CQFFA: A Chaotic Quasi-oppositional Farmland Fertility Algorithm for Solving Engineering Optimization 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 (89)
Show source-provided references
1. Nadimi-Shahraki, M. H., Taghian, S., Mirjalili, S., Ewees, A. A., Abualigah, L., & Abd Elaziz, M. (2021). MTV-MFO: Multi-trial vector-based moth-flame optimization algorithm. Symmetry, 13(12), 2388.10.3390/sym13122388 ↗
2. Goldanloo, M. J., & Gharehchopogh, F. S. (2021). A hybrid OBL-based firefly algorithm with symbiotic organisms search algorithm for solving continuous optimization problems. The Journal of Supercomputing, 3, 1–34.
3. Zamani, H., Nadimi-Shahraki, M. H., & Gandomi, A. H. (2021). QANA: Quantum-based avian navigation optimizer algorithm. Engineering Applications of Artificial Intelligence, 104(104314), 2021.
4. Ghafori, S., & Gharehchopogh, F. S. (2021). Advances in spotted hyena optimizer: a comprehensive survey. Archives of Computational Methods in Engineering, 29, 1–22.
5. Banaie-Dezfouli, M., Nadimi-Shahraki, M. H., & Beheshti, Z. (2021). R-GWO: representative-based grey wolf optimizer for solving engineering problems. Applied Soft Computing, 106, 107328.10.1016/j.asoc.2021.107328 ↗
6. Zaman, H. R. R., & Gharehchopogh, F. S. (2021). An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems. Engineering with Computers, 5, 1–35.
7. Gharehchopogh, F. S. (2022). Advances in tree seed algorithm: a comprehensive survey. Archives of Computational Methods in Engineering, 7, 1–24.
8. Gharehchopogh, F. S. (2022). An improved tunicate swarm algorithm with best-random mutation strategy for global optimization problems. Journal of Bionic Engineering, 48, 1–26.
9. Gharehchopogh, F. S., & Gholizadeh, H. (2019). A comprehensive survey: whale optimization algorithm and its applications. Swarm and Evolutionary Computation, 48, 1–24.10.1016/j.swevo.2019.03.004 ↗
10. Xu, Z., Yang, Y., Li, J., Zhang, X., Lu, B., & Gao, S. (2021). Comparative study on single and multiple chaotic maps incorporated grey wolf optimization algorithms. IEEE Access, 9, 77416–77437.10.1109/ACCESS.2021.3083220 ↗
11. Ibrahim, R. A., Elaziz, M. A., & Lu, S. (2018). (2018) Chaotic opposition-based grey-wolf optimization algorithm based on differential evolution and disruption operator for global optimization. Expert Systems with Applications, 108, 1–27.10.1016/j.eswa.2018.04.028 ↗
12. Li,J., Cheng, Y.M., & Chen,K.Z. (2014) Chaotic particle swarm optimization algorithm based on adaptive inertia weight, in Control and Decision Conference (2014 CCDC), The 26th Chinese, 2014: IEEE, 1310-1315.10.1109/CCDC.2014.6852369 ↗
13. Wang, L., Liu, X., Sun, M., Qu, J., & Wei, Y. (2018). A new chaotic starling particle swarm optimization algorithm for clustering problems. Mathematical Problems in Engineering., 2018, 1–14.
14. Gao, S., Zhou, M., Wang, Y., Cheng, J., Yachi, H., & Wang, J. (2018). Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction. IEEE Transactions on Neural Networks and Learning Systems, 30(2), 601–614.10.1109/TNNLS.2018.2846646 ↗
15. Zhang, Y. T., Zhou, W., & Yi, J. (2016) A novel adaptive chaotic bacterial foraging optimization algorithm. In 2016 international conference on computational modeling, simulation and applied mathematics.10.12783/dtcse/cmsam2016/3621 ↗
16. Teng, H., & Cao, A. (2011). 2011) An novel quantum genetic algorithm with Piecewise Logistic chaotic map, In Natural Computation (ICNC. Seventh International Conference on, 2, 1053–1057.
17. Mitic, M., Vukovic, N., Petrovic, M., & Miljkovic, Z. (2015). Chaotic fruit fly optimization algorithm. Knowledge-Based Systems, 89, 446–458.10.1016/j.knosys.2015.08.010 ↗
18. Yuzgec, U., & Eser, M. (2018). Chaotic based differential evolution algorithm for optimization of baker’s yeast drying process. Egyptian Informatics Journal., 19, 151–163.10.1016/j.eij.2018.02.001 ↗
19. Thangaraj, R., Pant, M., Chelliah, T.R. & Abraham, A. (2012) Opposition based chaotic differential evolution algorithm for solving global optimization problems, 2012 fourth world congress on nature and biologically inspired computing (NaBIC), 1–7.10.1109/NaBIC.2012.6402168 ↗
20. Gandomi, A. H., & Yang, X. S. (2014). Chaotic bat algorithm. Journal of computational science, 5(2), 224–232.10.1016/j.jocs.2013.10.002 ↗
21. Guvenc, U., Duman, S., & Hinislioglu, Y. (2017). Chaotic Moth Swarm Algorithm. IEEE International Conference on Inovations in Intelligent Systems and Applications (INISTA), 2017, 90–95.
22. Rahman, T. A., Arry, A., Jalil, N. A. A., & Ahmad, R. M. K. R. (2017). Chaotic fractal search algorithm for global optimization with application to control design. IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), 2017, 111–116.10.1109/ISCAIE.2017.8074960 ↗
23. Mirjalili, S., & Gandomi, A. H. (2017). Chaotic gravitational constants for the gravitational search algorithm. Applied Soft Computing, 53, 407–419.10.1016/j.asoc.2017.01.008 ↗
24. Rizk-Allah, R. M., Hassanien, A. E., & Bhattacharyya, S. (2018). Chaotic crow search algorithm for fractional optimization problems. Applied Soft Computing, 71, 1161–1175.10.1016/j.asoc.2018.03.019 ↗
25. Arora, S., & Anand, P. (2018). Chaotic grasshopper optimization algorithm for global optimization. Neural Computing and Applications, 31, 1–21.
26. Yao, J. F., Mei, C., Peng, X. Q., Hu, Z. K., & Hu, J. (2001). A new optimization approach-chaos genetic algorithm. Systems Engineering, 1, 015.
27. Mingjun, J., & Huanwen, T. (2004). Application of chaos in simulated annealing. Chaos, Solitons & Fractals, 21(4), 933–941.10.1016/j.chaos.2003.12.032 ↗
28. Gao, S., Yu, Y., Wang, Y., Wang, J., Cheng, J., & Zhou, M. (2019). Chaotic local search-based differential evolution algorithms for optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(6), 3954–3967.10.1109/TSMC.2019.2956121 ↗
29. Zhou, Y., Su, K., & Shao, L. (2018). A new chaotic hybrid cognitive optimization algorithm. Cognitive Systems Research, 52, 537–542.10.1016/j.cogsys.2018.08.001 ↗
30. Chahkandi, V., Yaghoobi, M., & Veisi, G. (2013). CABC–CSA: a new chaotic hybrid algorithm for solving optimization problems. Nonlinear Dynamics, 73(12), 475–484.10.1007/s11071-013-0802-2 ↗
31. Wang, G. G., Guo, L., Gandomi, A. H., Hao, G. H., & Wang, H. (2014). Chaotic krill herd algorithm. Information Sciences, 274, 17–34.10.1016/j.ins.2014.02.123 ↗
32. Sayed, G. I., Khoriba, G. M., & Haggag, H. (2018). A novel chaotic salp swarm algorithm for global optimization and feature selection. Applied Intelligence, 48, 1–20.10.1007/s10489-018-1158-6 ↗
33. Gharehchopogh, F. S., Maleki, I., & Dizaji, Z. A. (2021). Chaotic vortex search algorithm: metaheuristic algorithm for feature selection. Evolutionary Intelligence, 15, 1–32.
