Mehdi Hosseinzadeh, Debrina Puspita Andriani, Amir Masoud Rahmani, Farhad Soleimanian Gharehchopogh, Nazanin Rahimi, Adnan Khan, Aso Darwesh, Parisa Khoshvaght, Sadia Din, Thantrira Porntaveetus
Achieving optimal long short-term memory (LSTM) performance is highly dependent on the correct selection of hyperparameters. Incorrectly choosing the number of layers, neurons, learning rate, or batch size can significantly reduce the model’s accuracy or even cause instability during training. In this regard, metaheuristic algorithms play a crucial role in optimizing hyperparameters. One of these algorithms is the sparrow search algorithm (SSA), which is inspired by the social behaviour of sparrows in their search for food and response to threats. Studies have shown that combining SSA with LSTM not only increases the prediction accuracy in time series problems and noisy data, but also significantly reduces the time required for human trial and error in parameter tuning. Another critical strategy for improving LSTM is the use of signal decomposition methods such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complete EEMD with adaptive noise (CEEMDAN), and variational mode decomposition (VMD). These methods decompose non-stationary and noisy data into simpler components and then model each component separately using an LSTM. This paper presents a comprehensive analysis of 237 selected papers published between 2020 and 2025, collected from reputable international sources, including IEEE, Elsevier, Springer, and MDPI. The share of scientific publishers shows that IEEE and MDPI publish more than 60% of the papers. The primary purpose of this paper is to investigate the role of SSA and its improved versions in optimizing LSTM networks, analyzing signal decomposition, and reviewing the wide range of applications of SSA-LSTM in various fields, including industrial, medical, financial, and environmental sectors. © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2026.
Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam; Department of AI, School of Computer Science and Engineering, Galgotias University, Greater Noida, India; Department of Industrial Engineering, Brawijaya University, Malang, 65145, Indonesia; Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, Taiwan; Department of Computer Engineering, Ur., C., Islamic Azad University, Urmia, Iran; Department of Computer Engineering, Yazd University, Yazd, Iran; Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan; Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, Rajpura, 140401, India; Jadara University Research Center, Jadara University, Irbid, Jordan; Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India; School of Engineering & Technology, Duy Tan University, Da Nang, Viet Nam; Department of Computer Engineering, Gachon University, Seongnam-si, South Korea; Center of Excellence in Precision Medicine and Digital Health, Department of Physiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, 10330, Thailand
