The Position of Metaheuristic Optimization Methods in Psychiatric Practice: A Bibliometric Analysis

Abstract

Mental diseases affect billions of people worldwide to varying degrees. This poses a risk to public health. Although the levels and effects of diseases vary, in general, mental diseases prevent people from having a healthy relationship with themselves and their environment and prevent them from living their daily lives efficiently. Individuals who move away from the comfort of daily life face many difficulties in their social and professional lives. People with long-term mental disorders may also experience physiological disorders due to stress. When this situation is not treated and prevented, it becomes a public health problem. Therefore, centers such as health institutions, research centers, and universities operating in the field of health carry out many studies to detect and treat mental disorders. These methods include traditional methods, but today, they also include artificial intelligence-supported methods. AI-powered methods are used for early diagnosis and treatment of mental illnesses by analyzing large amounts of mental illness data of various types. Researchers are also using metaheuristic optimization algorithms to improve the performance of these methods. These optimization methods, which seek fast and high-performance solutions to very complex nonlinear problems, are increasing the success of machine learning and deep learning methods. Nonetheless, there is no distinct bibliometric analysis demonstrating the quantity and quality of research in this domain for scholars. This study aims to fill this gap in this field and to be a preliminary guide for researchers.

Keywords

• Bibliometric analysis
• Metaheuristic optimization
• Mental diseases
• Mental disorders
• Machine learning
• Deep learning

Metasezgisel Optimizasyon Yöntemlerinin Psikiyatrik Uygulamalardaki Yeri: Bibliyometrik Bir Analiz

Abstract

Mental hastalıklar dünya çapında milyarlarca insanı çeşitli seviyelerde etkilemektedir. Bu durum toplum sağlığı açısından bir risk teşkil etmektedir. Hastalıkların seviyeleri ve etkileri değişiklik gösterse de genel olarak mental rahatsızlıklar kişilerin kendileri ve çevreleri ile sağlıklı bir ilişki kurmalarını engelleyerek günlük yaşamlarını verimli bir şekilde geçirmelerini engeller. Günlük yaşam konforundan uzaklaşan bireyler sosyal ve iş yaşamlarında da birçok zorluk ile karşılaşırlar. Uzun süreli mental rahatsızlık yaşayan insanlarda strese bağlı olarak fizyolojik rahatsızlıklar da görülebilir. Bu durum tedavi edilmediği ve önlenmediği zaman toplum sağlığı problemine dönüşür. Bundan dolayı sağlık alanında faaliyet gösteren sağlık kuruluşları, araştırma merkezleri ve üniversiteler gibi merkezler mental hastalıkların tespiti ve tedavisi için birçok çalışma yapmaktadır. Bu yöntemler geleneksel yöntemleri içermekle birlikte günümüzde yapay zeka destekli yöntemleri de kapsamaktadır. Yapay zeka destekli yöntemler çeşitli türdeki büyük miktarda mental hastalık verisini analiz ederek mental hastalıkların erken teşhisi ve tedavisinde kullanılmaktadır. Araştırmacılar bu yöntemlerin başarılarını geliştirmek için metasezgisel optimizasyon algoritmalarını da kullanmaktadır. Çok karmaşık nonlinear problemlere hızlı ve yüksek başarımlı çözümler arayan bu optimizasyon yöntemleri makine öğrenmesi ve derin öğrenme yöntemlerinin başarılarını arttırmaktadır. Fakat araştırmacılar için bu alanda yapılan çalışmaların sayısını ve niteliğini gösteren ayrı bir bibliyometrik bir çalışma yoktur. Bu çalışma bu alanda oluşan bu boşluğu doldurarak araştırmacılar için ön açısı bir rehber olmayı hedeflemektedir.

Keywords

• Bibliyometrik analiz
• Metasezgisel optimizasyon
• Mental hastalıklar
• Mental bozukluklar
• Makine öğrenmesi
• Derin öğrenme

References

1. Abdulsalam KO, Torera MN, Dawuda AA, Musa S, Olumide OO. 2025. Emerging trends and insights: A comprehensive bibliometric analysis of artificial intelligence applications in healthcare and psychology. J Adv Math Comput Sci, 40(1): 54-71.
2. Abualigah L, Abd Elaziz M, Sumari P, Geem ZW, Gandomi AH. 2022. Reptile search algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Syst Appl, 191: 116158.
3. American Psychiatric Association. 2022. Warning signs of mental illness. URL: https://www.psychiatry.org/patients-families/warning-signs-of-mental-illness (accessed date: March 20, 2025).
4. Bandaru S, Deb K. 2016. Metaheuristic techniques. In: Ravindran AR, editor. Decision sciences. CRC Press, Boca Raton, FL, USA, pp: 709–766.
5. Berta A, Ángel CM, Clara GS, Rubén H. 2022. A bibliometric analysis of 10 years of research on symptom networks in psychopathology and mental health. Psychiatry Res, 308: 114380.
6. Caraballo-Arias Y, Feola D, Milani S. 2024. The science behind happiness at work. Curr Opin Epidemiol Public Health, 3(1): 11-24.
7. Carvalho AF, Firth J, Vieta E. 2020. Bipolar disorder. N Engl J Med, 383(1): 58-66.
8. Chen J, Yuan D, Dong R, Cai J, Ai Z, Zhou S. 2024. Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis. Front Psychol, 15: 1375294.

