. Y., & Wei, G. (2023). SaCHBA_PDN: Modified honey badger algorithm with multi-strategy for UAV path planning. Expert Systems with Applications, 223, 223119941. https://doi.org/10.1016/j.eswa.2023.11994110.1016/j.eswa.2023.119941 ↗
77. Hu, G., Zhong, J. Y., Wei, G., & Chang, C. T. (2023). DTCSMO: An efficient hybrid starling murmuration optimizer for engineering applications. Computer Methods in Applied Mechanics and Engineering, 405, 115878. https://doi.org/10.1016/j.cma.2023.11587810.1016/j.cma.2023.115878 ↗
78. Hu, G., Yang, R., Qin, X. Q., & Wei, G. (2023). MCSA: Multi-strategy boosted chameleon-inspired optimization algorithm for engineering applications. Computer Methods in Applied Mechanics and Engineering, 403, 115676. https://doi.org/10.1016/j.cma.2022.11567610.1016/j.cma.2022.115676 ↗