A comprehensive overview of PSO-LSTM approaches: Applications, analytical insights, and future opportunities
The combination of particle swarm optimization algorithm with long short-term memory has led to new horizons in data analysis and solving complex problems. The particle swarm optimization has been used to solve complex and hard optimization problems since 1995 and has gained extraordinary popularit...
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| Format: | Article |
| Language: | en |
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Springer Science and Business Media B.V.
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29549/2/02310051120251755442425.pdf http://eprints.utem.edu.my/id/eprint/29549/ https://link.springer.com/article/10.1007/s11831-025-10445-y https://doi.org/10.1007/s11831-025-10445-y |
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| author | Hosseinzadeh, Mehdi Tanveer, Jawad Rahmani, Amir Masoud Gharehchopogh, Farhad Soleimanian Mohd Yusof, Norfadzlia Khoshvaght, Parisa Zhe, Liu Porntaveetus, Thantrira Lee, Sang-Woong |
| author_facet | Hosseinzadeh, Mehdi Tanveer, Jawad Rahmani, Amir Masoud Gharehchopogh, Farhad Soleimanian Mohd Yusof, Norfadzlia Khoshvaght, Parisa Zhe, Liu Porntaveetus, Thantrira Lee, Sang-Woong |
| author_sort | Hosseinzadeh, Mehdi |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The combination of particle swarm optimization algorithm with long short-term memory has led to new horizons in data
analysis and solving complex problems. The particle swarm optimization has been used to solve complex and hard optimization problems since 1995 and has gained extraordinary popularity among researchers. The combination of particle swarm optimization-long short-term memory is carried out in order to simultaneously utilize the global search capability and convergence ability of the particle swarm optimization to optimize long short-term memory in modeling long-term temporal dependencies. The goal of the particle swarm optimization-long short-term memory model is to increase the prediction accuracy and reduce the error in solving complex and dynamic real-world problems. In this paper, a comprehensive and structured look at the scientific literature of particle swarm optimization-long short-term memory models between 2019 and June 2025 has been conducted. By categorizing the articles according to the publication date and place of publication, it was found that prominent publishers such as MDPI, Springer, Elsevier, and IEEE played a major role in the publication of particle swarm optimization-long short-term memory models in 2024. This paper aims to provide a clear overview of the potential applications of particle swarm optimization-long short-term memory in various domains. particle swarm optimization-long short-term memory has been widely used in engineering systems design, time series forecasting, and the oil and gas industry. This paper analyses the strengths and weaknesses, as well as the challenges of complexity in hybrid architectures, and issues related to scalability and optimization. Finally, future directions are proposed with an
emphasis on performance enhancement and development of adaptable solutions for real-world problems. |
| format | Article |
| id | my.utem.eprints-29549 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2025 |
| publisher | Springer Science and Business Media B.V. |
| record_format | eprints |
| spelling | my.utem.eprints-295492026-02-23T04:09:59Z http://eprints.utem.edu.my/id/eprint/29549/ A comprehensive overview of PSO-LSTM approaches: Applications, analytical insights, and future opportunities Hosseinzadeh, Mehdi Tanveer, Jawad Rahmani, Amir Masoud Gharehchopogh, Farhad Soleimanian Mohd Yusof, Norfadzlia Khoshvaght, Parisa Zhe, Liu Porntaveetus, Thantrira Lee, Sang-Woong The combination of particle swarm optimization algorithm with long short-term memory has led to new horizons in data analysis and solving complex problems. The particle swarm optimization has been used to solve complex and hard optimization problems since 1995 and has gained extraordinary popularity among researchers. The combination of particle swarm optimization-long short-term memory is carried out in order to simultaneously utilize the global search capability and convergence ability of the particle swarm optimization to optimize long short-term memory in modeling long-term temporal dependencies. The goal of the particle swarm optimization-long short-term memory model is to increase the prediction accuracy and reduce the error in solving complex and dynamic real-world problems. In this paper, a comprehensive and structured look at the scientific literature of particle swarm optimization-long short-term memory models between 2019 and June 2025 has been conducted. By categorizing the articles according to the publication date and place of publication, it was found that prominent publishers such as MDPI, Springer, Elsevier, and IEEE played a major role in the publication of particle swarm optimization-long short-term memory models in 2024. This paper aims to provide a clear overview of the potential applications of particle swarm optimization-long short-term memory in various domains. particle swarm optimization-long short-term memory has been widely used in engineering systems design, time series forecasting, and the oil and gas industry. This paper analyses the strengths and weaknesses, as well as the challenges of complexity in hybrid architectures, and issues related to scalability and optimization. Finally, future directions are proposed with an emphasis on performance enhancement and development of adaptable solutions for real-world problems. Springer Science and Business Media B.V. 2025 Article PeerReviewed text en cc_by_4 http://eprints.utem.edu.my/id/eprint/29549/2/02310051120251755442425.pdf Hosseinzadeh, Mehdi and Tanveer, Jawad and Rahmani, Amir Masoud and Gharehchopogh, Farhad Soleimanian and Mohd Yusof, Norfadzlia and Khoshvaght, Parisa and Zhe, Liu and Porntaveetus, Thantrira and Lee, Sang-Woong (2025) A comprehensive overview of PSO-LSTM approaches: Applications, analytical insights, and future opportunities. Archives of Computational Methods in Engineering. pp. 1-46. ISSN 1134-3060 https://link.springer.com/article/10.1007/s11831-025-10445-y https://doi.org/10.1007/s11831-025-10445-y |
| spellingShingle | Hosseinzadeh, Mehdi Tanveer, Jawad Rahmani, Amir Masoud Gharehchopogh, Farhad Soleimanian Mohd Yusof, Norfadzlia Khoshvaght, Parisa Zhe, Liu Porntaveetus, Thantrira Lee, Sang-Woong A comprehensive overview of PSO-LSTM approaches: Applications, analytical insights, and future opportunities |
| title | A comprehensive overview of PSO-LSTM approaches: Applications, analytical insights, and future opportunities |
| title_full | A comprehensive overview of PSO-LSTM approaches: Applications, analytical insights, and future opportunities |
| title_fullStr | A comprehensive overview of PSO-LSTM approaches: Applications, analytical insights, and future opportunities |
| title_full_unstemmed | A comprehensive overview of PSO-LSTM approaches: Applications, analytical insights, and future opportunities |
| title_short | A comprehensive overview of PSO-LSTM approaches: Applications, analytical insights, and future opportunities |
| title_sort | comprehensive overview of pso-lstm approaches: applications, analytical insights, and future opportunities |
| url | http://eprints.utem.edu.my/id/eprint/29549/2/02310051120251755442425.pdf http://eprints.utem.edu.my/id/eprint/29549/ https://link.springer.com/article/10.1007/s11831-025-10445-y https://doi.org/10.1007/s11831-025-10445-y |
| url_provider | http://eprints.utem.edu.my/ |