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Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and...
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Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 6555

Special Issue Editor

Prof. Dr. Alma Y. Alanis

E-Mail Website1 Website2
Guest Editor
Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), University of Guadalajara, Guadalajara 44330, Mexico
Interests: automatic control; artificial neural networks; intelligence systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The relevance of artificial intelligence in our daily lives today is evident, and this has led to significant advances in the development and implementation of bioinspired intelligent algorithms to solve a wide variety of real-world problems, as well as growing interest in the analysis of its mathematical properties. Although artificial intelligence has been developed mainly based on its applications, it is currently not possible to conceive it without its respective theoretical and algorithmic analysis, further to its multidisciplinary motivation and applications. These applications include, among others, mechatronic systems, artificial vision, biomedical systems, energy systems, transportation, economics, classification, complex networks, economic systems, industry, and transportation. The aim of this Special Issue is to highlight recent advances in the development and application of bioinspired intelligent algorithms to solve real-world problems related to optimization, modeling, and control, to provide a space for collaboration between researchers from different disciplines to solve real-world applications with their respective constraints and features in different fields of research. Papers with mathematical analysis and real-world application are particularly welcome. 

Topics of interest include, but are not limited to:

  • Bioinspired intelligent algorithms for modeling;
  • Bioinspired intelligent algorithms for control;
  • Bioinspired intelligent algorithms for fault detection and diagnosis;
  • Bioinspired intelligent algorithms for cyber-physical systems;
  • Bioinspired intelligent algorithms for optimization;
  • Data-driven bioinspired intelligent algorithms;
  • Deep learning bioinspired intelligent algorithms;
  • Mathematical analysis of bioinspired intelligent algorithms;
  • Bioinspired intelligent algorithms applications to robotics, dynamic systems, complex networks, classification, forecasting, biomedical systems, energy systems, industry, transportation, mechatronics, and others.

Prof. Dr. Alma Y. Alanis
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. Mathematics 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 2600 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.

Published Papers (5 papers)

