A review on research trends in using cuckoo search algorithm: applications and open research challenges

Safdar, Kalsoom; Abdul Rani, Khairul Najmy; Rahim, Hasliza A.; Rosli, Siti Julia; Jamlos, Mohd Aminudin

doi:10.15199/48.2023.05.04

Artykuł - szczegóły

Tytuł artykułu

A review on research trends in using cuckoo search algorithm: applications and open research challenges

Autorzy

Safdar Kalsoom , Abdul Rani Khairul Najmy , Rahim Hasliza A. , Rosli Siti Julia , Jamlos Mohd Aminudin

Wybrane pełne teksty z tego czasopisma

http://pe.org.pl/

Identyfikatory

DOI

10.15199/48.2023.05.04

Warianty tytułu

Przegląd trendów badawczych w używaniu algorytmu cuckpooz: aplikacje i otwarte wyzwania badawcze

Języki publikacji

Abstrakty

This paper provides an exclusive understanding of the Cuckoo Search Algorithm (CSA) using a comprehensive review for various optimization problems. CSA is a swarm-based nature inspired, intelligent and metaheuristic approach, which is used to solve complex, single or multi objective optimization problems to provide better solutions with maximum or minimum parameters. It was developed in 2009 by Yang and Deb to emulate the breeding behaviour of cuckoos. Since CSA provides promising solutions to solve real world optimization problems, in recent years there have been introduced several new modified and hybridized CSAs using for different applications. In this regard this article provides a comprehensive survey including recent trends, modifications, open research challenges, applications, and related taxonomies for various optimization problems. The literature of this reviewed paper belongs to the domains of engineering, optimization, and pattern recognition. The aim of this review paper is to provide a detailed overview regarding CSA for possible future directions using the recent contributions.

Ten artykuł zapewnia wyłączne zrozumienie algorytmu przeszukiwania kukułki (CSA) za pomocą kompleksowego przeglądu różnych problemów optymalizacyjnych. CSA to oparte na roju, inteligentne i metaheurystyczne podejście inspirowane naturą, które służy do rozwiązywania złożonych, jedno- lub wielocelowych problemów optymalizacyjnych w celu zapewnienia lepszych rozwiązań z maksymalnymi lub minimalnymi parametrami. Został opracowany w 2009 roku przez Yang i Deb, aby naśladować zachowanie hodowlane kukułek. Ponieważ CSA zapewnia obiecujące rozwiązania do rozwiązywania rzeczywistych problemów optymalizacyjnych, w ostatnich latach wprowadzono kilka nowych zmodyfikowanych i hybrydowych CSA używanych do różnych zastosowań. Pod tym względem ten artykuł zawiera obszerną ankietę, w tym najnowsze trendy, modyfikacje, otwarte wyzwania badawcze, aplikacje i powiązane taksonomie dla różnych problemów optymalizacyjnych. Literatura tego recenzowanego artykułu należy do dziedzin inżynierii, optymalizacji i rozpoznawania wzorców. Celem tego artykułu przeglądowego jest przedstawienie szczegółowego przeglądu dotyczącego CSA dla możliwych przyszłych kierunków z wykorzystaniem ostatnich wkładów.

Słowa kluczowe

cuckoo search algorithm swarm intelligence algorithm metaheuristic optimization problem local trap problem

algorytm Cuckoo optymalizacja CSA

Wydawca

Wydawnictwo SIGMA-NOT

Czasopismo

Przegląd Elektrotechniczny

Rocznik

2023

Tom

R. 99, nr 5

Strony

18--24

Opis fizyczny

Bibliogr. 45 poz., rys., tab.

Twórcy

autor

Safdar Kalsoom

Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia
University of Jhang, Jhang, Pakistan

autor

Abdul Rani Khairul Najmy

khairulnajmy@unimap.edu.my

Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia
Advanced Communication Engineering, Centre of Excellence (ACE), UniMAP

https://orcid.org/0000-0001-9566-0834

autor

Rahim Hasliza A.

Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia
Advanced Communication Engineering, Centre of Excellence (ACE), UniMAP

autor

Rosli Siti Julia

Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia
Advanced Communication Engineering, Centre of Excellence (ACE), UniMAP

autor

Jamlos Mohd Aminudin

Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia
Advanced Communication Engineering, Centre of Excellence (ACE), UniMAP

Bibliografia

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5. Shehab, M., Mashal, I., Momani, Z., Shambour, M.K.Y., AL-Badareen, A., Al-Dabet, S., Bataina, N., Alsoud, A.R. and Abualigah, L., Harris Hawks Optimization Algorithm: Variants and Applications. Archives of Computational Methods in Engineering, 2022: p. 1-25.
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11. Houssein, E.H., Helmy, B.E.D., Elngar, A.A., Abdelminaam, D.S. and Shaban, H., An improved tunicate swarm algorithm for global optimization and image segmentation. IEEE Access, 2021. 9: p. 56066-56092.
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13. Zhang, H., Cai, Z., Ye, X., Wang, M., Kuang, F., Chen, H., Li, C. and Li, Y., A multi-strategy enhanced salp swarm algorithm for global optimization. Engineering with Computers, 2020: p. 1-27.
14. Abdul Rani, K.N., Abdulmalek, M., A Rahim, H., Siew Chin, N. and Abd Wahab, A., Hybridization of strength pareto multiobjective optimization with modified cuckoo search algorithm for rectangular array. Scientific reports, 2017. 7(1): p. 1-19.
15. Rosli, S.J., Rahim, H.A., Rani, K.N.A., Ngadiran, R., Mustafa, W.A., Jusoh, M., Yasin, M.N.M., Sabapathy, T., Abdulmalek, M., Ariffin, W.S.F.W. and Alkhayyat, A., A Hybrid Modified Sine Cosine Algorithm Using Inverse Filtering and Clipping Methods for Low Autocorrelation Binary Sequences. CMC COMPUTERS MATERIALS & CONTINUA, 2022. 71(2): p. 3533-3556.
16. Deng, W., Shang, S., Cai, X., Zhao, H., Song, Y. and Xu, J., An improved differential evolution algorithm and its application in optimization problem. Soft Computing, 2021. 25(7): p. 5277-5298.
17. Llorente-Peralta, C.E., Cruz-Reyes, L. and Espín-Andrade, R.A., Knowledge discovery using an evolutionary algorithm and compensatory fuzzy logic, in Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications. 2021, Springer. p. 363-383.
18. Yang, J., Hu, Y., Zhang, K. and Wu, Y., An improved evolution algorithm using population competition genetic algorithm and self-correction BP neural network based on fitness landscape.Soft Computing, 2021. 25(3): p. 1751-1776.
19. Xiang, X., Tian, Y., Zhang, X., Xiao, J. and Jin, Y., A pairwise proximity learning-based ant colony algorithm for dynamic vehicle routing problems. IEEE Transactions on Intelligent Transportation Systems, 2021.
20. Hafeez, M.A., Rashid, M., Tariq, H., Abideen, Z.U., Alotaibi, S.S. and Sinky, M.H., Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm.Applied Sciences, 2021. 11(15): p. 6728.
21. Song, B., Wang, Z. and Zou, l., An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve. Applied Soft Computing, 2021. 100: p. 106960.
22. Abdel-Basset, M., Ding, W., and El-Shahat, D., A hybrid Harris Hawks optimization algorithm with simulated annealing for feature selection. Artificial Intelligence Review, 2021. 54(1): p. 593-637.
23. Rani, K.A. and Malek, F., Preliminary study on cuckoo search parameters for symmetric linear array geometry synthesis. in TENCON 2011-2011 IEEE region 10 conference. 2011. IEEE.
24. Da, S., Li, X., Han, F. and Li, H., An improved CS-LS hybrid algorithm on microseismic source location. Systems Science & Control Engineering, 2021. 9(1): p. 467-478.
25. Alkhateeb, F. and Abed-Alguni, B.H., A hybrid cuckoo search and simulated annealing algorithm. Journal of Intelligent Systems, 2019. 28(4): p. 683-698.
26. Shehab, M., Khader A.T, and Al-Betar M.A., A survey on applications and variants of the cuckoo search algorithm.Applied Soft Computing, 2017. 61: p. 1041-1059.
