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MCA
Special Issues
Numerical and Evolutionary Optimization 2024
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Numerical and Evolutionary Optimization 2024

A special issue of Mathematical and Computational Applications (ISSN 2297-8747).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 6318

Special Issue Editors

Dr. Marcela Quiroz-Castellanos

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Guest Editor
Centro de Investigación en Inteligencia Artificial, University of Veracruz, Xalapa 91000, Mexico
Interests: experimental algorithmics; metaheuristics; genetic algorithms; bin packing; machine learning; causal inference applications
Special Issues, Collections and Topics in MDPI journals
Dr. Oliver Cuate

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Guest Editor
Escuela Superior de Física y Matemáticas del Instituto Politécnico Nacional, Mexico City 07738, Mexico
Interests: many-objective optimization; numerical optimization; machine learning; industry applications
Dr. Leonardo Trujillo

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Guest Editor
Departamento de Ingeniería en Electrónica y Eléctrica, Instituto Tecnológico de Tijuana, Calzada Tecnológico SN, Tomas Aquino, Tijuana 22414, Mexico
Interests: evolutionary computation; machine learning; data science; computer vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Oliver Schütze

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Guest Editor
Depto de Computacion, Cinvestav, Mexico City 07360, Mexico
Interests: multi-objective optimization; evolutionary computation (genetic algorithms and evolution strategies); numerical analysis; engineering applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will mainly consist of selected papers presented at the 11th International Workshop on Numerical and Evolutionary Optimization (NEO 2024; see http://neo.cinvestav.mx for detailed information). However, other works that fit within the scope of the NEO are also welcome. Papers considered to fit the scope of the journal and to be of sufficient quality after evaluation by the reviewers will be published free of charge.

The aim of this Special Issue is to collect papers on the intersection of numerical and evolutionary optimization. We strongly encourage the development of fast and reliable hybrid methods that maximize the strengths and minimize the weaknesses of each underlying paradigm while also being applicable to a broader class of problems. Moreover, this Special Issue aims to foster an understanding and adequate treatment of real-world problems, particularly in emerging fields that affect us all, such as healthcare, smart cities, and big data, among many others.

Topics of interest include (but are not limited to) the following:

(A) Search and optimization:

  • Single- and multi-objective optimization;
  • Mathematical programming techniques;
  • Evolutionary algorithms;
  • Genetic programming;
  • Hybrid and memetic algorithms;
  • Set-oriented numerics;
  • Stochastic optimization;
  • Robust optimization.

(B) Real-world problems:

  • Optimization, machine learning, and metaheuristics applied to:
  • Energy production and consumption;
  • Health monitoring systems;
  • Computer vision and pattern recognition;
  • Energy optimization and prediction;
  • Modeling and control of real-world energy systems;
  • Smart cities.

Dr. Marcela Quiroz-Castellanos
Dr. Oliver Cuate
Dr. Leonardo Trujillo
Prof. Dr. Oliver Schütze
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. Mathematical and Computational Applications 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 1400 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

  • single- and multi-objective optimization
  • evolutionary algorithms and genetic programming
  • hybrid and memetic algorithms
  • set-oriented numerics
  • stochastic optimization
  • robust optimization

