Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Bio-Inspired Collective Intelligence in Multi-Agent Systems
share announcement

Bio-Inspired Collective Intelligence in Multi-Agent Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 996

Special Issue Editor

Dr. Yara Khaluf

E-Mail Website
Guest Editor
Information Technology Group, Department of Social Sciences, Wageningen University and Research, Hollandseweg 1, 6706 KN Wageningen, The Netherlands
Interests: robot swarms; artificial intelligence; mathematical modeling; design of distributed systems; computational modeling; distributed AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue explores the fascinating realm of integrating nature's strategies into the design and functionality of multi-agent systems. Drawing inspiration from biological entities such as insect colonies, animal herds, and cellular systems, this research investigates how collective behaviors emerge from interactions among multiple agents.

This interdisciplinary field aims to replicate the adaptive, self-organizing, and decentralized nature of biological systems within artificial agents. By mimicking the decentralized decision making and coordination observed in nature, these multi-agent systems aim to exhibit enhanced problem solving, robustness, and scalability.

Exploring bio-inspired collective intelligence involves studying algorithms, models, and mechanisms that mimic natural systems' collective behaviors. From swarm robotics to optimization algorithms, this research avenue seeks to harness the power of decentralized control, information sharing, and emergent behaviors within artificial systems.

Understanding and implementing bio-inspired collective intelligence can revolutionize various domains, from autonomous robotics and distributed computing to logistics and optimization problems. This Special Issue aims to contribute to this evolving field by presenting novel approaches, insights, or applications that leverage nature's principles to enhance the capabilities of multi-agent systems.

Dr. Yara Khaluf
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • bio-inspired
  • collective intelligence
  • multi-agent systems
  • swarm robotics
  • decentralized control
  • emergent behavior
  • nature-inspired algorithms
  • self-organization
  • distributed computing
  • adaptive systems

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 810 KiB  
Article
Impulsive Control Discrete Fractional Neural Networks in Product Form Design: Practical Mittag-Leffler Stability Criteria
by Trayan Stamov
Appl. Sci. 2024, 14(9), 3705; https://doi.org/10.3390/app14093705 - 26 Apr 2024
Viewed by 255
Abstract
The planning, regulation and effectiveness of the product design process depend on various characteristics. Recently, bio-inspired collective intelligence approaches have been applied in this process in order to create more appealing product forms and optimize the design process. In fact, the use of [...] Read more.
The planning, regulation and effectiveness of the product design process depend on various characteristics. Recently, bio-inspired collective intelligence approaches have been applied in this process in order to create more appealing product forms and optimize the design process. In fact, the use of neural network models in product form design analysis is a complex process, in which the type of network has to be determined, as well as the structure of the network layers and the neurons in them; the connection coefficients, inputs and outputs have to be explored; and the data have to be collected. In this paper, an impulsive discrete fractional neural network modeling approach is introduced for product design analysis. The proposed model extends and complements several existing integer-order neural network models to the generalized impulsive discrete fractional-order setting, which is a more flexible mechanism to study product form design. Since control and stability methods are fundamental in the construction and practical significance of a neural network model, appropriate impulsive controllers are designed, and practical Mittag-Leffler stability criteria are proposed. The Lyapunov function strategy is applied in providing the stability criteria and their efficiency is demonstrated via examples and a discussion. The established examples also illustrate the role of impulsive controllers in stabilizing the behavior of the neuronal states. The proposed modeling approach and the stability results are applicable to numerous industrial design tasks in which multi-agent systems are implemented. Full article
(This article belongs to the Special Issue Bio-Inspired Collective Intelligence in Multi-Agent Systems)
16 pages, 3310 KiB  
Article
Cascade-Forward, Multi-Parameter Artificial Neural Networks for Predicting the Energy Efficiency of Photovoltaic Modules in Temperate Climate
by Karol Postawa, Michał Czarnecki, Edyta Wrzesińska-Jędrusiak, Wieslaw Łyskawiński and Marek Kułażyński
Appl. Sci. 2024, 14(7), 2764; https://doi.org/10.3390/app14072764 - 26 Mar 2024
Viewed by 457
Abstract
Solar energy is a promising and efficient source of electricity in countries with stable and high sunshine duration. However, in less favorable conditions, for example in continental, temperate climates, the process requires optimization to be cost-effective. This cannot be done without the support [...] Read more.
Solar energy is a promising and efficient source of electricity in countries with stable and high sunshine duration. However, in less favorable conditions, for example in continental, temperate climates, the process requires optimization to be cost-effective. This cannot be done without the support of appropriate mathematical and numerical methods. This work presents a procedure for the construction and optimization of an artificial neural network (ANN), along with an example of its practical application under the conditions mentioned above. In the study, data gathered from a photovoltaic system in 457 consecutive days were utilized. The data includes measurements of generated power, as well as meteorological records. The cascade-forward ANN was trained with a resilient backpropagation procedure and sum squared error as a performance function. The final ANN has two hidden layers with nine and six nodes. This resulted in a relative error of 10.78% and R2 of 0.92–0.97 depending on the data sample. The case study was used to present an example of the potential application of the tool. This approach proved the real benefits of the optimization of energy consumption. Full article
(This article belongs to the Special Issue Bio-Inspired Collective Intelligence in Multi-Agent Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop