A Multi-strategy Improved Snake Optimizer Assisted with Population Crowding Analysis for Engineering Design Problems
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Metaheuristic Optimization Algorithms Research · Advanced Multi-Objective Optimization Algorithms · Evolutionary Algorithms and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (72)
Show source-provided references
1. Bilel, N., Mohamed, N., Zouhaier, A., & Lotfi, R. (2019). An efficient evolutionary algorithm for engineering design problems. Soft Computing, 23, 6197–6213.10.1007/s00500-018-3273-z ↗
2. Huy, T. H. B., Nallagownden, P., Truong, K. H., Kannan, R., Vo, D. N., & Ho, N. (2022). Multi-objective search group algorithm for engineering design problems. Applied Soft Computing, 126, 109287.
3. Fleming, P.J., Purshouse, R.C., & Lygoe, R.J. (2005). Many-objective optimization: An engineering design perspective. In Evolutionary Multi-Criterion Optimization, Heidelberg, Berlin, pp. 14–32.10.1007/978-3-540-31880-4_2 ↗
4. Deng, L., & Liu, S. (2023). A multi-strategy improved slime mould algorithm for global optimization and engineering design problems. Computer Methods in Applied Mechanics and Engineering, 404, 115764.
5. Zou, F., Chen, D. B., Liu, H., Cao, S. Y., Ji, X. Y., & Zhang, Y. (2022). A survey of fitness landscape analysis for optimization. Neurocomputing, 503, 129–139.10.1016/j.neucom.2022.06.084 ↗
6. Abualigah, L., Elaziz, M. A., Khasawneh, A. M., Alshinwan, M., Ibrahim, R. A., Al-Qaness, M. A., Mirjalili, S., Sumari, P., & Gandomi, A. H. (2022). Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: A comprehensive survey, applications, comparative analysis, and results. Neural Computing and Applications, 34(6), 4081–4110.10.1007/s00521-021-06747-4 ↗
7. Rajeev, S., & Krishnamoorthy, C. S. (1992). Discrete optimization of structures using genetic algorithms. Journal of Structural Engineering, 118(5), 1233–1250.10.1061/(ASCE)0733-9445(1992)118:5(1233) ↗
8. Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA, USA: MIT Press.
9. Storn, R., & Price, K. V. (1997). Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.10.1023/A:1008202821328 ↗
10. Hansen, N., Müller, S. D., & Koumoutsakos, P. (2003). Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary Computation, 11(1), 1–18.10.1162/106365603321828970 ↗
11. Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248.10.1016/j.ins.2009.03.004 ↗
12. Alatas, B. (2011). ACROA: Artificial chemical reaction optimization algorithm for global optimization. Expert Systems with Applications, 38(10), 13170–13180.10.1016/j.eswa.2011.04.126 ↗
13. Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495–513.10.1007/s00521-015-1870-7 ↗
14. Nematollahi, A. F., Rahiminejad, A., & Vahidi, B. (2017). A novel physical based meta-heuristic optimization method known as lightning attachment procedure optimization. Applied Soft Computing, 59, 596–621.10.1016/j.asoc.2017.06.033 ↗
15. Bouchekara, H. R. (2019). Electrostatic discharge algorithm: A novel nature-inspired optimisation algorithm and its application to worst-case tolerance analysis of an EMC filter. IET Science, Measurement & Technology, 13(4), 491–499.10.1049/iet-smt.2018.5194 ↗
16. Zhao, W. G., Wang, L. Y., & Zhang, Z. X. (2019). Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283–304.10.1016/j.knosys.2018.08.030 ↗
17. Anita Yadav, A., & Kumar, N. (2020). Artificial electric field algorithm for engineering optimization problems. Expert Systems with Applications, 149, 113308.