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Research

17 pages, 371 KiB  
Article
A Population-Based Local Search Algorithm for the Identifying Code Problem
by Alejandro Lara-Caballero and Diego González-Moreno
Mathematics 2023, 11(20), 4361; https://doi.org/10.3390/math11204361 - 20 Oct 2023
Viewed by 834
Abstract
The identifying code problem for a given graph involves finding a minimum subset of vertices such that each vertex of the graph is uniquely specified by its nonempty neighborhood within the identifying code. The combinatorial optimization problem has a wide variety of applications [...] Read more.
The identifying code problem for a given graph involves finding a minimum subset of vertices such that each vertex of the graph is uniquely specified by its nonempty neighborhood within the identifying code. The combinatorial optimization problem has a wide variety of applications in location and detection schemes. Finding an identifying code of minimum possible size is a difficult task. In fact, it has been proven to be computationally intractable (NP-complete). Therefore, the use of heuristics to provide good approximations in a reasonable amount of time is justified. In this work, we present a new population-based local search algorithm for finding identifying codes of minimum cost. Computational experiments show that the proposed approach was found to be more effective than other state-of-the-art algorithms at generating high-quality solutions in different types of graphs with varying numbers of vertices. Full article
(This article belongs to the Special Issue Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications, 2nd Edition)
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19 pages, 14272 KiB  
Article
Edge-Weighted Consensus-Based Formation Control with Collision Avoidance for Mobile Robots Based on Multi-Strategy Mutation Differential Evolution
by Jesus Hernandez-Barragan, Tonatiuh Hernandez, Jorge D. Rios, Marco Perez-Cisneros and Alma Y. Alanis
Mathematics 2023, 11(17), 3633; https://doi.org/10.3390/math11173633 - 23 Aug 2023
Viewed by 1336
Abstract
An edge-weighted consensus-based formation control strategy is presented for mobile robots. In the edge-weighted strategy, a desired formation pattern is achieved by adjusting gain weights related to the distance between robots. Moreover, the edge-weighted formation control exploits the properties of weighted graphs to [...] Read more.
An edge-weighted consensus-based formation control strategy is presented for mobile robots. In the edge-weighted strategy, a desired formation pattern is achieved by adjusting gain weights related to the distance between robots. Moreover, the edge-weighted formation control exploits the properties of weighted graphs to allow the formation to rotate and adapt its shape to avoid collision among robots. However, formation patterns are commonly defined by biases with respect to the centroid of the consensus rather than gain weights. This work proposes to optimize the gain weights in edge-weighted graphs, given a formation pattern in terms of biases. A multi-strategy mutation differential evolution algorithm is introduced to solve the optimization problem. Simulation and real-world experiments are performed considering multi-robot systems composed of differential drive robots. Additionally, the experimental setup includes Turtlebot3® Waffle Pi robots and an OptiTrack® motion capture system for control purposes. The experimental results verify the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications, 2nd Edition)
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11 pages, 478 KiB  
Article
Biased Random-Key Genetic Algorithm with Local Search Applied to the Maximum Diversity Problem
by Geiza Silva, André Leite, Raydonal Ospina, Víctor Leiva, Jorge Figueroa-Zúñiga and Cecilia Castro
Mathematics 2023, 11(14), 3072; https://doi.org/10.3390/math11143072 - 12 Jul 2023
Cited by 1 | Viewed by 861
Abstract
The maximum diversity problem (MDP) aims to select a subset with a predetermined number of elements from a given set, maximizing the diversity among them. This NP-hard problem requires efficient algorithms that can generate high-quality solutions within reasonable computational time. In this study, [...] Read more.
The maximum diversity problem (MDP) aims to select a subset with a predetermined number of elements from a given set, maximizing the diversity among them. This NP-hard problem requires efficient algorithms that can generate high-quality solutions within reasonable computational time. In this study, we propose a novel approach that combines the biased random-key genetic algorithm (BRKGA) with local search to tackle the MDP. Our computational study utilizes a comprehensive set of MDPLib instances, and demonstrates the superior average performance of our proposed algorithm compared to existing literature results. The MDP has a wide range of practical applications, including biology, ecology, and management. We provide future research directions for improving the algorithm’s performance and exploring its applicability in real-world scenarios. Full article
(This article belongs to the Special Issue Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications, 2nd Edition)
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25 pages, 5973 KiB  
Article
Trajectories Generation for Unmanned Aerial Vehicles Based on Obstacle Avoidance Located by a Visual Sensing System
by Luis Felipe Muñoz Mendoza, Guillermo García-Torales, Cuauhtémoc Acosta Lúa, Stefano Di Gennaro and José Trinidad Guillen Bonilla
Mathematics 2023, 11(6), 1413; https://doi.org/10.3390/math11061413 - 15 Mar 2023
Cited by 2 | Viewed by 1691
Abstract
In this work, vectorial trajectories for unmanned aerial vehicles are completed based on a new algorithm named trajectory generation based on object avoidance (TGBOA), which is presented using a UAV camera as a visual sensor to define collision-free trajectories in scenarios with randomly [...] Read more.
In this work, vectorial trajectories for unmanned aerial vehicles are completed based on a new algorithm named trajectory generation based on object avoidance (TGBOA), which is presented using a UAV camera as a visual sensor to define collision-free trajectories in scenarios with randomly distributed objects. The location information of the objects is collected by the visual sensor and processed in real-time. This proposal has two advantages. First, this system improves efficiency by focusing the algorithm on object detection and drone position, thus reducing computational complexity. Second, online trajectory references are generated and updated in real-time. To define a collision-free trajectory and avoid a collision between the UAV and the detected object, a reference is generated and shown by the vector, symmetrical, and parametric equations. Such vectors are used as a reference in a PI-like controller based on the Newton–Euler mathematical model. Experimentally, the TGBOA algorithm is corroborated by developing three experiments where the F-450 quadcopter, MATLAB® 2022ª, PI-like controller, and Wi-Fi communication are applied. The TGBOA algorithm and the PI-like controller show functionality because the controller always follows the vector generated due to the obstacle avoidance. Full article
(This article belongs to the Special Issue Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications, 2nd Edition)
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14 pages, 2661 KiB  
Article
Impulsive Pinning Control of Discrete-Time Complex Networks with Time-Varying Connections
by Daniel Ríos-Rivera, Jorge D. Rios, Oscar D. Sanchez and Alma Y. Alanis
Mathematics 2022, 10(21), 4051; https://doi.org/10.3390/math10214051 - 01 Nov 2022
Cited by 1 | Viewed by 1194
Abstract
Complex dynamical networks with time-varying connections have characteristics that allow a better representation of real-world complex systems, especially interest in their not static behavior and topology. Their applications reach areas such as communication systems, electrical systems, medicine, robotic, and more. Both continuous and [...] Read more.
Complex dynamical networks with time-varying connections have characteristics that allow a better representation of real-world complex systems, especially interest in their not static behavior and topology. Their applications reach areas such as communication systems, electrical systems, medicine, robotic, and more. Both continuous and discrete-time complex dynamical networks and the pinning control technique have been studied. However, even with interest in the research on complex networks combining characteristics of discrete-time, time-varying connections, pinning control, and impulsive control, there are few studies reported in the literature. There are some previous studies dealing with impulsively pin-controlling a discrete-time complex network. Nevertheless, they neglect to deal with time-varying connections; they deal with these systems by experimentally using continuous-time methods or linearizing the node dynamics. In this manner, this paper presents a control scheme that not only deals with pin control on discrete-time complex networks but also includes time-varying connections. This paper proposes an impulsive pin control to a zero state using passivity degrees considering a discrete-time complex network with undirected, linear, and diffusive couplings. Additionally, a corresponding mathematical analysis, which allows the representation of the dynamics as a set of symmetric matrices, is presented. With this, certain kinds of time-varying connections can be integrated into the analysis. Moreover, a particular criterion for selecting nodes to pin is also presented. The behavior of the controller for the non-varying and time-varying coupling cases is shown via numeric simulations. Full article
(This article belongs to the Special Issue Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications, 2nd Edition)
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