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MCA
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Numerical and Evolutionary Optimization 2022
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Numerical and Evolutionary Optimization 2022

A special issue of Mathematical and Computational Applications (ISSN 2297-8747). This special issue belongs to the section "Engineering".

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 8229

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
Prof. Dr. Daniel Hernández

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Guest Editor
Departamento de Ingeniería Industrial, Tecnológico Nacional de México/Instituto Tecnológico de Tijuana, Calzada Tecnológico SN, Tomas Aquino, Tijuana 22414, México
Interests: data science; machine learning; evolutionary computation; HPC
Dr. Nelson Rangel-Valdez

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Guest Editor
Graduate Program Division, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero 89440, Mexico
Interests: combinatorial optimization; artificial intelligence; evolutionary computation
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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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 10th International Workshop on Numerical and Evolutionary Optimization (NEO 2022; 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 the 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
Prof. Dr. Daniel E. Hernández
Dr. Nelson Rangel-Valdez
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.

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

Published Papers (4 papers)

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21 pages, 7882 KiB  
Article
Performance Analysis of Multi-Objective Simulated Annealing Based on Decomposition
by Manuel Vargas-Martínez, Nelson Rangel-Valdez, Eduardo Fernández, Claudia Gómez-Santillán and María Lucila Morales-Rodríguez
Math. Comput. Appl. 2023, 28(2), 38; https://doi.org/10.3390/mca28020038 - 8 Mar 2023
Cited by 2 | Viewed by 1799
Abstract
Simulated annealing is a metaheuristic that balances exploration and exploitation to solve global optimization problems. However, to deal with multi- and many-objective optimization problems, this balance needs to be improved due to diverse factors such as the number of objectives. To deal with [...] Read more.
Simulated annealing is a metaheuristic that balances exploration and exploitation to solve global optimization problems. However, to deal with multi- and many-objective optimization problems, this balance needs to be improved due to diverse factors such as the number of objectives. To deal with this issue, this work proposes MOSA/D, a hybrid framework for multi-objective simulated annealing based on decomposition and evolutionary perturbation functions. According to the literature, the decomposition strategy allows diversity in a population while evolutionary perturbations add convergence toward the Pareto front; however, a question should be asked: What is the effect of such components when included as part of a multi-objective simulated annealing design? Hence, this work studies the performance of the MOSA/D framework considering in its implementation two widely used perturbation operators: classical genetic operators and differential evolution. The proposed algorithms are MOSA/D-CGO, based on classical genetic operators, and MOSA/D-DE, based on differential evolution operators. The main contribution of this work is the performance analysis of MOSA/D using both perturbation operators and identifying the one most suitable for the framework. The approaches were tested using DTLZ on two and three objectives and CEC2009 benchmarks on two, three, five, and ten objectives; the performance analysis considered diversity and convergence measured through the hypervolume (HV) and inverted generational distance (IGD) indicators. The results pointed out that there is a promising improvement in performance in favor of MOSA/D-DE. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2022)
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15 pages, 2253 KiB  
Article
Comprehensive Analysis of Learning Cases in an Autonomous Navigation Task for the Evolution of General Controllers
by Enrique Naredo, Candelaria Sansores, Flaviano Godinez, Francisco López, Paulo Urbano, Leonardo Trujillo and Conor Ryan
Math. Comput. Appl. 2023, 28(2), 35; https://doi.org/10.3390/mca28020035 - 2 Mar 2023
Viewed by 1841
Abstract
Robotics technology has made significant advancements in various fields in industry and society. It is clear how robotics has transformed manufacturing processes and increased productivity. Additionally, navigation robotics has also been impacted by these advancements, with investors now investing in autonomous transportation for [...] Read more.
