Special Issue: “Machine Learning, Metaheuristics and Combinatorial Optimization Problems”

Mathematics (ISSN 2227-7390) is a peer-reviewed open-access journal that provides an advanced forum for studies related to mathematics and is published monthly online by MDPI.

Open Access – free for readers, with article processing charges (APC) paid by authors or their institutions.
High visibility:indexed within Scopus, SCIE (Web of Science), RePEc, and other databases.
Rapid publication: manuscripts are peer-reviewed, and a first decision provided to authors approximately 17.8 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the first half of 2022).
Recognition of reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitled to a discount on the APC of their next publication in any MDPI journal in appreciation of the work done.

Impact Factor: 2.592 (2021) ; 5-Year Impact Factor: 2.542 (2021) (First decile JCR journal) JCR – Q1 (Mathematics) / CiteScore – Q1 (General Mathematics)

Special Issue “Machine Learning, Metaheuristics and Combinatorial Optimization Problems”

Deadline for manuscript submissions: 10 February 2023.

Special Issue Editors

Prof. Dr. Víctor Yepes E-Mail Website SciProfiles Guest Editor

Institute of Concrete Science and Technology (ICITECH), Universitat Politècnica de València, 46022 València, Spain
Interests: multiobjective optimization; structures optimization; lifecycle assessment; social sustainability of infrastructures; reliability-based maintenance optimization; optimization and decision-making under uncertainty
Special Issues, Collections and Topics in MDPI journals

Dr. José Antonio García E-Mail Website Guest Editor

Escuela de Ingeniería en Construcción, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile
Interests: optimization; deep learning; operations research; artificial intelligence applications to industrial problems
Special Issues, Collections and Topics in MDPI journals

Dr. Broderick Crawford E-Mail Website SciProfiles Guest Editor

Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile
Interests: information systems; management information systems; operations research; constraint satisfaction problems; collaboration of solvers

Special Issue Information

Dear Colleagues,

Complex combinatorial problems have been successfully addressed through metaheuristic techniques. However, as the size of the problem increases, so does the need for robust optimization algorithms. An interesting method of strengthening these algorithms is through the application of hybrid techniques, specifically the hybridization of machine learning and metaheuristics. We invite researchers to submit articles on combined optimization and hybrid techniques for this Special Issue. Benchmarking problems or applications in the industry are also of interest.

The areas of machine learning and data science have received considerable research interest in recent years. These techniques have strongly excelled in supporting decision-making in complex and data-intensive scenarios. In this Special Issue, we are additionally interested in contributions to machine learning applications in the industry.

Prof. Víctor Yepes
Dr. José Antonio García
Dr. Broderick Crawford
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 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 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI’s English editing service before publication or during author revisions.

Optimización de la estrategia de desarrollo sostenible en la gestión de proyectos de ingeniería internacionales

Acaban de publicarnos un artículo en la revista Mathematics, revista indexada en el primer decil del JCR. En este caso se ha desarrollado una aplicación para la optimización de una estrategia sostenible en la gestión de un proyecto de ingeniería internacional. El trabajo se enmarca dentro del proyecto de investigación HYDELIFE que dirijo como investigador principal en la Universitat Politècnica de València.

El objetivo de este artículo es establecer un marco internacional para la gestión sostenible de proyectos en ingeniería, completar la investigación en este campo y proponer una base teórica para el establecimiento de un nuevo sistema de gestión de proyectos. El artículo adopta como método de investigación la revisión de la literatura, un algoritmo de programación matemática y el estudio de casos. La revisión de la literatura analizó los resultados de 21 años de investigación en este campo. Como resultado, se constató que el sistema de gestión de proyectos presenta deficiencias. Se estableció un modelo matemático para analizar la composición y los elementos del sistema optimizado de gestión de proyectos internacionales. La investigación de casos seleccionó grandes puentes para su análisis y verificó la superioridad y viabilidad del sistema teórico propuesto. La aportación de esta nueva investigación radica en el establecimiento de un modelo de sistema de gestión de proyectos internacional completo; en la integración del desarrollo sostenible con la gestión de proyectos; y en la propuesta de nuevos marcos de investigación y modelos de gestión para promover el desarrollo sostenible de la industria de la construcción.

