An Improved Atomic Orbital Search Algorithm Utilizing Firefly Algorithm for Optimization Problems
Subject Areas : Meta-heurestics
1 - دانشجو دکتری سازه، گروه مهندسی عمران، دانشگاه محقق اردبیلی، اردبیل، ایران
Keywords: Mathematical modelling, Numerical methods, Hybrid algorithm, Atomic Orbital Search algorithm, Firefly Algorithm, Engineering optimization,
Abstract :
The Atomic Orbital Search inspiration from the rules of quantum mechanics and Firefly Algorithm is a metaheuristic technique which is widely used for solving the optimization problems. Both algorithms collectively improved the performance of search. This article goals to optimize engineering design problems utilizing a new hybrid optimizer; AOS-FA (Atomic Orbital Search-Firefly Algorithm). Incorporating the FA methodology into the basic AOS framework has successfully addressedthe issue of local optima trap and significantly enhanced the quality of solutions generated by the algorithm. The FA algorithm is work on the combinatorial optimization and utilized as application of AOS algorithm. Hence, we merge these two algorithms and make a hybrid algorithm. The purpose of the suggested hybridization method was to promote the improvement of the exploration-exploitation manners of the AOS search. To analyse the viability of the suggested hybridized algorithm in real-world usages, it is studied for five constrained engineering design issues, and the performance was determined with other outstanding metaheuristics extracted from the publications.
References:
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34. Ghannadiasl, A. and Ghaemifard, S., "Crack detection of the cantilever beam using new triple hybrid algorithms based on particle swarm optimization", Frontiers of Structural and Civil Engineering, Vol. 16, No. 9, (2022), 1127-1140.
35. Khorram, S. and Bahrami, A., "Application of a new fuzzy dematel–todim hybrid algorithm in port dredging project management", Proceedings of the Institution of Civil Engineers - Maritime Engineering, Vol. 173, No. 3, (2020), 79-95 DOI: 10.1680/jmaen.2017.22.
36. Euchi, J. and Sadok, A., "Optimising the travel of home health carers using a hybrid ant colony algorithm", Proceedings of the Institution of Civil Engineers - Transport, Vol. 176, No. 6, (2023), 325-336 DOI: 10.1680/jtran.19.00114.
37. Hemagowri, J. and Tamil Selvan, P., "A hybrid evolutionary algorithm of optimized controller placement in sdn environment", Computer Assisted Methods in Engineering and Science, Vol., No., (2023), DOI: 10.24423/cames.498.
38. Abouhabaga, O.O.F., Gadallah, M.H., Kouta, H.K. and Zaghloul, M.A., "Hybrid inner-outer algorithm for solving real-world mechanical optimization problems", Journal of Engineering and Applied Science, Vol. 68, No. 1, (2021), 2 DOI: 10.1186/s44147-021-00004-0.
39. Fasina, E., Sawyerr, B.A., Abdullahi, Y.U. and Oke, S.A., "A comparison of two hybrid optimization techniques: The taguchi-bbd-firefly and the taguchi-regression-firefly methods on the is 2062-e250 steel plates boring problem", Journal of Engineering and Applied Science, Vol. 70, No. 1, (2023), 47 DOI: 10.1186/s44147-023-00215-7.
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43. Yang, X.-S., "Firefly algorithms for multimodal optimization", in International symposium on stochastic algorithms, Springer. Vol., No., (2009), 169-178.
44. Schmit Jr, L. and Farshi, B., "Some approximation concepts for structural synthesis", AIAA journal, Vol. 12, No. 5, (1974), 692-699.
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62. Kaveh, A. and Talatahari, S., "A novel heuristic optimization method: Charged system search", Acta Mechanica, Vol. 213, No. 3-4, (2010), 267-289.
63. Askarzadeh, A., "A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm", Computers & Structures, Vol. 169, No., (2016), 1-12.
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1. Ghaemifard, S. and Ghannadiasl, A., "Usages of metaheuristic algorithms in investigating civil infrastructure optimization models; a review", AI in Civil Engineering, Vol. 3, No. 1, (2024), 17 DOI: 10.1007/s43503-024-00036-4.
2. Houssein, E.H., Mahdy, M.A., Shebl, D. and Mohamed, W.M., A survey of metaheuristic algorithms for solving optimization problems, in Metaheuristics in machine learning: Theory and applications. 2021, Springer.515-543.