18. Houssein, E. H., 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, 103731.10.1016/j.engappai.2020.103731 ↗
19. Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2020). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems, 191, 105190.10.1016/j.knosys.2019.105190 ↗
20. Ghasemi, M., Davoudkhani, I. F., Akbari, E., Rahimnejad, A., Ghavidel, S., & Li, L. (2020). A novel and effective optimization algorithm for global optimization and its engineering applications: Turbulent flow of water-based optimization (TFWO). Engineering Applications of Artificial Intelligence, 92, 103666.10.1016/j.engappai.2020.103666 ↗
21. Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S., & Al-Atabany, W. (2021). Archimedes optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51(3), 1531–1551.10.1007/s10489-020-01893-z ↗
22. Ghasemi, M., Zare, M., Zahedi, A., Akbari, M. A., Mirjalili, S., & Abualigah, L. (2024). Geyser inspired algorithm: A new geological-inspired meta-heuristic for real-parameter and constrained engineering optimization. Journal of Bionic Engineering, 21(1), 374–408.10.1007/s42235-023-00437-8 ↗
23. Rai, R., Das, A., Ray, S., & Dhal, K. G. (2022). Human-inspired optimization algorithms: Theoretical foundations, algorithms, open-research issues and application for multi-level thresholding. Archives of Computational Methods in Engineering, 29(7), 5313–5352.10.1007/s11831-022-09766-z ↗
24. Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. Simulation, 76(2), 60–68.10.1177/003754970107600201 ↗
25. Dai, C.H., Zhu, Y.F., & Chen, W.R. (2007). Seeker optimization algorithm. In Computational Intelligence and Security, Heidelberg, Berlin, pp. 167–176.10.1007/978-3-540-74377-4_18 ↗
26. Shi, Y.H. (2011). Brain storm optimization algorithm. In Advances in Swarm Intelligence, Heidelberg, Berlin, pp. 303–309.10.1007/978-3-642-21515-5_36 ↗
27. Tian, D. X., Hu, J. J., Sheng, Z. G., Wang, Y. P., Ma, J. M., & Wang, J. (2016). Swarm intelligence algorithm inspired by route choice behavior. Journal of Bionic Engineering, 13(4), 669–678.10.1016/S1672-6529(16)60338-4 ↗
28. Huan, T. T., Kulkarni, A. J., Kanesan, J., Huang, C. J., & Abraham, A. (2017). Ideology algorithm: A socio-inspired optimization methodology. Neural Computing and Applications, 28(1), 845–876.10.1007/s00521-016-2379-4 ↗
29. Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling, 63, 464–490.10.1016/j.apm.2018.06.036 ↗
30. Shabani, A., Asgarian, B., Salido, M., & Asil Gharebaghi, S. (2020). Search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems. Expert Systems with Applications, 161, 113698.10.1016/j.eswa.2020.113698 ↗
31. Mohamed, A. W., Hadi, A. A., & Mohamed, A. K. (2020). Gaining-sharing knowledge based algorithm for solving optimization problems: A novel nature-inspired algorithm. International Journal of Machine Learning and Cybernetics, 11(7), 1501–1529.10.1007/s13042-019-01053-x ↗
32. Rahkar Farshi, T. (2021). Battle royale optimization algorithm. Neural Computing and Applications, 33(4), 1139–1157.10.1007/s00521-020-05004-4 ↗
33. Emami, H. (2022). Anti-coronavirus optimization algorithm. Soft Computing, 26(11), 4991–5023.10.1007/s00500-022-06903-5 ↗
34. Yuan, Y. L., Shen, Q. L., Wang, S., Ren, J. J., Yang, D. H., Yang, Q. K., Fan, J. K., & Mu, X. K. (2023). Coronavirus mask protection algorithm: A new bio-inspired optimization algorithm and its applications. Journal of Bionic Engineering, 20(4), 1747–1765.10.1007/s42235-023-00359-5 ↗
35. Kennedy, J. & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95 - International Conference on Neural Networks, Perth, WA, Australia, pp. 1942–1948.10.1109/ICNN.1995.488968 ↗
36. Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28–39.10.1109/MCI.2006.329691 ↗
37. Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.10.1007/s10898-007-9149-x ↗
38. Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2013). Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), 17–35.10.1007/s00366-011-0241-y ↗
39. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.10.1016/j.advengsoft.2013.12.007 ↗
40. Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249.10.1016/j.knosys.2015.07.006 ↗
41. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.10.1016/j.advengsoft.2016.01.008 ↗
42. 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, 163–191.10.1016/j.advengsoft.2017.07.002 ↗
43. Salgotra, R., & Singh, U. (2019). The naked mole-rat algorithm. Neural Computing and Applications, 31, 8837–8857.10.1007/s00521-019-04464-7 ↗
44. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. L. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872.10.1016/j.future.2019.02.028 ↗
45. Dhiman, G., & Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems, 165, 169–196.10.1016/j.knosys.2018.11.024 ↗
46. 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, 300–323.10.1016/j.future.2020.03.055 ↗
47. Abdollahzadeh, B., Soleimanian Gharehchopogh, F., & Mirjalili, S. (2021). Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems, 36(10), 5887–5958.10.1002/int.22535 ↗
48. Tu, J. Z., Chen, H. L., Wang, M. J., & Gandomi, A. H. (2021). The colony predation algorithm. Journal of Bionic Engineering, 18(3), 674–710.10.1007/s42235-021-0050-y ↗
49. Wang, L. Y., Cao, Q. J., Zhang, Z. X., Mirjalili, S., & Zhao, W. G. (2022). Artificial rabbits optimization: a new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 114, 105082.
50. Hashim, F. A., & Hussien, A. G. (2022). Snake Optimizer: A novel meta-heuristic optimization algorithm. Knowledge-Based Systems, 242, 108320.10.1016/j.knosys.2022.108320 ↗
51. Zhao, W. G., Wang, L. Y., & Mirjalili, S. (2022). Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications. Computer Methods in Applied Mechanics and Engineering, 388, 114194.
