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Bio-Inspired Collective Intelligence in Multi-Agent Systems
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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: closed (31 December 2024) | Viewed by 6032

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

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Keywords

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

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Published Papers (4 papers)

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Research

32 pages, 5897 KiB  
Article
A Self-Adaptive Neighborhood Search Differential Evolution Algorithm for Planning Sustainable Sequential Cyber–Physical Production Systems
by Fu-Shiung Hsieh
Appl. Sci. 2024, 14(17), 8044; https://doi.org/10.3390/app14178044 - 8 Sep 2024
Viewed by 1258
Abstract
Although Cyber–Physical Systems (CPSs) provide a flexible architecture for enterprises to deal with changing demand, an effective method to organize and allocate resources while considering sustainability factors is required to meet customers’ order requirements and mitigate negative impacts on the environment. The planning [...] Read more.
Although Cyber–Physical Systems (CPSs) provide a flexible architecture for enterprises to deal with changing demand, an effective method to organize and allocate resources while considering sustainability factors is required to meet customers’ order requirements and mitigate negative impacts on the environment. The planning of processes to achieve sustainable CPSs becomes an important issue to meet demand timely in a dynamic environment. The problem with planning processes in sustainable CPSs is the determination of the configuration of workflows/resources to compose processes with desirable properties, taking into account time and energy consumption factors. The planning problem in sustainable CPSs can be formulated as an integer programming problem with constraints, and this poses a challenge due to computational complexity. Furthermore, the ever-shrinking life cycle of technologies leads to frequent changes in processes and makes the planning of processes a challenging task. To plan processes in a changing environment, an effective planning method must be developed to automate the planning task. To tackle computational complexity, evolutionary computation approaches such as bio-inspired computing and metaheuristics have been adopted extensively in solving complex optimization problems. This paper aims to propose a solution methodology and an effective evolutionary algorithm with a local search mechanism to support the planning of processes in sustainable CPSs based on an auction mechanism. To achieve this goal, we focus on developing a self-adaptive neighborhood search-based Differential Evolution method. An effective planning method should be robust in terms of performance with respect to algorithmic parameters. We assess the performance and robustness of this approach by performing experiments for several cases. By comparing the results of these experiments, it shows that the proposed method outperforms several other algorithms in the literature. To illustrate the robustness of the proposed self-adaptive algorithm, experiments with different settings of algorithmic parameters were conducted. The results show that the proposed self-adaptive algorithm is robust with respect to algorithmic parameters. Full article
(This article belongs to the Special Issue Bio-Inspired Collective Intelligence in Multi-Agent Systems)
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16 pages, 5560 KiB  
Article
Environmental-Impact-Based Multi-Agent Reinforcement Learning
by Farinaz Alamiyan-Harandi and Pouria Ramazi
Appl. Sci. 2024, 14(15), 6432; https://doi.org/10.3390/app14156432 - 24 Jul 2024
Viewed by 1351
Abstract
To promote cooperation and strengthen the individual impact on the collective outcome in social dilemmas, we propose the Environmental-impact Multi-Agent Reinforcement Learning (EMuReL) method where each agent estimates the “environmental impact” of every other agent, that is, the difference in the current environment [...] Read more.
To promote cooperation and strengthen the individual impact on the collective outcome in social dilemmas, we propose the Environmental-impact Multi-Agent Reinforcement Learning (EMuReL) method where each agent estimates the “environmental impact” of every other agent, that is, the difference in the current environment state compared to the hypothetical environment in the absence of that other agent. Inspired by the inequity aversion model, the agent then compares its own reward with that of its fellows multiplied by their environmental impacts. If its reward exceeds the scaled reward of one of its fellows, the agent takes “social responsibility” toward that fellow by reducing its own reward. Therefore, the less influential an agent is in reaching the current state, the more social responsibility is taken by other agents. Experiments in the Cleanup (resp. Harvest) test environment demonstrated that agents trained based on EMuReL learned to cooperate more effectively and obtained 54% (39%) and 20% (44%) more total rewards while preserving the same cooperation levels compared to when they were trained based on the two state-of-the-art reward reshaping methods: inequity aversion and social influence. Full article
(This article belongs to the Special Issue Bio-Inspired Collective Intelligence in Multi-Agent Systems)
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22 pages, 821 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
Cited by 1 | Viewed by 1286
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)
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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
Cited by 1 | Viewed by 1269
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)
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