3. Ghaemifard, S. and Ghannadiasl, A., "Design and analysis of offshore wind turbines: Problem formulation and optimization techniques", Journal of Marine Science and Application, Vol. 23, No. 4, (2024), 707-722 DOI: 10.1007/s11804-024-00473-8.
4. Ghannadiasl, A. and Ghaemifard, S., "Meta-heuristic algorithms: An appropriate approach in crack detection", Innovative Infrastructure Solutions, Vol. 9, No. 7, (2024), 263 DOI: 10.1007/s41062-024-01583-6.
5. Shahebrahimi, S.S., Lork, A., Shayegan, D.S. and AmirKardoust, A.A., "Solving the problem of multi-stakeholder construction site layout using metaheuristic algorithms", Iranian Journal of Optimization, Vol. 3, No. 15, (2024), 235-249.
6. Fazli, M., khiabani, F.M. and Daneshian, B., "Meta-heuristic algorithms to solve the problem of terminal facilities on a real scale", Iranian Journal of Optimization, Vol. 1, No. 14, (2022), 51-65.
7. Dorigo, M., Birattari, M. and Stutzle, T., "Ant colony optimization", IEEE computational intelligence magazine, Vol. 1, No. 4, (2006), 28-39.
8. Karaboga, D., "Artificial bee colony algorithm", scholarpedia, Vol. 5, No. 3, (2010), 6915.
9. Eberhart, R. and Kennedy, J., "Particle swarm optimization", in Proceedings of the IEEE international conference on neural networks, Citeseer. Vol. 4, No., (1995), 1942-1948.
10. Jia, H., Peng, X. and Lang, C., "Remora optimization algorithm", Expert systems with applications, Vol. 185, No., (2021), 115665.
11. Hussien, A.G., "An enhanced opposition-based salp swarm algorithm for global optimization and engineering problems", Journal of Ambient Intelligence and Humanized Computing, Vol. 13, No. 1, (2022), 129-150.
12. Assiri, A.S., Hussien, A.G. and Amin, M., "Ant lion optimization: Variants, hybrids, and applications", IEEE Access, Vol. 8, No., (2020), 77746-77764.
13. Mirjalili, S., Mirjalili, S.M. and Lewis, A., "Grey wolf optimizer", Advances in engineering software, Vol. 69, No., (2014), 46-61.
14. Yang, X.S. and Hossein Gandomi, A., "Bat algorithm: A novel approach for global engineering optimization", Engineering computations, Vol. 29, No. 5, (2012), 464-483.
15. Gandomi, A.H. and Alavi, A.H., "Krill herd: A new bio-inspired optimization algorithm", Communications in nonlinear science and numerical simulation, Vol. 17, No. 12, (2012), 4831-4845.
16. Mirjalili, S. and Lewis, A., "The whale optimization algorithm", Advances in engineering software, Vol. 95, No., (2016), 51-67.
17. Holland, J.H., "Genetic algorithms", Scientific american, Vol. 267, No. 1, (1992), 66-73.
18. Beyer, H.-G. and Schwefel, H.-P., "Evolution strategies–a comprehensive introduction", Natural computing, Vol. 1, No., (2002), 3-52.
19. Banzhaf, W., Koza, J., Ryan, C., Spector, L. and Jacob, C., "Genetic programming", IEEE Intelligent Systems and their Applications, Vol. 15, No. 3, (2000), 74-84.
20. Price, K.V., Differential evolution, in Handbook of optimization: From classical to modern approach. 2013, Springer.187-214.
21. Jaderyan, M. and Khotanlou, H., "Virulence optimization algorithm", Applied Soft Computing, Vol. 43, No., (2016), 596-618.
22. Hatamlou, A., "Black hole: A new heuristic optimization approach for data clustering", Information sciences, Vol. 222, No., (2013), 175-184.
23. Sinha, N., Chakrabarti, R. and Chattopadhyay, P., "Evolutionary programming techniques for economic load dispatch", IEEE transactions on evolutionary computation, Vol. 7, No. 1, (2003), 83-94.
24. Rashedi, E., Nezamabadi-Pour, H. and Saryazdi, S., "Gsa: A gravitational search algorithm", Information sciences, Vol. 179, No. 13, (2009), 2232-2248.
25. Ghasemi, M., Golalipour, K., Zare, M., Mirjalili, S., Trojovský, P., Abualigah, L. and Hemmati, R., "Flood algorithm (fla): An efficient inspired meta-heuristic for engineering optimization", The Journal of Supercomputing, Vol. 80, No. 15, (2024), 22913-23017 DOI: 10.1007/s11227-024-06291-7.