Abstract:

The aim of this paper is to establish an international framework for sustainable project management in engineering, to make up the lack of research in this field, and to propose a scientific theoretical basis for the establishment of a new project management system. The article adopts literature review, mathematical programming algorithm and case study as the research method. The literature review applied the visual clustering research method and analyzed the results of 21-year research in this field. As a result, the project management system was found to have defects and deficiencies. A mathematical model was established to analyze the composition and elements of the optimized international project management system. The case study research selected large bridges for analysis and verified the superiority and practicability of the theoretical system. Thus, the goal of sustainable development of bridges was achieved. The value of this re-search lies in establishing a comprehensive international project management system model; truly integrating sustainable development with project management; providing new research frames and management models to promote the sustainable development of the construction industry.

Keywords:

Bridge; project management; environmental impact; cost; optimization

Reference:

ZHOU, Z.; ALCALÁ, J.; YEPES, V. (2021). Optimized application of sustainable development strategy in international engineering project management. Mathematics, 9(14):1633. DOI:10.3390/math9141633

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Discretización de metaheurísticas continuas a través de un operador KNN

Acaban de publicarnos un artículo en la revista Mathematics, revista indexada en el primer cuartil del JCR. En este caso hemos abordado la binarización de metaheurísticas continuas. Se trata de una estrategia muy útil para el caso de la optimización de estructuras, puesto que éstas suelen presentar variables discretas para favoreces su constructabilidad. El trabajo entra dentro de la estrecha colaboración internacional de nuestro grupo de investigación, en este caso, con investigaciones chilenos.

En este trabajo se propone un operador de perturbación que utiliza la técnica de k-vecinos más cercanos, y se estudia con el objetivo de mejorar las propiedades de diversificación e intensificación de los algoritmos metaheurísticos en su versión binaria. Se diseñan operadores aleatorios para estudiar la contribución del operador de perturbación. Para verificar la propuesta, se estudian grandes instancias del conocido problema de cobertura de conjuntos. Se utilizan gráficos de caja, gráficos de convergencia y la prueba estadística de Wilcoxon para determinar la contribución del operador. Además, se realiza una comparación con técnicas metaheurísticas que utilizan mecanismos generales de binarización como las funciones de transferencia o el db-scan como métodos de binarización. Los resultados obtenidos indican que el operador de perturbación KNN mejora significativamente los resultados.

ABSTRACT:

The optimization methods and, in particular, metaheuristics must be constantly improved to reduce execution times, improve the results, and thus be able to address broader instances. In particular, addressing combinatorial optimization problems is critical in the areas of operational research and engineering. In this work, a perturbation operator is proposed which uses the k-nearest neighbors technique, and this is studied with the aim of improving the diversification and intensification properties of metaheuristic algorithms in their binary version. Random operators are designed to study the contribution of the perturbation operator. To verify the proposal, large instances of the well-known set covering problem are studied. Box plots, convergence charts, and the Wilcoxon statistical test are used to determine the operator contribution. Furthermore, a comparison is made using metaheuristic techniques that use general binarization mechanisms such as transfer functions or db-scan as binarization methods. The results obtained indicate that the KNN perturbation operator improves significantly the results.

KEYWORDS:

Combinatorial optimization; machine learning; KNN; metaheuristics; transfer functions

REFERENCE:

GARCÍA, J.; ASTORGA, G.; YEPES, V. (2021). An analysis of a KNN perturbation operator: an application to the binarization of continuous metaheuristics. Mathematics, 9(3):225. DOI:10.3390/math9030225.

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Open Access Book: Optimization for Decision Making II

Tengo el placer de compartir con todos vosotros, totalmente en abierto, un libro que he editado junto con el profesor de la Universidad de Zaragoza, José María Moreno Jiménez. La labor de editar libros científicos es una oportunidad de poder seleccionar aquellos autores y temas que destacan en un ámbito determinado. En este caso, la optimización en la toma de decisiones.

Este libro forma parte de una serie sobre toma de decisiones. Podéis descargar también el primer libro de la serie en la siguiente dirección: https://victoryepes.blogs.upv.es/2020/10/09/open-access-book-optimization-for-decision-making/

Además, resulta gratificante ver que el libro se encuentra editado en abierto, por lo que cualquiera de vosotros os lo podéis descargar sin ningún tipo de problema en esta entrada del blog. También os lo podéis descargar, o incluso pedirlo en papel, en la página web de la editorial MPDI: https://www.mdpi.com/books/pdfview/book/3129

Referencia:

YEPES, V.; MORENO-JIMÉNEZ, J.M. (Eds.) (2020). Optimization for Decision Making II. MPDI, 302 pp., Basel, Switzerland. ISBN 978-3-03943-607-1