9. Craske MG, Rauch SL, Ursano R, Prenoveau J, Pine DS, Zinbarg RE. 2011. What is an anxiety disorder? Focus, 9(3): 369-388.
10. Dalle Grave R. 2011. Eating disorders: progress and challenges. Eur J Intern Med, 22(2): 153-160.
11. Disruptive Behaviour and Dissocial Disorders. 2022. Disruptive behavior disorders. URL: https://libguides.spsd.org/dissocial (accessed date: March 3, 2025).
12. Dokeroglu T, Sevinc E, Kucukyilmaz T, Cosar A. 2019. A survey on new generation metaheuristic algorithms. Comput Ind Eng, 137: 106040.
13. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. 2021. How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res, 133: 285-296.
14. Dorigo M, Blum C. 2005. Ant colony optimization theory: A survey. Theor Comput Sci, 344(2–3): 243–278.
15. Evans SC, de la Peña FR, Matthys W, Lochman JE. 2024. Disruptive behavior and dissocial disorders and attention deficit hyperactivity disorder. In: Reed GM, Ritchie PLJ, Maercker A, Rebello TJ, editors. A psychological approach to diagnosis: Using the ICD-11 as a framework. American Psychological Association, Washington, DC, USA, pp: 251–268.
16. Ezugwu AE, Shukla AK, Nath R, Akinyelu AA, Agushaka JO, Chiroma H, Muhuri PK. 2021. Metaheuristics: A comprehensive overview and classification along with bibliometric analysis. Artif Intell Rev, 54: 4237-4316.
17. Fu L, Aximu R, Zhao G, Chen Y, Sun Z, Xue H, Liu F. 2024. Mapping the landscape: A bibliometric analysis of resting-state fMRI research on schizophrenia over the past 25 years. Schizophr, 10(1): 35.
18. Glover F, Laguna M. 1998. Tabu search. In: Handbook of combinatorial optimization. Springer, Boston, MA, USA, pp: 2093–2229.
19. Gogna A, Tayal A. 2013. Metaheuristics: Review and application. J Exp Theor Artif Intell, 25(4): 503-526.
20. Goldberg DE. 1989. Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Boston, MA, USA, pp: 412.
21. Guo Z, Zhang Y, Liu Q. 2023. Bibliometric and visualization analysis of research trend in mental health problems of children and adolescents during the COVID-19 pandemic. Front Public Health, 10: 1040676.
22. Halim AH, Ismail I, Das S. 2021. Performance assessment of the metaheuristic optimization algorithms: An exhaustive review. Artif Intell Rev, 54(3): 2323-2409.
23. Hendy H, Irawan MI, Mukhlash I, Setumin S. 2023. A bibliometric analysis of metaheuristic research and its applications. Register J Ilm Teknol Sist Inform, 9(1): 1-17.
24. Jia H, Rao H, Wen C, Mirjalili S. 2023. Crayfish optimization algorithm. Artif Intell Rev, 56(Suppl 2): 1919-1979.
25. Kala P, Joshi P, Joshi M, Agarwal S, Yadav LK. 2021. Applications of metaheuristics in power electronics. In: Tiwari A, Singh MK, Gupta SK, editors. Recent advances in power electronics and drives. Springer, Singapore, pp: 155–186.
26. Kalra S, Aggarwal G, Pawaria S, Yadav S, Ajmera P. 2023. Psychological health of postmenopausal women: A bibliometric analysis in the recent decade. Climacteric, 26(5): 428-436.
27. Karaboga D. 2005. An idea based on honey bee swarm for numerical optimization. Erciyes Univ Press, Kayseri, Türkiye, pp: 10.
28. Kareem SW, Ali KWH, Askar S, Xoshaba FS, Hawezi R. 2022. Metaheuristic algorithms in optimization and its application: A review. J Adv Res Electr Eng, 6(1).
29. Katon WJ. 2006. Panic disorder. N Engl J Med, 354(22): 2360-2367.
30. Khishe M, Mosavi MR. 2020. Chimp optimization algorithm. Expert Syst Appl, 149: 113338.
31. Kim J, Lee J, Park E, Han J. 2020. A deep learning model for detecting mental illness from user content on social media. Sci Rep, 10(1): 11846.
32. Kim J, Lee D, Park E. 2021. Machine learning for mental health in social media: Bibliometric study. J Med Internet Res, 23(3): e24870.
33. Kozakijevic S, Jovanovic L, Tedic S, Jankovic N, Zivkovic M, Bacanin N. 2025. Modified metaheuristic optimization algorithm for hyperparameter tuning in mental health applications. In: Proc 6th Int Conf Mobile Comput Sustain Inform (ICMCSI), IEEE, pp: 948–953.
34. Lu E, Zhang D, Han M, Wang S, He L. 2025. The application of artificial intelligence in insomnia, anxiety, and depression: A bibliometric analysis. Digit Health, 11: 20552076251324456.