26. Kaveh, A. and Dadras, A., "A novel meta-heuristic optimization algorithm: Thermal exchange optimization", Advances in engineering software, Vol. 110, No., (2017), 69-84.
27. Kaveh, A. and Khayatazad, M., "A new meta-heuristic method: Ray optimization", Computers & Structures, Vol. 112, No., (2012), 283-294.
28. Geem, Z.W., Kim, J.H. and Loganathan, G.V., "A new heuristic optimization algorithm: Harmony search", simulation, Vol. 76, No. 2, (2001), 60-68.
29. Ghorbani, N. and Babaei, E., "Exchange market algorithm", Applied Soft Computing, Vol. 19, No., (2014), 177-187.
30. Kannan, B. and Kramer, S.N., "An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design", Vol., No., (1994).
31. Hakli, H. and Ortacay, Z., "An improved scatter search algorithm for the uncapacitated facility location problem", Computers & Industrial Engineering, Vol. 135, No., (2019), 855-867.
32. Ghannadiasl, A. and Ghaemifard, S., "Parameter selection for pso-based hybrid algorithms and its effect on crack detection in cantilever beams", Numerical Methods in Civil Engineering, Vol. 9, No. 2, (2024), 17-28 DOI: 10.61186/nmce.2311.1036.
33. Ghajarnia, N., Haddad, O.B. and Mariño, M.A., "Performance of a novel hybrid algorithm in the design of water networks", Proceedings of the Institution of Civil Engineers - Water Management, Vol. 164, No. 4, (2011), 173-191 DOI: 10.1680/wama.1000028.
34. Ghannadiasl, A. and Ghaemifard, S., "Crack detection of the cantilever beam using new triple hybrid algorithms based on particle swarm optimization", Frontiers of Structural and Civil Engineering, Vol. 16, No. 9, (2022), 1127-1140.
35. Khorram, S. and Bahrami, A., "Application of a new fuzzy dematel–todim hybrid algorithm in port dredging project management", Proceedings of the Institution of Civil Engineers - Maritime Engineering, Vol. 173, No. 3, (2020), 79-95 DOI: 10.1680/jmaen.2017.22.
36. Euchi, J. and Sadok, A., "Optimising the travel of home health carers using a hybrid ant colony algorithm", Proceedings of the Institution of Civil Engineers - Transport, Vol. 176, No. 6, (2023), 325-336 DOI: 10.1680/jtran.19.00114.
37. Hemagowri, J. and Tamil Selvan, P., "A hybrid evolutionary algorithm of optimized controller placement in sdn environment", Computer Assisted Methods in Engineering and Science, Vol., No., (2023), DOI: 10.24423/cames.498.
38. Abouhabaga, O.O.F., Gadallah, M.H., Kouta, H.K. and Zaghloul, M.A., "Hybrid inner-outer algorithm for solving real-world mechanical optimization problems", Journal of Engineering and Applied Science, Vol. 68, No. 1, (2021), 2 DOI: 10.1186/s44147-021-00004-0.
39. Fasina, E., Sawyerr, B.A., Abdullahi, Y.U. and Oke, S.A., "A comparison of two hybrid optimization techniques: The taguchi-bbd-firefly and the taguchi-regression-firefly methods on the is 2062-e250 steel plates boring problem", Journal of Engineering and Applied Science, Vol. 70, No. 1, (2023), 47 DOI: 10.1186/s44147-023-00215-7.
40. Chen, S. and Zheng, J., "A hybrid grey wolf optimizer for engineering design problems", Journal of Combinatorial Optimization, Vol. 47, No. 5, (2024), 86 DOI: 10.1007/s10878-024-01189-9.
41. Eiben, A.E. and Schippers, C.A., "On evolutionary exploration and exploitation", Fundamenta Informaticae, Vol. 35, No. 1-4, (1998), 35-50.
42. Azizi, M., "Atomic orbital search: A novel metaheuristic algorithm", Applied Mathematical Modelling, Vol. 93, No., (2021), 657-683.
43. Yang, X.-S., "Firefly algorithms for multimodal optimization", in International symposium on stochastic algorithms, Springer. Vol., No., (2009), 169-178.
44. Schmit Jr, L. and Farshi, B., "Some approximation concepts for structural synthesis", AIAA journal, Vol. 12, No. 5, (1974), 692-699.
45. Farshi, B. and Alinia-Ziazi, A., "Sizing optimization of truss structures by method of centers and force formulation", international Journal of Solids and Structures, Vol. 47, No. 18-19, (2010), 2508-2524.
