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Symmetry
Special Issues
Advances of Network Structures for Cooperative Working
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Advances of Network Structures for Cooperative Working

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 1390

Special Issue Editors

Prof. Dr. Yuhua Luo

E-Mail Website
Guest Editor
Department of Computer Science, University of Balearic Islands, Palma, Spain
Interests: computer supported cooperative work; computer vision; computer graphics; multimedia
Special Issues, Collections and Topics in MDPI journals
Dr. Pilar Fuster Parra

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, Universitat de les Illes Balears, Palma de Mallorca, Spain
Interests: mathematical modelling; fuzzy sets; generalized distances; aggregation operators; data mining; multidisciplinary applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

An obvious trend that emerged in the current research and development is the significant advantage of applying network structures for the cooperative working. It shows the potential and power at all levels of cooperative applications convincingly. The structure of the networks seen here is very general in both symmetric and asymmetric manner. This special issue concentrates on the symmetric characteristic of the networks and its influence in the performance for a wide range of cooperative applications. The applications include the cooperative work in the well-known internet and the social networks, specific networks such as neural networks, sensor networks, even the basic convolutional networks embedded into many basic algorithms.

This special issue is calling for all the contributions that apply the network structure both symmetric and asymmetric in different application areas at all the levels with the analysis of the impact of the symmetry in the network structure. It intends to show the potential of the network structure from macro to micro level for cooperative working and cooperative task accomplishment related to the symmetry characteristic. All the up-to-date related contributions are welcome to submit.

Prof. Dr. Yuhua Luo
Dr. Pilar Fuster Parra
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • network structures
  • impact of symmetry in networks
  • cooperative work
  • internet
  • social network
  • sensor network
  • neural network
  • Internet of Things
  • convolutional neural network
  • energy network
  • multi-cell, multi-unit structures

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Published Papers (1 paper)

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Research

29 pages, 6054 KiB  
Article
An Enhanced Tunicate Swarm Algorithm with Symmetric Cooperative Swarms for Training Feedforward Neural Networks
by Chengtao Du and Jinzhong Zhang
Symmetry 2024, 16(7), 866; https://doi.org/10.3390/sym16070866 - 8 Jul 2024
Viewed by 762
Abstract
The input layer, hidden layer, and output layer are three models of neural processors that comprise feedforward neural networks. In this paper, an enhanced tunicate swarm algorithm based on a differential sequencing alteration operator (ETSA) with symmetric cooperative swarms is presented to train [...] Read more.
The input layer, hidden layer, and output layer are three models of neural processors that comprise feedforward neural networks. In this paper, an enhanced tunicate swarm algorithm based on a differential sequencing alteration operator (ETSA) with symmetric cooperative swarms is presented to train feedforward neural networks. The objective is to accomplish minimum classification errors and the most appropriate neural network layout by regulating the layers’ connection weights and neurons’ deviation thresholds according to the transmission error between the anticipated input and the authentic output. The TSA mimics jet motorization and swarm scavenging to mitigate directional collisions and to maintain the greatest solution that is customized and regional. However, the TSA exhibits the disadvantages of low computational accuracy, a slow convergence speed, and easy search stagnation. The differential sequencing alteration operator has adaptable localized extraction and search screening to broaden the identification scope, enrich population creativity, expedite computation productivity, and avoid search stagnation. The ETSA integrates exploration and exploitation to mitigate search stagnation, which has sufficient stability and flexibility to acquire the finest solution. The ETSA was distinguished from the ETTAO, EPSA, SABO, SAO, EWWPA, YDSE, and TSA by monitoring seventeen alternative datasets. The experimental results confirm that the ETSA maintains profound sustainability and durability to avoid exaggerated convergence, locate the acceptable transmission error, and equalize extraction and prospection to yield a faster convergence speed, superior calculation accuracy, and greater categorization accuracy. Full article
(This article belongs to the Special Issue Advances of Network Structures for Cooperative Working)
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