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Biomimetics
Special Issues
Bioinspired Behaviors and Control Strategies Empowering Swarm Intelligent Systems
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Bioinspired Behaviors and Control Strategies Empowering Swarm Intelligent Systems

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Bioinspired Sensorics, Information Processing and Control".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 6160

Special Issue Editors

Dr. Emanuela Del Dottore

E-Mail Website
Guest Editor
Istituto Italiano di Tecnologia, Genova, Italy
Interests: biorobotics; behavior analysis; swarm and collective behavior; intelligent systems
Dr. Michele Palladino

E-Mail Website
Guest Editor
Department of Information Engineering, University of L’Aquila, L'Aquila, Italy
Interests: control theory; applied mathematics; learning systems; bioinspired systems

Special Issue Information

Dear Colleagues,

Nature offers several examples of complex systems with social behaviors. Ants, honeybees, wasps, fishes, birds, and even plants more recently, have inspired control and optimization algorithms in artificial contexts, including in logistics, transportation, telecommuncations, and robotics.

Swarm intelligence is deeply grounded in biomimetic and bioinspired models of self-organization featuring the emergence of complex behaviors such as collective foraging, co-operative construction, co-ordinated exploration, and colonization of an area, with no need for a central elaboration but relying instead on distributed control and individual decision-making strategies. Nature implements these strategies in a variety of ways, including multi-agent systems with different complexity levels of signal processing or into individuals through the distribution and embodiment of "simple" responses into the system's skin or tissue, which are functionalized through proper design, mechanical properties, and reactive behaviors to physical/chemical signals converging into high-level system behavior. Individuals of such complex systems can have homogeneous or heterogeneous chararcetristics and show extremely simple to articulated and intelligent individual behaviors.

Industrial, agricultaral, domotic, and other fields of applications need more and more prompt, reliable, and fault-tolerant implementations to be effective in dynamic environments.   

This Special Issue aims to collect the latest results at the boundary between control theory and swarm intelligence and to discuss to what extent the bioinspired appraoch can offer favorable impacts through effective applications of swarm intelligence and foster new thoughts about novel paradigms, research directions, and innovative solutions in swarm robotics. To this end, we encourage submissions of theoretical papers, reviews, as well as experimental studies dealing with relevant questions in swarm robotics and distributed intelligence.

Dr. Emanuela Del Dottore
Dr. Michele Palladino
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. Biomimetics 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 2200 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

  • bioinspired collective and collaborative behaviors
  • distributed control
  • reinforcement learning in bioinspired systems
  • complex systems
  • robotic networks
  • self-organization and emergent behaviors
  • cellular automata, graph and control theory
  • computational models for swarm intelligence
  • swarm robotics applications
  • soft robotics
  • intelligent materials
  • bioinspired design

Published Papers (4 papers)

