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Mathematics
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Artificial Intelligence and Metaheuristics: Connections and Applications
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Artificial Intelligence and Metaheuristics: Connections and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 7054

Special Issue Editors

Prof. Dr. Carlos-Alberto Cobos-Lozada

E-Mail Website
Guest Editor
Computer Science Department, Universidad del Cauca, Popayán 190001, Colombia
Interests: artificial intelligence; metaheuristics; data science; information retrieval; NLP
Dr. Diego Hernán Peluffo-Ordóñez

E-Mail Website
Guest Editor
College of Computing, Mohammed VI Polytechnic University, Ben Guerir 47963, Morocco
Interests: clustering; dimensionality reduction; kernel methods; digital signal processing
Prof. Dr. Himer Avila

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Universidad de Guadalajara, Ameca 46600, Mexico
Interests: machine learning; combinatorial optimization; sustainable environmental studies

Special Issue Information

Dear Colleagues,

Humanity's problems are becoming increasingly complex on a daily basis, and approaches that hybridize different techniques have been critical to solving many of these problems. Artificial intelligence (machine learning, deep learning, reinforcement learning, expert systems, and fuzzy systems) and metaheuristics (genetic algorithms, memetic algorithms, swarm intelligence, and differential evolution, among others) are two important examples of fields of knowledge that can be synergistically combined to solve highly complex problems.

In this Special Issue, we are interested in recent solutions that explicitly show the interrelation of these two fields (AI and metaheuristics) for the solution of academic, scientific, or industrial problems where, in addition, it is evident how the proposed solution was subjected to an evaluation concerning the quality of the obtained solution, the execution time, or other relevant metrics. Topics include, but are not limited to, the following:

  • Hybrid intelligent optimization techniques for solving very high-complexity problems.
  • Applications in industry, transportation, health, agriculture, services, and others.
  • Metaheuristic algorithms enhanced with artificial intelligence techniques and vice versa.

Prof. Dr. Carlos-Alberto Cobos-Lozada
Dr. Diego Hernán Peluffo-Ordóñez
Prof. Dr. Himer Avila
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. 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.

Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • metaheuristics
  • nature-inspired algorithms
  • hybrid approaches

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Published Papers (3 papers)

