Special Issue "Optimization Algorithms and Applications at OLA 2021"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 28 February 2022.

Special Issue Editors

Prof. Dr. El-ghazali Talbi
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Guest Editor
Computer Sciences, University of Lille 1, 59000 Lille, France
Interests: optimization; heuristics; combinatorial optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Bernabe Dorronsoro
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Guest Editor
Computer Science Engineering, Department Engineering School, University of Cadiz, 11003 Cádiz, Spain
Interests: metaheuristics; optimization; multi-objective optimization; mobile ad hoc networks; cloud computing
Special Issues, Collections and Topics in MDPI journals
Prof. Lionel Amodeo
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Guest Editor
Logistics and Industrial Systems Optimization Laboratory (LOSI), University of Technology of Troyes, Troyes, France
Interests: operational research; planning and scheduling; optimal design of production and assembly lines; layout; transport optimization; Heuristics and Meta-heuristics; Multi objective optimisation; supply chain
Special Issues, Collections and Topics in MDPI journals
Dr. Vincenzo Cutello
E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Catania, 2-95131 Catania, Italy
Interests: algorithms; combinatorial optimization; artificial intelligence
Dr. Mario Pavone
E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Catania, 2-95131 Catania, Italy
Interests: optimization; complex networks; metaheuristics; operations research

Special Issue Information

Dear Colleagues,

Every day, each of us continually makes decisions during our own daily activities, which means simply selecting from among all available options the one that optimizes a given goal (e.g., the classical maximizing profits and minimizing costs). Therefore, optimization problems are always around us. Consequently, efficiently solving such problems becomes crucial and important, since it may lead to fruitful and faster production, the development of cheaper industrial products, and more sustainable solutions.

However, solving optimization problems is often quite difficult. In many cases, current optimization methods may be impractical to apply due to the large size of the search space and time constraints. In light of this, approximate algorithms, and among these the metaheuristics, are reliable tools able to offer robust solutions in reasonable times, and often they represent the unique alternative in solving complex optimization problems thanks to their ability to explore large search spaces efficiently by reducing their effective sizes.

This Special Issue aims to present a collection of recent high-quality papers on optimization algorithms and their applications to problems in the real world, particularly on topics covered at The International Conference in Optimization and Learning (OLA 2021, Catania, Italy, 21–23 June 2021). Extended versions of the best papers presented at the conference will be invited for submission to this issue and will go through a rigorous review process. Submissions of contributions not presented at the conference are also welcome if they are related to the themes of the Special Issue.

Prof. El-ghazali Talbi
Dr. Bernabe Dorronsoro
Prof. Dr. Lionel Amodeo
Dr. Vincenzo Cutello
Dr. Mario Pavone
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. Algorithms 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 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

  • Exact optimization algorithms
  • New high-impact applications
  • Parameter tuning
  • Fourth industrial revolution
  • Bioinformatics
  • Smart cities
  • Intelligent transportation systems
  • Optimization for sustainability
  • New research challenges
  • Hybridization issues
  • Simulation-based optimization
  • Metamodeling
  • Surrogate modeling
  • Multiobjective optimization
  • Parallel optimization algorithms
  • Optimization for machine learning
  • Machine learning for optimization
  • Optimization and learning under uncertainty

Published Papers (2 papers)

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Research

Article
Optimal CNN–Hopfield Network for Pattern Recognition Based on a Genetic Algorithm
Algorithms 2022, 15(1), 11; https://doi.org/10.3390/a15010011 - 27 Dec 2021
Viewed by 301
Abstract
Convolutional neural networks (CNNs) have powerful representation learning capabilities by automatically learning and extracting features directly from inputs. In classification applications, CNN models are typically composed of: convolutional layers, pooling layers, and fully connected (FC) layer(s). In a chain-based deep neural network, the [...] Read more.
Convolutional neural networks (CNNs) have powerful representation learning capabilities by automatically learning and extracting features directly from inputs. In classification applications, CNN models are typically composed of: convolutional layers, pooling layers, and fully connected (FC) layer(s). In a chain-based deep neural network, the FC layers contain most of the parameters of the network, which affects memory occupancy and computational complexity. For many real-world problems, speeding up inference time is an important matter because of the hardware design implications. To deal with this problem, we propose the replacement of the FC layers with a Hopfield neural network (HNN). The proposed architecture combines both a CNN and an HNN: A pretrained CNN model is used for feature extraction, followed by an HNN, which is considered as an associative memory that saves all features created by the CNN. Then, to deal with the limitation of the storage capacity of the HNN, the proposed work uses multiple HNNs. To optimize this step, the knapsack problem formulation is proposed, and a genetic algorithm (GA) is used solve it. According to the results obtained on the Noisy MNIST Dataset, our work outperformed the state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Optimization Algorithms and Applications at OLA 2021)
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Article
Compensating Data Shortages in Manufacturing with Monotonicity Knowledge
Algorithms 2021, 14(12), 345; https://doi.org/10.3390/a14120345 - 27 Nov 2021
Cited by 1 | Viewed by 468
Abstract
Systematic decision making in engineering requires appropriate models. In this article, we introduce a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the form of shape constraints, or more specifically, monotonicity constraints. Incorporating such information is [...] Read more.
Systematic decision making in engineering requires appropriate models. In this article, we introduce a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the form of shape constraints, or more specifically, monotonicity constraints. Incorporating such information is particularly useful when the available datasets are small or do not cover the entire input space, as is often the case in manufacturing applications. We set up the regression subject to the considered monotonicity constraints as a semi-infinite optimization problem, and propose an adaptive solution algorithm. The method is applicable in multiple dimensions and can be extended to more general shape constraints. It was tested and validated on two real-world manufacturing processes, namely, laser glass bending and press hardening of sheet metal. It was found that the resulting models both complied well with the expert’s monotonicity knowledge and predicted the training data accurately. The suggested approach led to lower root-mean-squared errors than comparative methods from the literature for the sparse datasets considered in this work. Full article
(This article belongs to the Special Issue Optimization Algorithms and Applications at OLA 2021)
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