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Special Issue "Machine Learning, Metaheuristics and Combinatorial Optimization Problems"

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

Deadline for manuscript submissions: closed (10 February 2023) | Viewed by 6284

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Prof. Dr. Víctor Yepes

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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
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Dr. José Antonio García

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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
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Dr. Broderick Crawford

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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 hybrid techniques, specifically hybridization of machine learning and metaheuristics. For this Special Issue, we invite researchers to submit articles on combined optimization and hybrid techniques. 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 industry.

Prof. Dr. Víctor Yepes
Dr. José Antonio García
Dr. Broderick Crawford
Guest Editors

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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.

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Published Papers (7 papers)

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Open AccessArticle

Hybrid Swarm Intelligence Optimization Methods for Low-Embodied Energy Steel-Concrete Composite Bridges

and

Mathematics 2023, 11(1), 140; https://doi.org/10.3390/math11010140 - 27 Dec 2022

Cited by 1 | Viewed by 974

Abstract

Bridge optimization is a significant challenge, given the huge number of possible configurations of the problem. Embodied energy and cost were taken as objective functions for a box-girder steel–concrete optimization problem considering both as single-objective. Embodied energy was chosen as a sustainable criterion to compare the results with cost. The stochastic global search TAMO algorithm, the swarm intelligence cuckoo search (CS), and sine cosine algorithms (SCA) were used to achieve this goal. To allow the SCA and SC techniques to solve the discrete bridge optimization problem, the discretization technique applying the k-means clustering technique was used. As a result, SC was found to produce objective energy function values comparable to TAMO while reducing the computation time by 25.79%. In addition, the cost optimization and embodied energy analysis revealed that each euro saved using metaheuristic methodologies decreased the energy consumption for this optimization problem by 0.584 kW·h. Additionally, by including cells in the upper and lower parts of the webs, the behavior of the section was improved, as were the optimization outcomes for the two optimization objectives. This study concludes that double composite action design on supports makes the continuous longitudinal stiffeners in the bottom flange unnecessary. Full article

(This article belongs to the Special Issue Machine Learning, Metaheuristics and Combinatorial Optimization Problems)

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Open AccessArticle

Swarm-Inspired Computing to Solve Binary Optimization Problems: A Backward Q-Learning Binarization Scheme Selector

Marcelo Becerra-Rozas

José Lemus-Romani

Felipe Cisternas-Caneo

Broderick Crawford

Ricardo Soto

and

José García

Mathematics 2022, 10(24), 4776; https://doi.org/10.3390/math10244776 - 15 Dec 2022

Cited by 1 | Viewed by 716

Abstract

In recent years, continuous metaheuristics have been a trend in solving binary-based combinatorial problems due to their good results. However, to use this type of metaheuristics, it is necessary to adapt them to work in binary environments, and in general, this adaptation is not trivial. The method proposed in this work evaluates the use of reinforcement learning techniques in the binarization process. Specifically, the backward Q-learning technique is explored to choose binarization schemes intelligently. This allows any continuous metaheuristic to be adapted to binary environments. The illustrated results are competitive, thus providing a novel option to address different complex problems in the industry. Full article

(This article belongs to the Special Issue Machine Learning, Metaheuristics and Combinatorial Optimization Problems)

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Open AccessArticle

Embedded Learning Approaches in the Whale Optimizer to Solve Coverage Combinatorial Problems

Marcelo Becerra-Rozas

Felipe Cisternas-Caneo

and

Mathematics 2022, 10(23), 4529; https://doi.org/10.3390/math10234529 - 30 Nov 2022

Cited by 2 | Viewed by 680

Abstract

When we face real problems using computational resources, we understand that it is common to find combinatorial problems in binary domains. Moreover, we have to take into account a large number of possible candidate solutions, since these can be numerous and make it complicated for classical algorithmic techniques to address them. When this happens, in most cases, it becomes a problem due to the high resource cost they generate, so it is of utmost importance to solve these problems efficiently. To cope with this problem, we can apply other methods, such as metaheuristics. There are some metaheuristics that allow operation in discrete search spaces; however, in the case of continuous swarm intelligence metaheuristics, it is necessary to adapt them to operate in discrete domains. To perform this adaptation, it is necessary to use a binary scheme to take advantage of the original moves of the metaheuristics designed for continuous problems. In this work, we propose to hybridize the whale optimization algorithm metaheuristic with the Q-learning reinforcement learning technique, which we call (the QBWOA). By using this technique, we are able to realize an smart and fully online binarization scheme selector, the results have been statistically promising thanks to the respective tables and graphs. Full article

(This article belongs to the Special Issue Machine Learning, Metaheuristics and Combinatorial Optimization Problems)

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Open AccessArticle

Generation of Synthetic Data for the Analysis of the Physical Stability of Tailing Dams through Artificial Intelligence

Fernando Pacheco

Gabriel Hermosilla

Osvaldo Piña

Gabriel Villavicencio

and

Mathematics 2022, 10(23), 4396; https://doi.org/10.3390/math10234396 - 22 Nov 2022