34. Boushaki, S. I., Kamel, N., & Bendjeghaba, O. (2018). A new quantum chaotic cuckoo search algorithm for data clustering. Expert Systems with Applications, 96, 358–372.10.1016/j.eswa.2017.12.001 ↗
35. Tharwat, A., & Hassanien, A. E. (2018). Chaotic antlion algorithm for parameter optimization of support vector machine. Applied Intelligence, 48(3), 670–686.10.1007/s10489-017-0994-0 ↗
36. Xu, X., Rong, H., Trovati, M., Liptrott, M., & Bessis, N. (2018). CS-PSO: Chaotic particle swarm optimization algorithm for solving combinatorial optimization problems. Soft Computing, 22(3), 783–795.10.1007/s00500-016-2383-8 ↗
37. Kohli, M., & Arora, S. (2018). Chaotic grey wolf optimization algorithm for constrained optimization problems. Journal of Computational Design and Engineering, 5(4), 458–472.10.1016/j.jcde.2017.02.005 ↗
38. Shayanfar, H., & Gharehchopogh, F. S. (2018). Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems. Applied Soft Computing, 71, 728–746.10.1016/j.asoc.2018.07.033 ↗
39. Yuan, X., Zhao, J., Yang, Y., & Wang, Y. (2014). Hybrid parallel chaos optimization algorithm with harmony search algorithm. Applied Soft Computing, 17, 12–22.10.1016/j.asoc.2013.12.016 ↗
40. Asghari, K., Masdari, M., Gharehchopogh, F. S., & Saneifard, R. (2021). A chaotic and hybrid gray wolf-whale algorithm for solving continuous optimization problems. Progress in Artificial Intelligence, 10, 1–26.10.1007/s13748-021-00244-4 ↗
41. Geisel, T., & Fairen, V. (1984). Statistical properties of chaos in Chebyshev maps. Physics Letters A, 105(6), 263–266.10.1016/0375-9601(84)90993-9 ↗
42. Hilborn, R. C. (2000). Chaos and Nonlinear Dynamics: An Introduction for Scientists and Engineers. Oxford University Press.10.1093/acprof:oso/9780198507239.001.0001 ↗
43. Lauwerier, H. (1989). Two-dimensional iterative maps. Chaos, 2, 58–95.
44. Barshandeh, S., & Haghzadeh, M. (2020). A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems. Engineering with Computers, 4, 1–44.
45. Li, Y., Deng, S., & Xiao, D. (2011). A novel Hash algorithm construction based on chaotic neural network. Neural Computing and Applications, 20(1), 133–141.10.1007/s00521-010-0432-2 ↗
46. Tomida, A.G. (2008) Matlab toolbox and GUI for analyzing one-dimensional chaotic maps, International conference on computational sciences and its application, 321–330.10.1109/ICCSA.2008.7 ↗
47. Devaney, R. (2008). An Introduction to Chaotic Dynamical Systems. Westview: Westview press.
48. Peitgen,H.O, Jurgens, H., & Saupe,D. (2006) Chaos and fractals: new frontiers of science. Springer science & business media.
49. Ott, E. (2002). Chaos in Dynamical Systems. Cambridge University Press.10.1017/CBO9780511803260 ↗
50. Truong, K. H., Nallagownden, P., Baharudin, Z., & Vo, D. N. (2019). A quasi-oppositional-chaotic symbiotic organisms search algorithm for global optimization problems. Applied Soft Computing, 77, 567–583.10.1016/j.asoc.2019.01.043 ↗
51. Basu, M. (2016). Quasi-oppositional differential evolution for optimal reactive power dispatch. International Journal of Electrical Power & Energy Systems, 78, 29–40.10.1016/j.ijepes.2015.11.067 ↗
52. Warid, W., Hizam, H., Mariun, N., & Wahab, N. I. A. (2018). A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution. Applied Soft Computing, 65, 360–373.10.1016/j.asoc.2018.01.039 ↗
53. Guha, D., Roy, P., & Banerjee, S. (2017). Quasi-oppositional symbiotic organism search algorithm applied to load frequency control. Swarm and Evolutionary Computation, 33, 46–67.10.1016/j.swevo.2016.10.001 ↗
54. Coello, C. A. C. (2000). Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry, 41(2), 113–127.10.1016/S0166-3615(99)00046-9 ↗
55. Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: Theory and application. Advances in Engineering Software, 105, 30–47.10.1016/j.advengsoft.2017.01.004 ↗
56. Sadollah, A., Bahreininejad, A., Eskandar, H., & Hamdi, M. (2013). Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Applied Soft Computing, 13(5), 2592–2612.10.1016/j.asoc.2012.11.026 ↗
57. 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 ↗
58. Liu, H., Cai, Z., & Wang, Y. (2010). Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Applied Soft Computing, 10(2), 629–640.10.1016/j.asoc.2009.08.031 ↗
59. Zhang, M., Luo, W., & Wang, X. (2008). Differential evolution with dynamic stochastic selection for constrained optimization. Information Sciences, 178(15), 3043–3074.10.1016/j.ins.2008.02.014 ↗