52. Abdollahzadeh, B., Gharehchopogh, F. S., Khodadadi, N., & Mirjalili, S. (2022). Mountain gazelle optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems. Advances in Engineering Software, 174, 103282.10.1016/j.advengsoft.2022.103282 ↗
53. Golalipour, K., Davoudkhani, I. F., Nasri, S., Naderipour, A., Mirjalili, S., Abdelaziz, A. Y., & El-Shahat, A. (2023). The corona virus search optimizer for solving global and engineering optimization problems. Alexandria Engineering Journal, 78, 614–642.10.1016/j.aej.2023.07.066 ↗
54. Jia, H. M., Rao, H. H., Wen, C. S., & Mirjalili, S. (2023). Crayfish optimization algorithm. Artificial Intelligence Review, 56(2), 1919–1979.
55. Rezvani, K., Gaffari, A., & Dishabi, M. R. E. (2023). The bedbug meta-heuristic algorithm to solve optimization problems. Journal of Bionic Engineering, 20(5), 2465–2485.10.1007/s42235-023-00356-8 ↗
56. Khurma, R. A., Albashish, D., Braik, M., Alzaqebah, A., Qasem, A., & Adwan, O. (2023). An augmented snake optimizer for diseases and COVID-19 diagnosis. Biomedical Signal Processing and Control, 84, 104718.
57. Alamir, N., Kamel, S., Megahed, T. F., Hori, M., & Abdelkader, S. M. (2022). Energy management of multi-microgrid considering demand response using snake optimizer. 2022 23rd International Middle East Power Systems Conference (MEPCON), Cairo, Egypt, pp. 1–6.10.1109/MEPCON55441.2022.10021750 ↗
58. Wang, C., Jiao, S. B., Li, Y. J., & Zhang, Q. (2023). Capacity optimization of a hybrid energy storage system considering wind-solar reliability evaluation based on a novel multi-strategy snake optimization algorithm. Expert Systems with Applications, 231, 120602.
59. Hu, G., Yang, R., Abbas, M., & Wei, G. (2023). BEESO: Multi-strategy boosted snake-inspired optimizer for engineering applications. Journal of Bionic Engineering, 20(4), 1791–1827.10.1007/s42235-022-00330-w ↗
60. Zhang, J. Q., & Sanderson, A. C. (2009). JADE: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation, 13(5), 945–958.10.1109/TEVC.2009.2014613 ↗
61. Tanabe, R., & Fukunaga, A. (2013). Success-history based parameter adaptation for differential evolution. 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico, pp. 71–78.10.1109/CEC.2013.6557555 ↗
62. Sallam, K. M., Elsayed, S. M., Chakrabortty, R. K., & Ryan, M. J. (2020). Improved multi-operator differential evolution algorithm for solving unconstrained problems. 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK, pp. 1–8.
63. Hong, L. B., Guo, Y. J., Liu, F. C., & Wang, B. (2023). A variant of the united multi-operator evolutionary algorithms using sequential quadratic programming and improved SHADE-cnEpSin. Information Sciences, 622, 652–681.10.1016/j.ins.2022.11.131 ↗
64. Andrei, N. (2017). Sequential Quadratic Programming (SQP). Cham, Switzerland: Springer International Publishing.
65. Yao, L. G., Yuan, P. L., Tsai, C. Y., Zhang, T. H., Lu, Y., & Ding, S. L. (2023). ESO: An enhanced snake optimizer for real-world engineering problems. Expert Systems with Applications, 230, 120594.
66. Deng, L. Y., & Liu, S. Y. (2023). Snow ablation optimizer: A novel metaheuristic technique for numerical optimization and engineering design. Expert Systems with Applications, 225, 120069.
67. Shen, Y., Zhang, C., Soleimanian Gharehchopogh, F., & Mirjalili, S. (2023). An improved whale optimization algorithm based on multi-population evolution for global optimization and engineering design problems. Expert Systems with Applications, 215, 119269.10.1016/j.eswa.2022.119269 ↗
68. Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2011). GA with a new multi-parent crossover for solving IEEE-CEC2011 competition problems. 2011 IEEE Congress of Evolutionary Computation (CEC), Los Angeles, USA, pp. 1034–1040.10.1109/CEC.2011.5949731 ↗
69. Zhang, G. & Shi, Y.H. (2018). Hybrid sampling evolution strategy for solving single objective bound constrained problems. In 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil, pp. 1–7.10.1109/CEC.2018.8477908 ↗
70. Diep, Q. B. (2019). Self-organizing migrating algorithm team to team adaptive - SOMA T3A. 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand, pp. 1182–1187.10.1109/CEC.2019.8790202 ↗
71. Mohamed, A. W., Hadi, A. A., Mohamed, A. K., & Awad, N. H. (2020). Evaluating the performance of adaptive gainingsharing knowledge based algorithm on CEC 2020 benchmark problems. 2020 IEEE congress on evolutionary computation (CEC), Glasgow, UK, pp. 1–8.
72. Mohamed, A. W., Hadi, A. A., Agrawal, P., Sallam, K. M., & Mohamed, A. K. (2021). Gaining-sharing knowledge based algorithm with adaptive parameters hybrid with IMODE algorithm for solving CEC 2021 benchmark problems. 2021 IEEE Congress on Evolutionary Computation (CEC), Kraków, Poland, pp. 841–848.10.1109/CEC45853.2021.9504814 ↗