Preface to ”Optimization for Decision Making II”

Decision making is one of the distinctive activities of the human being; it is an indication of the degree of evolution, cognition and freedom of the species. In the past, until the end of the 20th century, scientific decision making was based on the paradigms of substantive rationality (normative approach) and procedural rationality (descriptive approach). Since the beginning of the 21st century and the advent of the Knowledge Society, decision making has been enriched with new constructivist, evolutionary and cognitive paradigms that aim to respond to new challenges and needs; especially the integration into formal models of the intangible, subjective and emotional aspects associated with the human factor, and the participation in decision-making processes of spatially distributed multiple actors that intervene in a synchronous or an asynchronous manner. To help address and resolve these types of questions, this book comprises 16 chapters that present a series of decision models, methods and techniques and their practical applications in the fields of economics, engineering and social sciences. The chapters collect the papers included in the “Optimization for Decision Making II” Special Issue of the Mathematics journal, 2020, 8(6), first decile of the JCR 2019 in the Mathematics category. We would like to thank both the MDPI publishing and editorial staff for their excellent work, as well as the 51 authors who have collaborated in its preparation. The papers cover a wide spectrum of issues related to the scientific resolution of problems; in particular, related to decision making, optimization, metaheuristics, and multi-criteria decision making. We hope that the papers, with their undoubted mathematical content, can be of use to academics and professionals from the many branches of knowledge (philosophy, psychology, economics, mathematics, decision science, computer science, artificial intelligence, neuroscience and more) that have, from such diverse perspectives, approached the study of decision making, an essential aspect of human life and development.

Víctor Yepes, José M. Moreno-Jiménez
Editors

About the Editors

Víctor Yepes Full Professor of Construction Engineering; he holds a Ph.D. degree in civil engineering. He serves at the Department of Construction Engineering, Universitat Politecnica de Valencia, Valencia, Spain. He has been the Academic Director of the M.S. studies in concrete materials and structures since 2007 and a Member of the Concrete Science and Technology Institute (ICITECH). He is currently involved in several projects related to the optimization and life-cycle assessment
of concrete structures as well as optimization models for infrastructure asset management. He is currently teaching courses in construction methods, innovation, and quality management. He authored more than 250 journals and conference papers including more than 100 published in the journal quoted in JCR. He acted as an Expert for project proposals evaluation for the Spanish Ministry of Technology and Science, and he is the Main Researcher in many projects. He currently serves as the Editor-in-Chief of the International Journal of Construction Engineering and Management and a member of the editorial board of 12 international journals (Structure & Infrastructure Engineering, Structural Engineering and Mechanics, Mathematics, Sustainability, Revista de la Construcción, Advances in Civil Engineering, and Advances in Concrete Construction, among others).

José María Moreno-Jiménez Full Professor of Operations Research and Multicriteria Decision Making, received the degrees in mathematics and economics as well as a Ph.D. degree in applied mathematics from the University of Zaragoza, Spain; where he is teaching from the course 1980–1981. He is the Head of the Quantitative Methods Area in the Faculty of Economics and Business of the University of Zaragoza from 1997, the Chair of the Zaragoza Multicriteria Decision Making Group from 1996, a member of the Advisory Board of the Euro Working Group on Decision Support Systems from 2017, and an Honorary Member of the International Society on Applied Economics ASEPELT from 2019. He has also been the President of this international scientific society (2014–2018) and the Coordinator of the Spanish Multicriteria Decision Making Group (2012–2015). His research interests are in the general area of Operations Research theory and practice, with an emphasis on multicriteria decision making, electronic democracy/cognocracy, performance analysis, and industrial and technological diversification. He has published more than 250 papers in scientific books and journals in the most prestigious editorials and is a member of the Editorial Board of several national and international journals.

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Special Issue “Optimization for Decision Making III”

Mathematics (ISSN 2227-7390) is a peer-reviewed open access journal which provides an advanced forum for studies related to mathematics, and is published monthly online by MDPI.

