research-article

Classification of Sonar Targets Using an MLP Neural Network Trained by Dragonfly Algorithm

Authors:

Mohammad Khishe,

Abbas SafariAuthors Info & Claims

Volume 108, Issue 4

Pages 2241 - 2260

https://doi.org/10.1007/s11277-019-06520-w

Published: 01 October 2019 Publication History

Abstract

Due to the compatibility of the designed classifiers with MLP Neural Networks (MLP NNs), in this article, MLP NNs have been used to identify and classify active and passive sonar targets. On the one hand, the great importance of precise and immediate classification of sonar targets, and on the other hand, being trapped in local minimums and the low convergence speed in classic MLP NNs have led the newly proposed Dragonfly Algorithm (DA) to be offered for training MLP NNs. In order to assess the performance of the designed classifier, this algorithm have been compared with BBO, GWO, ALO, ACO, GSA and MVO algorithms in terms of precision of classification, convergence speed and the ability to avoid local optimum. To have a comprehensive comparison, the three sets of active and passive data were used. Simulation results indicate that DA-based classification have better results in all three datasets compared to benchmark algorithms.

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Cited By

Sowmya RPremkumar MJangir P(2024)Newton-Raphson-based optimizerEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107532128:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107532
Zhang Y(2024)Electrical line fault prediction using a novel grey wolf optimization algorithm based on multilayer perceptronAdvanced Control for Applications10.1002/adc2.2136:3Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1002/adc2.213
Alenizi FAlsalami OSaffari AZahiri SMohammadi M(2023)Decision Fusion and Micro-Doppler Effects in Moving Sonar Target RecognitionInternational Journal of Intelligent Systems10.1155/2023/27681262023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/2768126
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Index Terms

Classification of Sonar Targets Using an MLP Neural Network Trained by Dragonfly Algorithm
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
      2. Neural networks
2. Theory of computation
  1. Design and analysis of algorithms

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Wireless Personal Communications: An International Journal

Wireless Personal Communications: An International Journal Volume 108, Issue 4

Oct 2019

637 pages

ISSN:0929-6212

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Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 October 2019

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Cited By

Sowmya RPremkumar MJangir P(2024)Newton-Raphson-based optimizerEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107532128:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107532
Zhang Y(2024)Electrical line fault prediction using a novel grey wolf optimization algorithm based on multilayer perceptronAdvanced Control for Applications10.1002/adc2.2136:3Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1002/adc2.213
Alenizi FAlsalami OSaffari AZahiri SMohammadi M(2023)Decision Fusion and Micro-Doppler Effects in Moving Sonar Target RecognitionInternational Journal of Intelligent Systems10.1155/2023/27681262023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/2768126
Devi RPremkumar MKiruthiga GSowmya R(2023)IGJO: An Improved Golden Jackel Optimization Algorithm Using Local Escaping Operator for Feature Selection ProblemsNeural Processing Letters10.1007/s11063-023-11146-y55:5(6443-6531)Online publication date: 14-Feb-2023
https://dl.acm.org/doi/10.1007/s11063-023-11146-y
Rahmani SMousavirad SEl-Abd MSchaefer GOliva D(2023)Centroid-Based Differential Evolution with Composite Trial Vector Generation Strategies for Neural Network TrainingApplications of Evolutionary Computation10.1007/978-3-031-30229-9_39(608-622)Online publication date: 12-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-30229-9_39
Jiang Y(2022)Procurement Volume Prediction of Cross-Border E-Commerce Platform Based on BP-NNWireless Communications & Mobile Computing10.1155/2022/75968192022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7596819
Hosseini Nejad Takhti ASaffari AMartín DKhishe MMohammadi M(2022)Classification of Marine Mammals Using the Trained Multilayer Perceptron Neural Network with the Whale Algorithm Developed with the Fuzzy SystemComputational Intelligence and Neuroscience10.1155/2022/32164002022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/3216400
Mousavirad SGandomi AHomayoun H(2022)A Clustering-based Differential Evolution Boosted by a Regularisation-based Objective Function and a Local Refinement for Neural Network Training2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870211(1-8)Online publication date: 18-Jul-2022
https://dl.acm.org/doi/10.1109/CEC55065.2022.9870211
Mousavipour FMosavi M(2022)Sonar Data Classification Using Neural Network Trained by Hybrid Dragonfly and Chimp Optimization AlgorithmsWireless Personal Communications: An International Journal10.1007/s11277-022-10092-7129:1(191-208)Online publication date: 3-Nov-2022
https://dl.acm.org/doi/10.1007/s11277-022-10092-7
Karimi RShokri VKhishe MJameie M(2022)Marine Propeller Design Using Evolving Chaotic Autonomous Particle Swarm OptimizationWireless Personal Communications: An International Journal10.1007/s11277-022-09625-x125:2(1653-1675)Online publication date: 1-Jul-2022
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