Viewed by 831

Abstract

In this research, we address the problem of evaluating physical stability (PS) to close tailings dams (TD) from medium-sized Chilean mining using artificial intelligence (AI) algorithms. The PS can be analyzed through the study of critical variables of the TD that allow estimating different potential failure mechanisms (PFM): seismic liquefaction, slope instability, static liquefaction, overtopping, and piping, which may occur in this type of tailings storage facilities in a seismically active country such as Chile. Thus, this article proposes the use of four machine learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural networks (ANN), and extreme gradient boosting (XGBoost), to estimate five possible PFM. In addition, due to the scarcity of data to train the algorithms, the use of generative adversarial networks (GAN) is proposed to create synthetic data and increase the database used. Therefore, the novelty of this article consists in estimating the PFM for TD and generating synthetic data through the GAN. The results show that, when using the GAN, the result obtained by the ML models increases the F1-score metric by 30 percentage points, obtaining results of 97.4%, 96.3%, 96.7%, and 97.3% for RF, SVM, ANN, and XGBoost, respectively. Full article

(This article belongs to the Special Issue Machine Learning, Metaheuristics and Combinatorial Optimization Problems)

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Open AccessArticle

Binarization Technique Comparisons of Swarm Intelligence Algorithm: An Application to the Multi-Demand Multidimensional Knapsack Problem

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Mathematics 2022, 10(17), 3183; https://doi.org/10.3390/math10173183 - 03 Sep 2022

Viewed by 640

Abstract

In order to minimize execution times, improve the quality of solutions, and address more extensive target situations, optimization techniques, particularly metaheuristics, are continually improved. Hybridizing procedures are one of these noteworthy strategies due to their wide range of applications. This article describes a hybrid algorithm that combines the k-means method to produce a binary version of the cuckoo search and sine cosine algorithms. The binary algorithms are applied on the

NP

-hard multi-demand multidimensional knapsack problem. This problem is of particular interest because it has two types of constraints. The first group of constraints is related to the capacity of the knapsacks, and a second type is associated with the demand that must be met. Experiments were undertaken to acquire insight into the contribution of the k-means technique and the local search operator to the final results. Additionally, a comparison is made with two other types of binarization, the first based on a random method and the second based on the percentile concept. The results reveal that the k-means hybrid algorithm consistently provides superior results in most cases studied. In particular, incorporating the local search operator improved the results by an average of 0.23%. On the other hand, when comparing the results with 100 items and 30-30 restrictions, k-means was 1.06% better on average than the random operator. Full article

(This article belongs to the Special Issue Machine Learning, Metaheuristics and Combinatorial Optimization Problems)

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Open AccessArticle

Combining a Population-Based Approach with Multiple Linear Models for Continuous and Discrete Optimization Problems

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Mathematics 2022, 10(16), 2920; https://doi.org/10.3390/math10162920 - 13 Aug 2022

Viewed by 637

Abstract

Population-based approaches have given us new search strategies and ideas in order to solve optimization problems. Usually, these methods are based on the performance carried out by a finite number of agents, which by the interaction between them they evolve and work all over the search space. Also, it is well-known that the correct employment of parameter values in this kind of method can positively impact their performance and behavior. In this context, the present work focuses on the design of a hybrid architecture which smartly balances the population size on run-time. In order to smartly balance and control the population size, a modular approach, named Linear Modular Population Balancer (LMPB), is proposed. The main ideas behind the designed architecture include the solving strategy behind a population-based metaheuristic, the influence of learning components based on multiple statistical modeling methods which transform the dynamic data generated into knowledge, and the possibilities to tackle both discrete and continuous optimization problems. In this regard, three modules are proposed for LMPB, which concern tasks such as the management of the population-based algorithm, parameter setting, probabilities, learning methods, and selection mechanism for the population size to employ. In order to test the viability and effectiveness of our proposed approach, we solve a set of well-known benchmark functions and the multidimensional knapsack problem (MKP). Additionally, we illustrate promising solving results, compare them against state-of-the-art methods which have proved to be good options for solving optimization problems, and give solid arguments for future work in the necessity to keep evolving this type of proposed architecture. Full article

(This article belongs to the Special Issue Machine Learning, Metaheuristics and Combinatorial Optimization Problems)

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Review

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Open AccessReview

Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review

Marcelo Becerra-Rozas

José Lemus-Romani

Felipe Cisternas-Caneo

and

Mathematics 2023, 11(1), 129; https://doi.org/10.3390/math11010129 - 27 Dec 2022

Cited by 1 | Viewed by 859

Abstract

For years, extensive research has been in the binarization of continuous metaheuristics for solving binary-domain combinatorial problems. This paper is a continuation of a previous review and seeks to draw a comprehensive picture of the various ways to binarize this type of metaheuristics; the study uses a standard systematic review consisting of the analysis of 512 publications from 2017 to January 2022 (5 years). The work will provide a theoretical foundation for novice researchers tackling combinatorial optimization using metaheuristic algorithms and for expert researchers analyzing the binarization mechanism’s impact on the metaheuristic algorithms’ performance. Structuring this information allows for improving the results of metaheuristics and broadening the spectrum of binary problems to be solved. We can conclude from this study that there is no single general technique capable of efficient binarization; instead, there are multiple forms with different performances. Full article

(This article belongs to the Special Issue Machine Learning, Metaheuristics and Combinatorial Optimization Problems)

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