Open Access – free for readers, with article processing charges (APC) paid by authors or their institutions.
High visibility: Indexed in the Science Citation Indexed Expanded – SCIE (Web of Science) from Vol. 4 (2016), Scopus, and Zentralblatt MATH from Vol. 3 (2015).
Rapid publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 21.7 days after submission; acceptance to publication is undertaken in 5.3 days (median values for papers published in this journal in the second half of 2018).
Recognition of reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 1.747 (2019) (First decile JCR journal)

Special Issue “Optimization for Decision Making III”

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Guest Editor Prof. Víctor Yepes Universitat Politècnica de València, Spain Website \| E-Mail Interests: multiobjective optimization; structures optimization; lifecycle assessment; social sustainability of infrastructures; reliability-based maintenance optimization; optimization and decision-making under uncertainty
Guest Editor Prof. José M. Moreno-Jiménez Universidad de Zaragoza Website \| E-Mail Interests: multicriteria decision making; environmental selection; strategic planning; knowledge management; evaluation of systems; logistics and public decision making (e-government, e-participation, e-democracy and e-cognocracy)

Special Issue Information

Dear Colleagues,

In the current context of the electronic governance of society, both administrations and citizens are demanding greater participation of all the actors involved in the decision-making process relative to the governance of society. In addition, the design, planning, and operations management rely on mathematical models, the complexity of which depends on the detail of models and complexity/characteristics of the problem they represent. Unfortunately, decision-making by humans is often suboptimal in ways that can be reliably predicted. Furthermore, the process industry seeks not only to minimize cost, but also to minimize adverse environmental and social impacts. On the other hand, in order to give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and application of optimization techniques to support decisions is particularly complex, and a wide range of optimization techniques and methodologies are used to minimize risks or improve quality in making concomitant decisions. In addition, a sensitivity analysis should be done to validate/analyze the influence of uncertainty regarding decision-making.

Prof. Víctor Yepes
Prof. José Moreno-Jiménez
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 papers will be 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 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 1200 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

Multicriteria decision making
Optimization techniques
Multiobjective optimization

Open Access Book: Optimization for Decision Making

Referencia:

YEPES, V.; MORENO-JIMÉNEZ, J.M. (Eds.) (2020). Optimization for Decision Making. MPDI, 277 pp., Basel, Switzerland. ISBN: 978-3-03943-221-9

Preface to ”Optimization for Decision Making”

Decision making is one of the distinctive activities of the human being; it is an indication of the degree of evolution, cognition, and freedom of the species. In the past, until the end of the 20th century, scientific decision-making was based on the paradigms of substantive rationality (normative approach) and procedural rationality (descriptive approach). Since the beginning of the 21st century and the advent of the Knowledge Society, decision-making has been enriched with new constructivist, evolutionary, and cognitive paradigms that aim to respond to new challenges and needs; especially the integration into formal models of the intangible, subjective, and emotional aspects associated with the human factor, and the participation in decision-making processes of spatially distributed multiple actors that intervene in a synchronous or asynchronous manner. To help address and resolve these types of questions, this book comprises 13 chapters that present a series of decision models, methods, and techniques and their practical applications in the fields of economics, engineering, and social sciences. The chapters collect the papers included in the “Optimization for Decision Making” Special Issue of the Mathematics journal, 2019, 7(3), first decile of the JCR 2019 in the Mathematics category. We would like to thank both the MDPI publishing editorial team, for their excellent work, and the 47 authors who have collaborated in its preparation. The papers cover a wide spectrum of issues related to the scientific resolution of problems; in particular, related to decision making, optimization, metaheuristics, simulation, and multi-criteria decision-making. We hope that the papers, with their undoubted mathematical content, can be of use to academics and professionals from the many branches of knowledge (philosophy, psychology, economics, mathematics, decision science, computer science, artificial intelligence, neuroscience, and more) that have, from such diverse perspectives, approached the study of decision-making, an essential aspect of human life and development.

Víctor Yepes, José María Moreno-Jiménez
Editors

About the Editors

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Special Issue “Deep Learning and Hybrid-Metaheuristics: Novel Engineering Applications”

Mathematics (ISSN 2227-7390) is a peer-reviewed open access journal which provides an advanced forum for studies related to mathematics, and is published monthly online by MDPI. The European Society for Fuzzy Logic and Technology (EUSFLAT) and International Society for the Study of Information (IS4SI) are affiliated with Mathematics and their members receive a discount on article processing charges.

Open Access—free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: Indexed in the Science Citation Indexed Expanded – SCIE (Web of Science) from Vol. 4 (2016) and Scopus.
Rapid Publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 16.4 days after submission; acceptance to publication is undertaken in 4.6 days (median values for papers published in this journal in the first half of 2020).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 1.747 (2019) (First decile JCR)

Special Issue “Deep Learning and Hybrid-Metaheuristics: Novel Engineering Applications”

Deadline for manuscript submissions: 30 April 2021.

