Beetle Antennae Search Strategy for Neural Network Model Optimization with Application to Glomerular Filtration Rate Estimation

Wu, Qing; Chen, Zeyu; Chen, Dechao; Li, Shuai

doi:10.1007/s11063-021-10462-5

Cited by 6 publications

(4 citation statements)

References 63 publications

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“…By integrating historical information and dynamically updating step sizes and search ranges, this algorithm substantially improves optimization speed, control efficiency, and effectively minimizes error and overshoot. In marine engineering, Xie et al devised a novel speed control method for marine diesel engines (Wu et al 2021). This method adeptly manages load and model parameter perturbations in diesel engines by combining an adaptive state compensation extended state observer with backstepping and BAS for online parameter optimization.…”

Section: Other Applicationsmentioning

confidence: 99%

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

Chen,

Cao,

Chen

et al. 2024

Artif Intell Rev

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In recent years, swarm intelligence optimization algorithms have been proven to have significant effects in solving combinatorial optimization problems. Introducing the concept of evolutionary computing, which is currently a hot research topic, into swarm intelligence optimization algorithms to form novel swarm intelligence optimization algorithms has proposed a new research direction for better solving combinatorial optimization problems. The longhorn beetle whisker search algorithm is an emerging heuristic algorithm, which originates from the simulation of longhorn beetle foraging behavior. This algorithm simulates the touch strategy required by longhorn beetles during foraging, and achieves efficient search in complex problem spaces through bioheuristic methods. This article reviews the research progress on the search algorithm for longhorn beetles from 2017 to present. Firstly, the basic principle and model structure of the beetle whisker search algorithm were introduced, and its differences and connections with other heuristic algorithms were analyzed. Secondly, this paper summarizes the research achievements of scholars in recent years on the improvement of longhorn whisker search algorithms. Then, the application of the beetle whisker search algorithm in various fields was explored, including function optimization, engineering design, and path planning. Finally, this paper summarizes the research achievements of scholars in recent years on the improvement of the longhorn whisker search algorithm, and proposes future research directions, including algorithm deep learning fusion, processing of multimodal problems, etc. Through this review, readers will have a comprehensive understanding of the research status and prospects of the longhorn whisker search algorithm, providing useful guidance for its application in practical problems.

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Section: Other Applicationsmentioning

confidence: 99%

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

Chen,

Cao,

Chen

et al. 2024

Artif Intell Rev

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“…Yet, we have observed that the majority of these models are typically designed to handle one specific task or a small subset of closely related tasks [3,[6][7][8][9][10]. When applied to other tasks, they often exhibit subpar performance.…”

Section: Introductionmentioning

confidence: 99%

UMSSNet: A Unified Multi-Scale SegmentationNetwork for Heterogeneous Medical Images

Xu,

Chen,

Gong

2024

Preprint

Self Cite

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Medical image analysis plays a pivotal role in diagnosis and treatment. However, the diverse characteristics of various imaging modalities often demand distinct processing approaches. In this study, we introduce UMSSNet, a versatile method for medical image segmentation that leverages data from heterogeneous medical images across different scales. By utilizing Gaussian pyramid-based image processing techniques, we transform the heterogeneous medical images into a uniform multi-scale image structure. Subsequently, UMSSNet integrates multiscale image features, encompassing contextual information, and adopts a dynamic and hierarchical approach to process images at various scales, emulating the decision-making process of human pathologists and facilitating precise image segmentation. We tested UMSSNet on publicly available datasets consisting of various forms of medical images, including WSI, Biopsy slides, CT, MRI, X-ray, Colonoscopy, Fundus, and CMR, as well as private datasets of Immunohistochemical staining, Immunofluorescence staining, and Masson staining sample images. UMSSNet demonstrated performance comparable to state-of-the-art medical image segmentation methods Furthermore, the generalizability of UMSSNet in segmenting heterogeneous medical images holds promise for future research in the analysis of multimodal medical data.

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“…The main contributions of this article are summarized below: In this article, an efficient energy consumption and delay aware autonomous data gathering routing protocol scheme based on deep learning mobile edge model and beetle antennae search algorithm for UWSN is proposed that enhances the total information gathering organization proficiently. The AUV consists of merits in high throughput, computation, storing, and motion in UWSNs. Consequently, this method provides the MEM layer 27 that the MEC service and info gathering provision. DL has been increasingly applied as an enabling technology for mobile edge network optimization.…”

Section: Introductionmentioning

confidence: 99%

“…• The AUV consists of merits in high throughput, computation, storing, and motion in UWSNs. Consequently, this method provides the MEM layer 27 that the MEC service and info gathering provision. DL has been increasingly applied as an enabling technology for mobile edge network optimization.…”

mentioning

confidence: 99%

An efficient energy consumption and delay aware autonomous data gathering routing protocol scheme using a deep learning mobile edge model and beetle antennae search algorithm for underwater wireless sensor network

Pradeep¹,

Bapu²

2022

Concurrency and Computation

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Underwater wireless sensor network (UWSN) is used to monitor the compactness of ocean surveillance, marine and harsh underwater environment. In this article, an efficient energy consumption and delay aware autonomous data gathering routing protocol (ADGRP) scheme based on deep learning (dl) mobile edge model (mem) and beetle antennae search algorithm (BASA) for UWSN is proposed to overcome above problems. ADGRP is used to gather more data from the underwater environment by the use of the autonomous underwater vehicle (AUV). DL-MEM is used to increase the network life time. Then the deep learning parameters are optimized by using BAS. The objective function is "to increase the efficiency and lifetime of network by decreasing the energy consumptions and delay." The simulation process is carried out in MATLAB site. The proposed ADGRP-DL-MEM-BASA provides lower energy consumption 20.83%, 34.66%, 18.03%, 20.92%, 22.34%, lower energy drop 7.85%, 23.94%, 17.93%, 21.93%, 31.94% is compared with the existing energy-efficient probabilistic depth-based routing (EEPDBR-UWSN), ordered contention MAC (OCMAC-UWSN), Q-learning based energy-efficient and void avoidance routing protocol for underwater acoustic sensor networks (QL-EEBDG-UWSN), energy-efficient depth-base opportunistic routing along Q-learning for underwater wireless sensor networks (EDORQ-UWSN), channel-aware reinforcement learning-based multipath adaptive routing for underwater wireless sensor networks (CARMA-EE-UWSN) respectively. K E Y W O R D Sautonomous data gathering routing protocol, beetle antennae search optimization algorithm, deep learning mobile edge model, underwater wireless sensor network INTRODUCTIONNowadays, wireless sensors are used in many purposes, such as sensing in the underwater properties, remote sensing areas, and in the ocean and the marine underwater scenarios. 1 It is used to sensing, collecting information from large areas by the help of the sensors present in everynodes. 2 In this wireless sensors consists of many nodes and that nodes are connected by the sensors to collect the data from the underwater environments. 3 If any one of the node get damaged the system consumes more energy and cause delay to collect the data. 4 Then the network life time also decreased. 5

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Beetle Antennae Search Strategy for Neural Network Model Optimization with Application to Glomerular Filtration Rate Estimation

Cited by 6 publications

References 63 publications

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

UMSSNet: A Unified Multi-Scale SegmentationNetwork for Heterogeneous Medical Images

An efficient energy consumption and delay aware autonomous data gathering routing protocol scheme using a deep learning mobile edge model and beetle antennae search algorithm for underwater wireless sensor network

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