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Research

20 pages, 1085 KiB  
Article
Improving the Automatic Detection of Dropout Risk in Middle and High School Students: A Comparative Study of Feature Selection Techniques
by Daniel Zapata-Medina, Albeiro Espinosa-Bedoya and Jovani Alberto Jiménez-Builes
Mathematics 2024, 12(12), 1776; https://doi.org/10.3390/math12121776 - 7 Jun 2024
Cited by 4 | Viewed by 1838
Abstract
The dropout rate in underdeveloped and emerging countries is a pressing social issue, as highlighted by studies conducted by The Organization for Economic Co-operation and Development. This study compares five feature selection techniques to address this challenge and improve the automatic detection of [...] Read more.
The dropout rate in underdeveloped and emerging countries is a pressing social issue, as highlighted by studies conducted by The Organization for Economic Co-operation and Development. This study compares five feature selection techniques to address this challenge and improve the automatic detection of dropout risk. The methodological design involves three distinct phases: data preparation, feature selection, and model evaluation utilizing machine learning algorithms. The results demonstrate that (1) the top features identified by feature selection techniques, i.e., those constructed through feature engineering, proved to be among the most effective in classifying student dropout; (2) the F-score of the best model increased by 5% with feature selection techniques; and (3) depending on the type of feature selection, the performance of the machine learning algorithm can vary, potentially increasing or decreasing based on the sensitivity of features with higher noise. At the same time, metaheuristic algorithms demonstrated significant precision improvements, but there was a risk of increasing errors and reducing recall. Full article
(This article belongs to the Special Issue Artificial Intelligence and Metaheuristics: Connections and Applications)
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17 pages, 3140 KiB  
Article
MOR-SLAM: A New Visual SLAM System for Indoor Dynamic Environments Based on Mask Restoration
by Chengzhi Yao, Lei Ding and Yonghong Lan
Mathematics 2023, 11(19), 4037; https://doi.org/10.3390/math11194037 - 22 Sep 2023
Cited by 2 | Viewed by 1546
Abstract
The traditional Simultaneous Localization and Mapping (SLAM) systems are based on the strong static assumption, and their performance will degrade significantly due to the presence of dynamic objects located in dynamic environments. To decrease the effects of the dynamic objects, based on the [...] Read more.
The traditional Simultaneous Localization and Mapping (SLAM) systems are based on the strong static assumption, and their performance will degrade significantly due to the presence of dynamic objects located in dynamic environments. To decrease the effects of the dynamic objects, based on the ORB-SLAM2 system, a novel dynamic semantic SLAM system called MOR-SLAM is presented using a mask repair method, which can accurately detect dynamic objects and realize high-precision positioning and tracking of the system in dynamic indoor environments. First, an instance segmentation module is added to the front end of ORB-SLAM2 to distinguish dynamic and static objects in the environment and obtain a preliminary mask. Next, to overcome the under-segmentation problem in instance segmentation, a new mask inpainting model is proposed to ensure that the integrity of object masks, which repairs large objects and small objects in the image with the depth value fusion method and morphological method, respectively. Then, a reliable basic matrix can be obtained based on the above-repaired mask. Finally, the potential dynamic feature points in the environment are detected and removed through the reliable basic matrix, and the remaining static feature points are input into the tracking module of the system to realize the high-precision positioning and tracking in dynamic environments. The experiments on the public TUM dataset show that, compared with ORB-SLAM2, the MOR-SLAM improves the absolute trajectory accuracy by 95.55%. In addition, compared with DynaSLAM and DS-SLAM on the high-dynamic sequences (fr3/w/rpy and fr3/w/static), the MOR-SLAM improves the absolute trajectory accuracy by 15.20% and 59.71%, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence and Metaheuristics: Connections and Applications)
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26 pages, 1926 KiB  
Article
Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network
by Moises Ramos-Martinez, Carlos Alberto Torres-Cantero, Gerardo Ortiz-Torres, Felipe D. J. Sorcia-Vázquez, Himer Avila-George, Ricardo Eliú Lozoya-Ponce, Rodolfo A. Vargas-Méndez, Erasmo M. Renteria-Vargas and Jesse Y. Rumbo-Morales
Mathematics 2023, 11(18), 3967; https://doi.org/10.3390/math11183967 - 19 Sep 2023
Cited by 6 | Viewed by 2089
Abstract
This paper introduces a new approach to controlling Pressure Swing Adsorption (PSA) using a neural network controller based on a Model Predictive Control (MPC) process. We use a Hammerstein–Wiener (HW) model representing the real PSA process data. Then, we design an MPC-controlled model [...] Read more.
This paper introduces a new approach to controlling Pressure Swing Adsorption (PSA) using a neural network controller based on a Model Predictive Control (MPC) process. We use a Hammerstein–Wiener (HW) model representing the real PSA process data. Then, we design an MPC-controlled model based on the HW model to maintain the bioethanol purity near 99% molar fraction. This work proposes an Artificial Neural Network (ANN) that captures the dynamics of the PSA model controlled by the MPC strategy. Both controllers are validated using the HW model of the PSA process, showing great performance and robustness against disturbances. The results show that we can follow the desired trajectory and attenuate disturbances, achieving the purity of bioethanol at a molar fraction value of 0.99 using the ANN based on the MPC strategy with 94% of fit in the control signal and a 97% fit in the purity signal, so we can conclude that our ANN can be used to attenuate disturbances and maintain purity in the PSA process. Full article
(This article belongs to the Special Issue Artificial Intelligence and Metaheuristics: Connections and Applications)
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