Special Issue Editors

Prof. Dr. Víctor Yepes Website SciProfiles
Guest Editor

ICITECH, Universitat Politècnica de València, Valencia, Spain
Interests: multiobjective optimization; structure optimization; lifecycle assessment; social sustainability of infrastructures; reliability-based maintenance optimization; optimization and decision-making under uncertainty
Special Issues and Collections in MDPI journals

Dr. José Antonio García Website
Guest Editor

Pontificia Universidad Católica de Valparaíso, Chile
Interests: optimization; deep learning; operations research; artificial intelligence applications to industrial problems

Special Issue Information

Dear Colleagues,

Hybrid metaheuristic methods have shown very good performances in different combinatorial problems. Additionally, the rise of machine learning techniques has created a space to develop metaheuristic algorithms that use these techniques in order to tackle NP-hard problems and improve the convergence of algorithms. In this Special Issue, we invite researchers to submit papers in this optimization line, applying hybrid algorithms to industrial problems, including but not limited to industrial applications, and challenging problems arising in the fields of big data, construction, sustainability, transportation, and logistics, among others.

Deep learning techniques have also been important tools in extracting features, classifying situations, predicting events, and assisting in decision making. Some of these tools have been applied, for example, to Industry 4.0. Among the main techniques used are feedforward networks (FNN), convolutional networks (CNN), long-term short memory (LSTM), autoencoders (AE), enerative adversarial networks, and deep Q-networks (DQNs). Contributions on practical deep learning applications and cases are invited to this Special Issue, including but not limited to applications to the industry of computational vision, natural language processing, supervised learning applied to industry, unsupervised learning applied to industry, and reinforcement learning, among others.

Prof. Dr. Víctor Yepes
Dr. José Antonio García
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 papers will be 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.

Keywords

Construction
Smart cities
Optimization
Deep learning

Optimización de muros de contrafuertes mediante algoritmo híbrido de enjambre de partículas y clustering

Acaban de publicarnos un artículo en la revista Mathematics, revista indexada en el primer cuartil del JCR. En este artículo se presenta un algoritmo híbrido de enjambre de partículas y clustering para optimizar el coste y las emisiones de CO $_{2}$ de un muro de contrafuertes. El trabajo se enmarca dentro del proyecto de investigación DIMALIFE que dirijo como investigador principal en la Universitat Politècnica de València.

El diseño de los muros de contrafuertes es un problema de optimización combinatoria de interés debido a las aplicaciones prácticas relativas al ahorro de costos que implica el diseño y la optimización en la cantidad de emisiones de CO $_{2}$ generadas en su construcción. Por otro lado, este problema presenta importantes retos en cuanto a complejidad computacional, pues involucra 32 variables de diseño, por lo que tenemos en el orden de 10^20 combinaciones posibles. En este artículo proponemos un algoritmo híbrido en el que se integra el método de optimización del enjambre de partículas que resuelve los problemas de optimización en espacios continuos con la técnica de clustering db-scan. Este algoritmo optimiza dos funciones objetivo: las emisiones de carbono y el costo económico de los muros de hormigón armado. Para evaluar la contribución del operador del db-scan en el proceso de optimización, se diseñó un operador aleatorio. Se comparan las mejores soluciones, los promedios y los rangos intercuartílicos de las distribuciones obtenidas. A continuación se comparó el algoritmo db-scan con una versión híbrida que utiliza k-means como método de discretización y con una implementación discreta del algoritmo de búsqueda de armonía. Los resultados indican que el operador db-scan mejora significativamente la calidad de las soluciones y que la metaheurística propuesta muestra resultados competitivos con respecto al algoritmo de búsqueda de armonía.

Abstract:

The design of reinforced earth retaining walls is a combinatorial optimization problem of interest due to practical applications regarding the cost savings involved in the design and the optimization in the amount of CO $_{2}$ emissions generated in its construction. On the other hand, this problem presents important challenges in computational complexity since it involves 32 design variables; therefore we have in the order of $10^{20}$ possible combinations. In this article, we propose a hybrid algorithm in which the particle swarm optimization method is integrated that solves optimization problems in continuous spaces with the db-scan clustering technique, with the aim of addressing the combinatorial problem of the design of reinforced earth retaining walls. This algorithm optimizes two objective functions: the carbon emissions embedded and the economic cost of reinforced concrete walls. To assess the contribution of the db-scan operator in the optimization process, a random operator was designed. The best solutions, the averages, and the interquartile ranges of the obtained distributions are compared. The db-scan algorithm was then compared with a hybrid version that uses k-means as the discretization method and with a discrete implementation of the harmony search algorithm. The results indicate that the db-scan operator significantly improves the quality of the solutions and that the proposed metaheuristic shows competitive results with respect to the harmony search algorithm.

