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Symmetry
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Computational Intelligence and Soft Computing: Recent Applications—Second Volume
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Computational Intelligence and Soft Computing: Recent Applications—Second Volume

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2056

Special Issue Editor

Prof. Dr. László T. Kóczy

E-Mail Website
Guest Editor
Department of Information Technology, Széchenyi Istvan University, Egyetem Tér 1, Győr, Hungary
Interests: fuzzy and soft computing systems; telematic systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past year, a rather impressive hard-copy volume was published by MDPI, under the title “Computational Intelligence and Soft Computing: Recent Applications”. This 448-page book was based on a Special Issue the journal of Symmetry. Is there any connection between symmetry (or asymmetry) and computational intelligence approaches? If you are interested in this subject, please considering reading the Introduction to the book.

The Special Issue attracted, perhaps unexpectedly, lots of attention from a large number of prospective authors. After careful reviewing, several high-quality manuscripts were selected, and the open access Special Issue was published. The deadline was extended twice because of the large number of continuously arriving new submissions. Because of this continuous interest, the Editorial Board decided to publish a second Special Issue focused on the same topic; therefore, we hope that the quantity of submissions will not decrease. So, for the second Special Issue, we repeat the call for submissions, which are open now.

The closely related fields of computational intelligence and soft computing cover broad areas, such as fuzzy systems; artificial neural networks; evolutionary, population-based, and memetic algorithms; cognitive computing; and many others. The common points in these approaches are the sub-symbolic representation of knowledge, which enables the modeling and highly efficient (often approximate) algorithmic solution of mathematically intractable systems and problems. These methods are also referred to as being "biologically inspired", as the starting ideas in them usually derive from microscopic or macroscopic biological "systems", i.e., animals, populations of animals, and even the human body and thinking processes, even though these are often radically simplified in the implementation. It is sometimes amazing how efficient a method inspired by a phenomenon such as biological evolution can be when the optimal solution in a mathematically unsolvable task (e.g., NP-complete bin packing, traveling salesman) must be still sought in real life.

This Special Issue targets the collection of recent applications where such approaches have proved to be successful and efficient, covering the spectrum of new models ready to be applied in practical modeling (such as new extended fuzzy cognitive map models simulating the convergence behavior of uncertain multi-concept systems), new deep learning, hierarchical and multicomponent fuzzy-rule-based models, decision and control applications, or memetic algorithms for optimizing logistics and related tasks.

The condition for inclusion in this Special Issue is the presentation of either (i) a novel methodological approach that is clearly suitable for real-life applications, or (ii) an essentially new and working application with elements of novel approaches or novel combinations of existing methodologies.

Please note that all submissions must be within the general scope of Symmetry.

Prof. Dr. László T. Kóczy
Guest Editor

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

  • computational intelligence
  • soft computing
  • fuzzy systems
  • artificial neural networks
  • connectionist systems
  • evolutionary algorithms
  • memetic algorithms
  • subjective probability
  • cognitive systems
  • deep learning
  • industrial applications
  • biomedical applications
  • logistics applications
  • environmental science applications
  • management science applications
  • decision support applications
  • image processing applications
  • modeling and algorithms

Related Special Issue

Published Papers (3 papers)