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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22 pages, 56577 KiB  
Article
Resolving Contrast and Detail Trade-Offs in Image Processing with Multi-Objective Optimization
by Daniel Molina-Pérez and Alam Gabriel Rojas-López
Math. Comput. Appl. 2024, 29(6), 104; https://doi.org/10.3390/mca29060104 - 11 Nov 2024
Viewed by 557
Abstract
This article addresses the complex challenge of simultaneously enhancing contrast and detail in an image, where improving one property often compromises the other. This trade-off is tackled using a multi-objective optimization approach. Specifically, the proposal’s model integrates the sigmoid transformation function and unsharp [...] Read more.
This article addresses the complex challenge of simultaneously enhancing contrast and detail in an image, where improving one property often compromises the other. This trade-off is tackled using a multi-objective optimization approach. Specifically, the proposal’s model integrates the sigmoid transformation function and unsharp masking highboost filtering with the NSGA-II algorithm. Additionally, a posterior preference articulation is introduced to select three key solutions from the Pareto front: the maximum contrast solution, the maximum detail solution, and the knee point solution. The proposed technique is evaluated on a range of image types, including medical and natural scenes. The final solutions demonstrated significant superiority in terms of contrast and detail compared to the original images. The three selected solutions, although all are optimal, captured distinct characteristics within the images, offering different solutions according to field preferences. This highlights the method’s effectiveness across different types and enhancement requirements and emphasizes the importance of the proposed preferences in different contexts. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2024)
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14 pages, 552 KiB  
Article
Design and Implementation of a Discrete-PDC Controller for Stabilization of an Inverted Pendulum on a Self-Balancing Car Using a Convex Approach
by Yasmani González-Cárdenas, Francisco-Ronay López-Estrada, Víctor Estrada-Manzo, Joaquin Dominguez-Zenteno and Manuel López-Pérez
Math. Comput. Appl. 2024, 29(5), 83; https://doi.org/10.3390/mca29050083 - 18 Sep 2024
Viewed by 951
Abstract
This paper presents a trajectory-tracking controller of an inverted pendulum system on a self-balancing differential drive platform. First, the system modeling is described by considering approximations of the swing angles. Subsequently, a discrete convex representation of the system via the nonlinear sector technique [...] Read more.
This paper presents a trajectory-tracking controller of an inverted pendulum system on a self-balancing differential drive platform. First, the system modeling is described by considering approximations of the swing angles. Subsequently, a discrete convex representation of the system via the nonlinear sector technique is obtained, which considers the nonlinearities associated with the nonholonomic constraint. The design of a discrete parallel distributed compensation controller is achieved through an alternative method due to the presence of uncontrollable points that avoid finding a solution for the entire polytope. Finally, simulations and experimental results using a prototype illustrate the effectiveness of the proposal. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2024)
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20 pages, 3041 KiB  
Article
Human Activity Recognition from Accelerometry, Based on a Radius of Curvature Feature
by Elizabeth Cavita-Huerta, Juan Reyes-Reyes, Héctor M. Romero-Ugalde, Gloria L. Osorio-Gordillo, Ricardo F. Escobar-Jiménez and Victor M. Alvarado-Martínez
Math. Comput. Appl. 2024, 29(5), 80; https://doi.org/10.3390/mca29050080 - 13 Sep 2024
Viewed by 673
Abstract
Physical activity recognition using accelerometry is a rapidly advancing field with significant implications for healthcare, sports science, and wearable technology. This research presents an interesting approach for classifying physical activities using solely accelerometry data, signals that were taken from the available “MHEALTH dataset” [...] Read more.
Physical activity recognition using accelerometry is a rapidly advancing field with significant implications for healthcare, sports science, and wearable technology. This research presents an interesting approach for classifying physical activities using solely accelerometry data, signals that were taken from the available “MHEALTH dataset” and processed through artificial neural networks (ANNs). The methodology involves data acquisition, preprocessing, feature extraction, and the application of deep learning algorithms to accurately identify activity patterns. A major innovation in this study is the incorporation of a new feature derived from the radius of curvature. This time-domain feature is computed by segmenting accelerometry signals into windows, conducting double integration to derive positional data, and subsequently estimating a circumference based on the positional data obtained within each window. This characteristic is computed across the three movement planes, providing a robust and comprehensive feature for activity classification. The integration of the radius of curvature into the ANN models significantly enhances their accuracy, achieving over 95%. In comparison with other methodologies, our proposed approach, which utilizes a feedforward neural network (FFNN), demonstrates superior performance. This outperforms previous methods such as logistic regression, which achieved 93%, KNN models with 90%, and the InceptTime model with 88%. The findings demonstrate the potential of this model to improve the precision and reliability of physical activity recognition in wearable health monitoring systems. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2024)
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16 pages, 2099 KiB  
Article
Modeling of the Human Cardiovascular System: Implementing a Sliding Mode Observer for Fault Detection and Isolation
by Dulce A. Serrano-Cruz, Latifa Boutat-Baddas, Mohamed Darouach, Carlos M. Astorga-Zaragoza and Gerardo V. Guerrero Ramírez
Math. Comput. Appl. 2024, 29(4), 57; https://doi.org/10.3390/mca29040057 - 17 Jul 2024
Cited by 1 | Viewed by 1014
Abstract
This paper presents a mathematical model of the cardiovascular system (CVS) designed to simulate both normal and pathological conditions within the systemic circulation. The model introduces a novel representation of the CVS through a change of coordinates, transforming it into the “quadratic normal [...] Read more.
This paper presents a mathematical model of the cardiovascular system (CVS) designed to simulate both normal and pathological conditions within the systemic circulation. The model introduces a novel representation of the CVS through a change of coordinates, transforming it into the “quadratic normal form”. This model facilitates the implementation of a sliding mode observer (SMO), allowing for the estimation of system states and the detection of anomalies, even though the system is linearly unobservable. The primary focus is on identifying valvular heart diseases, which are significant risk factors for cardiovascular diseases. The model’s validity is confirmed through simulations that replicate hemodynamic parameters, aligning with existing literature and experimental data. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2024)
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18 pages, 625 KiB  
Article
Computational Cost Reduction in Multi-Objective Feature Selection Using Permutational-Based Differential Evolution
by Jesús-Arnulfo Barradas-Palmeros, Efrén Mezura-Montes, Rafael Rivera-López, Hector-Gabriel Acosta-Mesa and Aldo Márquez-Grajales
Math. Comput. Appl. 2024, 29(4), 56; https://doi.org/10.3390/mca29040056 - 13 Jul 2024
Viewed by 912
Abstract
Feature selection is a preprocessing step in machine learning that aims to reduce dimensionality and improve performance. The approaches for feature selection are often classified according to the evaluation of a subset of features as filter, wrapper, and embedded approaches. The high performance [...] Read more.
Feature selection is a preprocessing step in machine learning that aims to reduce dimensionality and improve performance. The approaches for feature selection are often classified according to the evaluation of a subset of features as filter, wrapper, and embedded approaches. The high performance of wrapper approaches for feature selection is associated at the same time with the disadvantage of high computational cost. Cost-reduction mechanisms for feature selection have been proposed in the literature, where competitive performance is achieved more efficiently. This work applies the simple and effective resource-saving mechanisms of the fixed and incremental sampling fraction strategies with memory to avoid repeated evaluations in multi-objective permutational-based differential evolution for feature selection. The selected multi-objective approach is an extension of the DE-FSPM algorithm with the selection mechanism of the GDE3 algorithm. The results showed high resource savings, especially in computational time and the number of evaluations required for the search process. Nonetheless, it was also detected that the algorithm’s performance was diminished. Therefore, the results reported in the literature on the effectiveness of the strategies for cost reduction in single-objective feature selection were only partially sustained in multi-objective feature selection. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2024)
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15 pages, 823 KiB  
Article
H∞ State and Parameter Estimation for Lipschitz Nonlinear Systems
by Pedro Eusebio Alvarado-Méndez, Carlos M. Astorga-Zaragoza, Gloria L. Osorio-Gordillo, Adriana Aguilera-González, Rodolfo Vargas-Méndez and Juan Reyes-Reyes
Math. Comput. Appl. 2024, 29(4), 51; https://doi.org/10.3390/mca29040051 - 4 Jul 2024
Viewed by 924
Abstract
A H∞ robust adaptive nonlinear observer for state and parameter estimation of a class of Lipschitz nonlinear systems with disturbances is presented in this work. The objective is to estimate parameters and monitor the performance of nonlinear processes with model uncertainties. The [...] Read more.
A H∞ robust adaptive nonlinear observer for state and parameter estimation of a class of Lipschitz nonlinear systems with disturbances is presented in this work. The objective is to estimate parameters and monitor the performance of nonlinear processes with model uncertainties. The behavior of the observer in the presence of disturbances is analyzed using Lyapunov stability theory and by considering an H∞ performance criterion. Numerical simulations were carried out to demonstrate the applicability of this observer for a semi-active car suspension. The adaptive observer performed well in estimating the tire rigidity (as an unknown parameter) and induced disturbances representing damage to the damper. The main contribution is the proposal of an alternative methodology for simultaneous parameter and actuator disturbance estimation for a more general class of nonlinear systems. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2024)
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