Keywords:

CO2 emission; earth-retaining walls; optimization; db-scan; particle swarm optimization

Reference:

GARCÍA, J.; MARTÍ, J.V.; YEPES, V. (2020). The buttressed walls problem: An application of a hybrid clustering particle swarm optimization algorithm. Mathematics, 8(6):862. https://doi.org/10.3390/math8060862

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Algoritmo híbrido de búsqueda del cuco para optimizar muros de contrafuertes

Acaban de publicarnos un artículo en la revista Mathematics, revista indexada en el primer cuartil del JCR. En este artículo se presenta un algoritmo híbrido de búsqueda del cuco y de clasificación no supervisada para optimizar el coste y las emisiones de CO2 de un muro de contrafuertes. El trabajo se enmarca dentro del proyecto de investigación DIMALIFE que dirijo como investigador principal en la Universitat Politècnica de València.

La Búsqueda Cuco se basa en la estrategia de reproducción de algunas especies de pájaros cucos. Éstos pájaros dejan sus huevos en los nidos de otros pájaros de otras especies para que éstas los críen, expulsando incluso los huevos del nido invadido. Si el pájaro anfitrión se percata que el huevo no es el propio, lo sacará del nido o directamente lo abandonará y construirá otro nido.

Por su parte, K-means es un algoritmo de clasificación no supervisada (clusterización) que agrupa objetos en k grupos basándose en sus características. El agrupamiento se realiza minimizando la suma de distancias entre cada objeto y el centroide de su grupo o cluster.

En este artículo se propone un algoritmo híbrido, en el que la metaheurística de búsqueda del cuco se utiliza como mecanismo de optimización en espacios continuos y la técnica de aprendizaje no supervisada k-means para discretizar las soluciones. Se diseña un operador aleatorio para determinar la contribución del operador k-means en el proceso de optimización. Se comparan los mejores valores, los promedios y los rangos intercuartiles de las distribuciones obtenidas. Los resultados muestran que el operador k-means contribuye significativamente a la calidad de las soluciones y que nuestro algoritmo es altamente competitivo.

Abstract

The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO₂ emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.

Keywords

CO2emission; earth-retaining walls; optimization; k-means; cuckoo search

Referencia:

GARCÍA, J.; YEPES, V.; MARTÍ, J.V. (2020). A hybrid k-means cuckoo search algorithm applied to the counterfort retaining walls problem. Mathematics, 8(4), 555. DOI:10.3390/math8040555

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Special Issue “Optimization for Decision Making II”

Mathematics (ISSN 2227-7390) is a peer-reviewed open access journal which provides an advanced forum for studies related to mathematics, and is published monthly online by MDPI.

Open Access – free for readers, with article processing charges (APC) paid by authors or their institutions.
High visibility: Indexed in the Science Citation Indexed Expanded – SCIE (Web of Science) from Vol. 4 (2016), Scopus, and Zentralblatt MATH from Vol. 3 (2015).
Rapid publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 21.7 days after submission; acceptance to publication is undertaken in 5.3 days (median values for papers published in this journal in the second half of 2018).
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Impact Factor: 1.105 (2018) (First quartile, JCR)

Special Issue “Optimization for Decision Making II”

Deadline for manuscript submissions: 29 February 2020.

Special Issue Editors

Guest Editor Prof. Víctor Yepes Universitat Politècnica de València, Spain Website \| E-Mail Interests: multiobjective optimization; structures optimization; lifecycle assessment; social sustainability of infrastructures; reliability-based maintenance optimization; optimization and decision-making under uncertainty
Guest Editor Prof. José M. Moreno-Jiménez Universidad de Zaragoza Website \| E-Mail Interests: multicriteria decision making; environmental selection; strategic planning; knowledge management; evaluation of systems; logistics and public decision making (e-government, e-participation, e-democracy and e-cognocracy)

Special Issue Information

Dear Colleagues,

Prof. Víctor Yepes
Prof. José Moreno-Jiménez
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 papers will be 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.

Keywords

Multicriteria decision making
Optimization techniques
Multiobjective optimization