35. Maghfiroh NH, Sukmawati B. 2024. Bibliometric analysis: Mental health problems in a decade. West Sci Soc Humanit Stud, 2(5): 706–715.
36. Metse AP, Wiggers JH, Wye PM, Wolfenden L, Prochaska JJ, Stockings EA, Bowman JA. 2016. Smoking and mental illness: A bibliometric analysis of research output over time. Nicotine Tob Res, ntw249.
37. Mirjalili S, Lewis A. 2016. The whale optimization algorithm. Adv Eng Softw, 95: 51–67.
38. Mirjalili S, Mirjalili SM, Lewis A. 2014. Grey wolf optimizer. Adv Eng Softw, 69: 46–61.
39. Mladenović N, Hansen P. 1997. Variable neighborhood search. Comput Oper Res, 24(11): 1097-1100.
40. Muchemwa M, Sodi T, Themane M. 2024. A bibliometric analysis of mental health research in places of higher learning in sub-Saharan Africa. Soc Sci Humanit Open, 10: 100950.
41. Nassef AM, Abdelkareem MA, Maghrabie HM, Baroutaji A. 2024. Hybrid metaheuristic algorithms: A recent comprehensive review with bibliometric analysis. Int J Electr Comput Eng, 14(6).
42. National Institute of Mental Health. 2024. Mental illness. URL: https://www.nimh.nih.gov/health/statistics/mental-illness (accessed date: March 15, 2025).
43. Papadimitrakis M, Giamarelos N, Stogiannos M, Zois EN, Livanos NI, Alexandridis A. 2021. Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications. Renew Sustain Energy Rev, 145: 111072.
44. Passas I. 2024. Bibliometric analysis: The main steps. Encycl, 4(2).
45. Paykel ES. 2008. Basic concepts of depression. Dialogues Clin Neurosci, 10(3): 279-289.
46. Rajkishan SS, Meitei AJ, Singh A. 2024. Role of AI/ML in the study of mental health problems of the students: A bibliometric study. Int J Syst Assur Eng Manag, 15(5): 1615-1637.
47. Rajwar K, Deep K, Das S. 2023. An exhaustive review of the metaheuristic algorithms for search and optimization: Taxonomy, applications, and open challenges. Artif Intell Rev, 56(11): 13187-13257.
48. Rashedi E, Nezamabadi-Pour H, Saryazdi S. 2009. GSA: A gravitational search algorithm. Inf Sci, 179(13): 2232–2248.
49. Schultz SH, North SW, Shields CG. 2007. Schizophrenia: A review. Am Fam Physician, 75(12): 1821-1829.
50. Sharma CM, Chariar VM. 2024. Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023. Heliyon.
51. Shuai HH, Shen CY, Yang DN, Lan YFC, Lee WC, Yu PS, Chen MS. 2017. A comprehensive study on social network mental disorders detection via online social media mining. IEEE Trans Knowl Data Eng, 30(7): 1212-1225.
52. Song S, Yu W, Li S, Sun W, Fu J, Cheng Q. 2024. A bibliometric analysis of mental health among high school students. Front Psychiatry, 15: 1433897.
53. Storn R, Price K. 1997. Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim, 11(4): 341–359.
54. Tomar V, Bansal M, Singh P. 2024. Metaheuristic algorithms for optimization: A brief review. Eng Proc, 59(1): 238.
55. Tran BX, McIntyre RS, Latkin CA, Phan HT, Vu GT, Nguyen HLT, Ho RC. 2019. The current research landscape on the artificial intelligence application in the management of depressive disorders: A bibliometric analysis. Int J Environ Res Public Health, 16(12): 2150.
56. VOSviewer. 2025. Welcome to VOSviewer. URL: https://www.vosviewer.com/ (accessed date: March 13, 2025).
57. Wang L, Ye L, Jin Y, Pan X, Wang X. 2024. A bibliometric analysis of the knowledge related to mental health during and post COVID-19 pandemic. Front Psychol, 15: 1411340.
58. Web of Science. 2025. URL: https://www.webofscience.com/wos/woscc/basic-search (accessed date: March 14, 2025).
59. Wei Y, Qiqiang L. 2004. Survey on particle swarm optimization algorithm. Eng Sci, 5(5): 87–94.
60. World Health Organization. 2022. Mental disorders. URL: https://www.who.int/news-room/fact-sheets/detail/mental-disorders (accessed date: March 5, 2025).
61. Yang XS, He X. 2013. Firefly algorithm: Recent advances and applications. Int J Swarm Intell, 1(1): 36-50.
62. Yehuda R, Hoge CW, McFarlane AC, Vermetten E, Lanius RA, Nievergelt CM, Hyman SE. 2015. Post-traumatic stress disorder. Nat Rev Dis Primers, 1(1): 1-22.