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Research

31 pages, 7038 KiB  
Article
Application of an Enhanced Whale Optimization Algorithm on Coverage Optimization of Sensor
by Yong Xu, Baicheng Zhang and Yi Zhang
Biomimetics 2023, 8(4), 354; https://doi.org/10.3390/biomimetics8040354 - 09 Aug 2023
Cited by 1 | Viewed by 1268
Abstract
The wireless sensor network (WSN) is an essential technology of the Internet of Things (IoT) but has the problem of low coverage due to the uneven distribution of sensor nodes. This paper proposes a novel enhanced whale optimization algorithm (WOA), incorporating Lévy flight [...] Read more.
The wireless sensor network (WSN) is an essential technology of the Internet of Things (IoT) but has the problem of low coverage due to the uneven distribution of sensor nodes. This paper proposes a novel enhanced whale optimization algorithm (WOA), incorporating Lévy flight and a genetic algorithm optimization mechanism (WOA-LFGA). The Lévy flight technique bolsters the global search ability and convergence speed of the WOA, while the genetic optimization mechanism enhances its local search and random search capabilities. WOA-LFGA is tested with 29 mathematical optimization problems and a WSN coverage optimization model. Simulation results demonstrate that the improved algorithm is highly competitive compared with mainstream algorithms. Moreover, the practicality and the effectiveness of the improved algorithm in optimizing wireless sensor network coverage are confirmed. Full article
(This article belongs to the Special Issue Bioinspired Behaviors and Control Strategies Empowering Swarm Intelligent Systems)
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23 pages, 4548 KiB  
Article
Research on Economic Optimal Dispatching of Microgrid Based on an Improved Bacteria Foraging Optimization
by Yi Zhang, Yang Lv and Yangkun Zhou
Biomimetics 2023, 8(2), 150; https://doi.org/10.3390/biomimetics8020150 - 07 Apr 2023
Cited by 2 | Viewed by 1063
Abstract
This paper proposes an improved Bacterial Foraging Optimization for economically optimal dispatching of the microgrid. Three optimized steps are presented to solve the slow convergence, poor precision, and low efficiency of traditional Bacterial Foraging Optimization. First, the self-adaptive step size equation in the [...] Read more.
This paper proposes an improved Bacterial Foraging Optimization for economically optimal dispatching of the microgrid. Three optimized steps are presented to solve the slow convergence, poor precision, and low efficiency of traditional Bacterial Foraging Optimization. First, the self-adaptive step size equation in the chemotaxis process is present, and the particle swarm velocity equation is used to improve the convergence speed and precision of the algorithm. Second, the crisscross algorithm is used to enrich the replication population and improve the global search performance of the algorithm in the replication process. Finally, the dynamic probability and sine-cosine algorithm are used to solve the problem of easy loss of high-quality individuals in dispersal. Quantitative analysis and experiments demonstrated the superiority of the algorithm in the benchmark function. In addition, this study built a multi-objective microgrid dynamic economic dispatch model and dealt with the uncertainty of wind and solar using the Monte Carlo method in the model. Experiments show that this model can effectively reduce the operating cost of the microgrid, improve economic benefits, and reduce environmental pollution. The economic cost is reduced by 3.79% compared to the widely used PSO, and the economic cost is reduced by 5.23% compared to the traditional BFO. Full article
(This article belongs to the Special Issue Bioinspired Behaviors and Control Strategies Empowering Swarm Intelligent Systems)
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25 pages, 33950 KiB  
Article
Analysis of UAV Thermal Soaring via Hawk-Inspired Swarm Interaction
by Adam Pooley, Max Gao, Arushi Sharma, Sachi Barnaby, Yu Gu and Jason Gross
Biomimetics 2023, 8(1), 124; https://doi.org/10.3390/biomimetics8010124 - 17 Mar 2023
Cited by 1 | Viewed by 1792
Abstract
A swarm of unmanned aerial vehicles (UAVs) can be used for many applications, including disaster relief, search and rescue, and establishing communication networks, due to its mobility, scalability, and robustness to failure. However, a UAV swarm’s performance is typically limited by each agent’s [...] Read more.
A swarm of unmanned aerial vehicles (UAVs) can be used for many applications, including disaster relief, search and rescue, and establishing communication networks, due to its mobility, scalability, and robustness to failure. However, a UAV swarm’s performance is typically limited by each agent’s stored energy. Recent works have considered the usage of thermals, or vertical updrafts of warm air, to address this issue. One challenge lies in a swarm of UAVs detecting and taking advantage of these thermals. Inspired by hawks, a swarm could take advantage of thermals better than individuals due to the swarm’s distributed sensing abilities. To determine which emergent behaviors increase survival time, simulation software was created to test the behavioral models of UAV gliders around thermals. For simplicity and robustness, agents operate with limited information about other agents. The UAVs’ motion was implemented as a Boids model, replicating the behavior of flocking birds through cohesion, separation, and alignment forces. Agents equipped with a modified behavioral model exhibit dynamic flocking behavior, including relative ascension-based cohesion and relative height-based separation and alignment. The simulation results show the agents flocking to thermals and improving swarm survival. These findings present a promising method to extend the flight time of autonomous UAV swarms. Full article
(This article belongs to the Special Issue Bioinspired Behaviors and Control Strategies Empowering Swarm Intelligent Systems)
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Graphical abstract

16 pages, 6194 KiB  
Article
Application of Hybrid Swarming Algorithm on a UAV Regional Logistics Distribution
by Yi Zhang and Hongda Yu
Biomimetics 2023, 8(1), 96; https://doi.org/10.3390/biomimetics8010096 - 27 Feb 2023
Viewed by 1223
Abstract
This paper proposes a hybrid algorithm based on the ant colony and Physarum Polycephalum algorithms. The positive feedback mechanism is used to find the globally optimal path. The crossover and mutation operations of the genetic algorithm are introduced into the path search mechanism [...] Read more.
This paper proposes a hybrid algorithm based on the ant colony and Physarum Polycephalum algorithms. The positive feedback mechanism is used to find the globally optimal path. The crossover and mutation operations of the genetic algorithm are introduced into the path search mechanism for the first time. The Van der Waals force is applied to the pheromone updating mechanism. Simulation results show that the improved algorithm has advantages in quality and speed of solution compared with other mainstream algorithms. This paper provides fast and accurate route methods for solving the Traveling Salesman Problem first and a delivery scheme is also presented for UAVs to realize “contactless delivery” to users in the Changchun Mingzhu District during the COVID-19 epidemic, which confirms the practicability and robustness of the algorithm. Full article
(This article belongs to the Special Issue Bioinspired Behaviors and Control Strategies Empowering Swarm Intelligent Systems)
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