Robotics technology has made significant advancements in various fields in industry and society. It is clear how robotics has transformed manufacturing processes and increased productivity. Additionally, navigation robotics has also been impacted by these advancements, with investors now investing in autonomous transportation for both public and private use. This research aims to explore how training scenarios affect the learning process for autonomous navigation tasks. The primary objective is to address whether the initial conditions (learning cases) have a positive or negative impact on the ability to develop general controllers. By examining this research question, the study seeks to provide insights into how to optimize the training process for autonomous navigation tasks, ultimately improving the quality of the controllers that are developed. Through this investigation, the study aims to contribute to the broader goal of advancing the field of autonomous navigation and developing more sophisticated and effective autonomous systems. Specifically, we conducted a comprehensive analysis of a particular navigation environment using evolutionary computing to develop controllers for a robot starting from different locations and aiming to reach a specific target. The final controller was then tested on a large number of unseen test cases. Experimental results provide strong evidence that the initial selection of the learning cases plays a role in evolving general controllers. This work includes a preliminary analysis of a specific set of small learning cases chosen manually, provides an in-depth analysis of learning cases in a particular navigation task, and develops a tool that shows the impact of the selected learning cases on the overall behavior of a robot’s controller. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2022)
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11 pages, 460 KiB  
Article
Hybrid Harmony Search Optimization Algorithm for Continuous Functions
by José Alfredo Brambila-Hernández, Miguel Ángel García-Morales, Héctor Joaquín Fraire-Huacuja, Eduardo Villegas-Huerta and Armando Becerra-del-Ángel
Math. Comput. Appl. 2023, 28(2), 29; https://doi.org/10.3390/mca28020029 - 22 Feb 2023
Cited by 1 | Viewed by 1790
Abstract
This paper proposes a hybrid harmony search algorithm that incorporates a method of reinitializing harmonies memory using a particle swarm optimization algorithm with an improved opposition-based learning method (IOBL) to solve continuous optimization problems. This method allows the algorithm to obtain better results [...] Read more.
This paper proposes a hybrid harmony search algorithm that incorporates a method of reinitializing harmonies memory using a particle swarm optimization algorithm with an improved opposition-based learning method (IOBL) to solve continuous optimization problems. This method allows the algorithm to obtain better results by increasing the search space of the solutions. This approach has been validated by comparing the performance of the proposed algorithm with that of a state-of-the-art harmony search algorithm, solving fifteen standard mathematical functions, and applying the Wilcoxon parametric test at a 5% significance level. The state-of-the-art algorithm uses an opposition-based improvement method (IOBL). Computational experiments show that the proposed algorithm outperforms the state-of-the-art algorithm. In quality, it is better in fourteen of the fifteen instances, and in efficiency is better in seven of fifteen instances. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2022)
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17 pages, 3275 KiB  
Article
A Radial Hybrid Estimation of Distribution Algorithm for the Truck and Trailer Routing Problem
by Ricardo Pérez-Rodríguez and Sergio Frausto-Hernández
Math. Comput. Appl. 2023, 28(1), 27; https://doi.org/10.3390/mca28010027 - 20 Feb 2023
Cited by 1 | Viewed by 1603
Abstract
The truck and trailer routing problem (TTRP) has been widely studied under different approaches. This is due to its practical characteristic that makes its research interesting. The TTRP continues to be attractive to developing new evolutionary algorithms. This research details a new estimation [...] Read more.
The truck and trailer routing problem (TTRP) has been widely studied under different approaches. This is due to its practical characteristic that makes its research interesting. The TTRP continues to be attractive to developing new evolutionary algorithms. This research details a new estimation of the distribution algorithm coupled with a radial probability function from hydrogen. Continuous values are used in the solution representation, and every value indicates, in a hydrogen atom, the distance between the electron and the core. The key point is to exploit the radial probability distribution to construct offspring and to tackle the drawbacks of the estimation of distribution algorithms. Various instances and numerical experiments are presented to illustrate and validate this novel research. Based on the performance of the proposed scheme, we can make the conclusion that incorporating radial probability distributions helps to improve the estimation of distribution algorithms. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2022)
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