27. Cuong-Le, T., Minh, H.L., Khatir, S., Wahab, M.A., Tran, M.T. and Mirjalili, S., A novel version of Cuckoo search algorithm for solving optimization problems. Expert Systems with Applications, 2021. 186: p. 115669.
28. Iswanathan, G.M., Buldyrev, S.V., Havlin, S., Da Luz, M.G.E., Raposo, E.P. and Stanley, H.E., Optimizing the success of random searches. nature, 1999. 401(6756): p. 911-914.
29. Viswanathan, G.M., Bartumeus, F., Buldyrev, S.V., Catalan, J., Fulco, U.L., Havlin, S., Da Luz, M.G.E., Lyra, M.L., Raposo, E.P. and Stanley, H.E., Lévy flight random searches in biological phenomena. Physica A: Statistical Mechanics and Its Applications, 2002. 314(1-4): p. 208-213.
30. Sharma, K., Singh S., and Doriya R., Optimized cuckoo search algorithm using tournament selection function for robot path planning. International Journal of Advanced Robotic Systems, 2021. 18(3): p. 1729881421996136.
31. Chatterjee, S., Dzitac, S., Sen, S., Rohatinovici, N.C., Dey, N., Ashour, A.S. and Balas, V.E., Hybrid modified Cuckoo Search Neural Network in chronic kidney disease classification. in 2017 14th International Conference on Engineering of Modern Electric Systems (EMES). 2017. IEEE.
32. Chen, G., Lu, Z., Zhang, Z. and Sun, Z., Research on hybrid modified cuckoo search algorithm for optimal reactive power dispatch problem. IAENG International Journal of Computer Science, 2018. 45(2): p. 328-339.
33. Cui, Z., Zhang, M., Wang, H., Cai, X. and Zhang, W., A hybrid many-objective cuckoo search algorithm. soft computing, 2019. 23(21): p. 10681-10697.
34. Yang, Q., Fu, Y., and Zhang, J., Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search. Neural Computing and Applications, 2021. 33(12): p. 6487-6509.
35. Yousri, D., Abd Elaziz, M., Abualigah, L., Oliva, D., Al-Qaness, M.A. and Ewees, A.A., COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions. Applied Soft Computing, 2021. 101: p. 107052.
36. Pandey, A.C., Rajpoot, D.S., and Saraswat, M., Twitter sentiment analysis using hybrid cuckoo search method.Information Processing & Management, 2017. 53(4): p. 764-779.
37. Aziz, M.A.E. and Hassanien, A.E., Modified cuckoo search algorithm with rough sets for feature selection. Neural Computing and Applications, 2018. 29(4): p. 925-934.
38. Liu, L., Liu, X., Wang, N. and Zou, P., Modified cuckoo search algorithm with variational parameters and logistic map.Algorithms, 2018. 11(3): p. 30.
39. Chi, R., Su, Y.X., Zhang, D.H., Chi, X.X. and Zhang, H.J., A hybridization of cuckoo search and particle swarm optimization for solving optimization problems. Neural Computing and Applications, 2019. 31(1): p. 653-670.
40. Abd, El Aziz, M. and Hassanien, A.E., Modified cuckoo search algorithm with rough sets for feature selection. Neural Computing and Applications, 2018. 29(4): p. 925-934.
41. Dhal, K.G., Das, A., Ray, S. and Das, S., A clustering based classification approach based on modified cuckoo search algorithm. Pattern Recognition and Image Analysis, 2019. 29(3): p. 344-359.
42. Zhao, J., Liu, S., Zhou, M., Guo, X. and Qi, L., Modified cuckoo search algorithm to solve economic power dispatch optimization problems. IEEE/CAA Journal of Automatica Sinica, 2018. 5(4): p. 794-806.
43. Gu, W., Li, Z., Dai, M. and Yuan, M., An energy-efficient multi objective permutation flow shop scheduling problem using an improved hybrid cuckoo search algorithm. Advances in Mechanical Engineering, 2021. 13(6): p. 16878140211023603.
44. García, J., Yepes, V. and Martí, J.V., A hybrid k-means cuckoo search algorithm applied to the counterfort retaining walls problem. Mathematics, 2020. 8(4): p. 555.
45. Boumedine, N. and Bouroubi, S., Protein folding simulations in the hydrophobic-polar model using a hybrid cuckoo search algorithm. arXiv preprint arXiv:2105.13226, 2021.

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Bibliografia

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