60. 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 ↗
61. 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 ↗
62. Ray, T., & Saini, P. (2001). Engineering design optimization using a swarm with an intelligent information sharing among individuals. Engineering Optimization, 33(6), 735–748.10.1080/03052150108940941 ↗
63. 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 ↗
64. Tsai, J. F. (2005). Global optimization of nonlinear fractional programming problems in engineering design. Engineering Optimization, 37(4), 399–409.10.1080/03052150500066737 ↗
65. Savsani, P., & Savsani, V. (2016). Passing vehicle search (PVS): A novel metaheuristic algorithm. Applied Mathematical Modelling, 40(5–6), 3951–3978.10.1016/j.apm.2015.10.040 ↗
66. Rao, R. V., Savsani, V. J., & Vakharia, S. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303–315.10.1016/j.cad.2010.12.015 ↗
67. 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, 849–872.10.1016/j.future.2019.02.028 ↗
68. Gupta, S., Tiwari, R., & Nair, S. B. (2007). Multi-objective design optimisation of rolling bearings using genetic algorithms. Mechanism and Machine Theory, 42(10), 1418–1443.10.1016/j.mechmachtheory.2006.10.002 ↗
69. Deb, K. (1991). Optimal design of a welded beam via genetic algorithms,". AIAA Journal, 29(11), 2013–2015.10.2514/3.10834 ↗
70. Lee, K. S., & Geem, Z. W. (2004). A new structural optimization method based on the harmony search algorithm. Computers & Structures, 82(9–10), 781–798.
71. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.10.1016/j.advengsoft.2016.01.008 ↗
72. Ragsdell, K., & Phillips, D., (1976) Optimal design of a class of welded structures using geometric programming.10.1115/1.3438995 ↗
73. Huang, F. Z., Wang, L., & He, Q. (2007). An effective co-evolutionary differential evolution for constrained optimization. Applied Mathematics and Computation, 186(1), 340–356.10.1016/j.amc.2006.07.105 ↗
74. Mezura-Montes, E. N., & Coello, C. A. C. (2005). A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Transactions on Evolutionary computation, 9(1), 1–17.10.1109/TEVC.2004.836819 ↗
75. Gandomi, A. H., Yang, X. S., Alavi, A. H., & Talatahari, S. (2013). Bat algorithm for constrained optimization tasks. Neural Computing and Applications, 22(6), 1239–1255.10.1007/s00521-012-1028-9 ↗
76. Mezura-Montes, E. N., Coello, C. C., Velazquez-Reyes, J. S., & Muaoz-Da¡vila, L.A. (2007). Multiple trial vectors in differential evolution for engineering design. Engineering optimization, 39(5), 567–589.10.1080/03052150701364022 ↗
77. He, Q., & Wang, L. (2007). An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence, 20(1), 89–99.10.1016/j.engappai.2006.03.003 ↗
78. Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: charged system search. Acta Mechanica, 213(3–4), 267–289.10.1007/s00707-009-0270-4 ↗
79. Montemurro, M., Vincenti, A., & Vannucci, P. (2013). The automatic dynamic penalisation method (ADP) for handling constraints with genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 256, 70–87.10.1016/j.cma.2012.12.009 ↗
80. He, Q., & Wang, L. (2007). A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Applied Mathematics and Computation, 186(2), 1407–1422.10.1016/j.amc.2006.07.134 ↗
81. Coelho, L. D. S. (2010). Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Systems with Applications, 37(2), 1676–1683.10.1016/j.eswa.2009.06.044 ↗
82. Kaveh, A., & Bakhshpoori, T. (2016). Water evaporation optimization: a novel physically inspired optimization algorithm. Computers & Structures, 167, 69–85.10.1016/j.compstruc.2016.01.008 ↗
83. Rosenbrock, H. (1960). An automatic method for finding the greatest or least value of a function. The Computer Journal, 3(3), 175–184.10.1093/comjnl/3.3.175 ↗
84. Coello, C. A. C., & Montes, E. N. M. (2002). Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Advanced Engineering Informatics, 16(3), 193–203.10.1016/S1474-0346(02)00011-3 ↗
85. 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 ↗
86. Wang, L., & Li, L. P. (2010). An effective differential evolution with level comparison for constrained engineering design. Structural and Multidisciplinary Optimization, 41(6), 947–963.10.1007/s00158-009-0454-5 ↗
87. Arora, J.S.(2004) Introduction to optimum design. 945.10.1016/B978-012064155-0/50012-4 ↗
88. 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 ↗
89. Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, 151–166.10.1016/j.compstruc.2012.07.010 ↗