Boosted local dimensional mutation and all-dimensional neighborhood slime mould algorithm for feature selection

Zhou, Xinsen; Chen, Yi; Wu, Zongda; Heidari, Ali Asghar; Chen, Huiling; Alabdulkreem, Eatedal; Escorcia‐Gutierrez, José; Wang, Xianchuan

doi:10.1016/j.neucom.2023.126467

Cited by 21 publications

(7 citation statements)

References 124 publications

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“…Yuanfei Wei et al [80] added a variable NS strategy in the proposed EOSMA to increase the exploitation outcomes. Zhou X et al [81] introduced an all-dimensional neighborhood search strategy for an SMA (LASMA) to explore the search space more effectively.…”

Section: Neighborhood Search (Ns)mentioning

confidence: 99%

Advances in Slime Mould Algorithm: A Comprehensive Survey

Wei,

Othman,

Daud

et al. 2024

Biomimetics

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The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining.

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Section: Neighborhood Search (Ns)mentioning

confidence: 99%

Advances in Slime Mould Algorithm: A Comprehensive Survey

Wei,

Othman,

Daud

et al. 2024

Biomimetics

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“…Mehmood combines Archimedes' optimization algorithm (AOA) with chaotic maps to optimize complex engineering problems [36]. Zhou introduces LASMA, a local dimensional mutation strategy, and an all-dimensional neighborhood search strategy for the slime mould algorithm (SMA) to improve the algorithm's exploration and exploitation abilities [37]. These enhancements aim to address the limitations of the original algorithms, such as poor exploitation ability and susceptibility to local optima.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Dong introduces the boosting kernel search optimizer (BKSO) to solve the combined economic emission dispatch (CEED) problem in power systems [43]. Zhou presents a boosted atomic search optimization (ASO) with a new anti-sine-cosine mechanism (ASCASO) for parameter estimation of photovoltaic (PV) models [37]. These studies illustrate the versatility of optimization algorithms in optimizing engineering designs and enhancing energy conversion efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Advanced RIME architecture for global optimization and feature selection

Abu Khurma,

Braik,

Alzaqebah

et al. 2024

J Big Data

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The article introduces an innovative approach to global optimization and feature selection (FS) using the RIME algorithm, inspired by RIME-ice formation. The RIME algorithm employs a soft-RIME search strategy and a hard-RIME puncture mechanism, along with an improved positive greedy selection mechanism, to resist getting trapped in local optima and enhance its overall search capabilities. The article also introduces Binary modified RIME (mRIME), a binary adaptation of the RIME algorithm to address the unique challenges posed by FS problems, which typically involve binary search spaces. Four different types of transfer functions (TFs) were selected for FS issues, and their efficacy was investigated for global optimization using CEC2011 and CEC2017 and FS tasks related to disease diagnosis. The results of the proposed mRIME were tested on ten reliable optimization algorithms. The advanced RIME architecture demonstrated superior performance in global optimization and FS tasks, providing an effective solution to complex optimization problems in various domains.

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“…In fact, the IoMT blends traditional IoT's dynamic, flexible, and scalability features with the dependability and safety of conventional medical devices 15 – 17 . By managing multiple gadgets deployed for numerous patients and being sufficiently generic to handle a variety of conditions calling for numerous monitoring and controlling demands, it has the potential to overcome the problem of aging and chronic diseases 18 .…”

Section: Introductionmentioning

confidence: 99%

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

Nandagopal,

Seerangan,

Govindaraju

et al. 2024

Sci Rep

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In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients’ medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier’s error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL’s effectiveness and efficiency in identifying diseases is evaluated and compared.

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Boosted local dimensional mutation and all-dimensional neighborhood slime mould algorithm for feature selection

Cited by 21 publications

References 124 publications

Advances in Slime Mould Algorithm: A Comprehensive Survey

Advances in Slime Mould Algorithm: A Comprehensive Survey

Advanced RIME architecture for global optimization and feature selection

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

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