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Research

17 pages, 3672 KiB  
Article
Extended Deep-Learning Network for Histopathological Image-Based Multiclass Breast Cancer Classification Using Residual Features
by Hiren Mewada
Symmetry 2024, 16(5), 507; https://0-doi-org.brum.beds.ac.uk/10.3390/sym16050507 - 23 Apr 2024
Viewed by 333
Abstract
Autonomy of breast cancer classification is a challenging problem, and early diagnosis is highly important. Histopathology images provide microscopic-level details of tissue samples and play a crucial role in the accurate diagnosis and classification of breast cancer. Moreover, advancements in deep learning play [...] Read more.
Autonomy of breast cancer classification is a challenging problem, and early diagnosis is highly important. Histopathology images provide microscopic-level details of tissue samples and play a crucial role in the accurate diagnosis and classification of breast cancer. Moreover, advancements in deep learning play an essential role in early cancer diagnosis. However, existing techniques involve unique models for each classification based on the magnification factor and require training numerous models or using a hierarchical approach combining multiple models irrespective of the focus of the cell features. This may lead to lower performance for multiclass categorization. This paper adopts the DenseNet161 network by adding a learnable residual layer. The learnable residual layer enhances the features, providing low-level information. In addition, residual features are obtained from the convolution features of the preceding layer, which ensures that the future size is consistent with the number of channels in DenseNet’s layer. The concatenation of spatial features with residual features helps better learn texture classification without the need for an additional texture feature extraction module. The model was validated for both binary and multiclass categorization of malignant images. The proposed model’s classification accuracy ranges from 94.65% to 100% for binary and multiclass classification, and the error rate is 2.78%. Overall, the suggested model has the potential to improve the survival of breast cancer patients by allowing precise diagnosis and therapy. Full article
(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications—Second Volume)
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21 pages, 379 KiB  
Article
An Integrated Framework for Dynamic Vehicle Routing Problems with Pick-up and Delivery Time Windows and Shared Fleet Capacity Planning
by Eyüp Tolunay Küp, Salih Cebeci, Barış Bayram, Gözde Aydın, Burcin Bozkaya and Raha Akhavan-Tabatabaei
Symmetry 2024, 16(4), 505; https://0-doi-org.brum.beds.ac.uk/10.3390/sym16040505 - 22 Apr 2024
Viewed by 368
Abstract
This paper proposes a novel route optimization framework to solve the problem of instant pick-up and delivery for e-grocery orders. The proposed framework extends the traditional time-windowed package delivery problem. We demonstrate the effectiveness of our approach for this integrated problem using actual [...] Read more.
This paper proposes a novel route optimization framework to solve the problem of instant pick-up and delivery for e-grocery orders. The proposed framework extends the traditional time-windowed package delivery problem. We demonstrate the effectiveness of our approach for this integrated problem using actual delivery data from HepsiJet, a leading e-commerce logistics provider in Turkey. We first employ several machine learning algorithms and simulations to investigate the capacity of the courier. Subsequently, a dynamic route planning workflow is executed with a highly specialized and novel routing algorithm. Our proposed heuristic approach considers combined fleet operations for delivering regular packages originating from a central depot and dynamic e-grocery orders picked up at local supermarkets and delivered to the customers. The heuristic algorithm constitutes k-opt and node transfer operation variations customized for this integrated problem. We report the performance of our approach in problem instances from the literature and instances from HepsiJet’s daily operations, which we also publicly share as new route optimization problem instances. Our results suggest that, despite the more complex nature of the integrated problem, our proposed algorithm and solution framework produce more efficient and cost-effective solutions that offer additional business opportunities for companies such as HepsiJet. The computational analyses reveal that implementing our proposed approach yields significant efficiency gains and cost reductions for the company, with a distance reduction of over 30%, underscoring our approach’s effectiveness in achieving substantial cost savings and enhanced efficiency through integrating two distinct delivery operations. Full article
(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications—Second Volume)
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29 pages, 9075 KiB  
Article
Scheduling of Multi-AGV Systems in Automated Electricity Meter Verification Workshops Based on an Improved Snake Optimization Algorithm
by Kun Shi, Miaohan Zhang, Zhaolei He, Shi Yin, Zhen Ai and Nan Pan
Symmetry 2023, 15(11), 2034; https://0-doi-org.brum.beds.ac.uk/10.3390/sym15112034 - 08 Nov 2023
Viewed by 1013
Abstract
Automated guided vehicles (AGVs) are one of the core technologies for building unmanned autonomous integrated automated electric meter verification workshops in metrology centers. However, complex obstacles on the verification lines, frequent AGV charging, and multi-AGV collaboration make the scheduling problem more complicated. Aiming [...] Read more.
Automated guided vehicles (AGVs) are one of the core technologies for building unmanned autonomous integrated automated electric meter verification workshops in metrology centers. However, complex obstacles on the verification lines, frequent AGV charging, and multi-AGV collaboration make the scheduling problem more complicated. Aiming at the characteristics and constraints of AGV transportation scheduling for metrology verification, a multi-AGV scheduling model was established to minimize the maximum completion time and charging cost, integrating collision-avoidance constraints. An improved snake optimization algorithm was proposed that first assigns and sorts tasks based on AGV-order-address three-level mapping encoding and decoding, then searches optimal paths using an improved A* algorithm solves multi-AGV path conflicts, and finally finds the minimum-charging-cost schedule through large neighborhood search. We conducted simulations using real data, and the calculated results reduced the objective function value by 16.4% compared to the traditional first-in-first-out (FIFO) method. It also reduced the number of charges by 60.3%. In addition, the proposed algorithm is compared with a variety of cutting-edge algorithms and the results show that the objective function value is reduced by 8.7–11.2%, which verifies the superiority of the proposed algorithm and the feasibility of the model. Full article
(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications—Second Volume)
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