46. Borges, A.d.Á., "Otimização de forma e paramétrica de estruturas treliçadas através dos métodos meta-heurísticos harmony search e firefly algorithm", Vol., No., (2013).
47. Ghaemifard, S. and Ghannadiasl, A., "A comparison of metaheuristic algorithms for structural optimization: Performance and efficiency analysis", Advances in Civil Engineering, Vol. 2024, No. 1, (2024), 2054173 DOI: https://doi.org/10.1155/2024/2054173.
48. Cheng, M.-Y. and Prayogo, D., "Symbiotic organisms search: A new metaheuristic optimization algorithm", Computers & Structures, Vol. 139, No., (2014), 98-112.
49. Bayzidi, H., Talatahari, S., Saraee, M. and Lamarche, C.-P., "Social network search for solving engineering optimization problems", Computational Intelligence and Neuroscience, Vol. 2021, No., (2021), 1-32.
50. Wang, G.G., "Adaptive response surface method using inherited latin hypercube design points", J. Mech. Des., Vol. 125, No. 2, (2003), 210-220.
51. Gandomi, A.H., Yang, X.-S. and Alavi, A.H., "Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems", Engineering with Computers, Vol. 29, No., (2013), 17-35.
52. Akay, B. and Karaboga, D., "Artificial bee colony algorithm for large-scale problems and engineering design optimization", Journal of intelligent manufacturing, Vol. 23, No., (2012), 1001-1014.
53. Sandgren, E., "Nonlinear integer and discrete programming in mechanical design optimization", Vol., No., (1990).
54. Rao, S.S., "Engineering optimization: Theory and practice, John Wiley & Sons, (2019).
55. Bayzidi, H., Talatahari, S., Saraee, M. and Lamarche, C.-P., "Social network search for solving engineering optimization problems", Computational Intelligence and Neuroscience, Vol. 2021, No., (2021), 8548639 DOI: 10.1155/2021/8548639.
56. Gandomi, A.H. and Roke, D.A., "Engineering optimization using interior search algorithm", in 2014 IEEE symposium on swarm intelligence, IEEE. Vol., No., (2014), 1-7.
57. Sadollah, A., Bahreininejad, A., Eskandar, H. and Hamdi, M., "Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems", Applied Soft Computing, Vol. 13, No. 5, (2013), 2592-2612.
58. Kamalinejad, M., Arzani, H. and Kaveh, A., "Quantum evolutionary algorithm with rotational gate and h ϵ-gate updating in real and integer domains for optimization", Acta Mechanica, Vol. 230, No. 8, (2019), 2937-2961.
59. Mezura-Montes, E. and Coello, C.A.C., "An empirical study about the usefulness of evolution strategies to solve constrained optimization problems", International Journal of General Systems, Vol. 37, No. 4, (2008), 443-473.
60. Mirjalili, S., Mirjalili, S.M. and Hatamlou, A., "Multi-verse optimizer: A nature-inspired algorithm for global optimization", Neural Computing and Applications, Vol. 27, No., (2016), 495-513.
61. Kaveh, A. and Talatahari, S., "An improved ant colony optimization for constrained engineering design problems", Engineering Computations, Vol. 27, No. 1, (2010), 155-182.
62. Kaveh, A. and Talatahari, S., "A novel heuristic optimization method: Charged system search", Acta Mechanica, Vol. 213, No. 3-4, (2010), 267-289.
63. Askarzadeh, A., "A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm", Computers & Structures, Vol. 169, No., (2016), 1-12.
64. Kaveh, A., Motie Share, M.A. and Moslehi, M., "Magnetic charged system search: A new meta-heuristic algorithm for optimization", Acta Mechanica, Vol. 224, No. 1, (2013), 85-107.
65. Azizi, M., Talatahari, S., Khodadadi, N. and Sareh, P., "Multiobjective atomic orbital search (moaos) for global and engineering design optimization", IEEE Access, Vol. 10, No., (2022), 67727-67746 DOI: 10.1109/ACCESS.2022.3186696.
66. Elaziz, M.A., Abualigah, L., Yousri, D., Oliva, D., Al-Qaness, M.A., Nadimi-Shahraki, M.H., Ewees, A.A., Lu, S. and Ali Ibrahim, R., "Boosting atomic orbit search using dynamic-based learning for feature selection", Mathematics, Vol. 9, No. 21, (2021), 2786.