Special Issue "Optimization Algorithms and Applications"

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

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Dr. Bernabe Dorronsoro
E-Mail Website
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 and Collections in MDPI journals
Dr. Juan Carlos de la Torre
E-Mail Website
Guest Editor
University of Cadiz, Spain
Interests: optimization; machine learning; software sustainability
Special Issues and Collections in MDPI journals
Prof. Pascal Bouvry
E-Mail Website
Guest Editor
Faculty of Science, Technology and Communication, University of Luxembourg, Esch-sur-Alzette , Luxembourg
Interests: optimization; distributed systems; bioinformatics; parallel and cloud computing
Dr. Patricia Ruiz
E-Mail Website
Guest Editor
University of Cadiz, Spain
Interests: intelligent transportation systems; optimization; wireless networks
Special Issues and Collections in MDPI journals
Prof. Dr. El-ghazali Talbi
E-Mail Website
Guest Editor
Computer Sciences, University of Lille 1, 59000 Lille, France
Interests: optimization; heuristics; combinatorial optimization
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Optimization problems are all around us. Finding the optimal solutions to such problems may lead to the development of cheaper products, faster and more efficient production, and more sustainable solutions. However, solving a problem to optimality is generally difficult as well as time and resource consuming, and, in many cases, may be impractical with current software and hardware technologies. For such cases, approximation optimization algorithms are interesting tools that offer good solutions in a reasonable time frame.

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 in the OLA2020 International Conference on Optimization and Learning. 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. However, the Special Issue is not restricted to those papers and is open to any research work matching the considered topics.

The best received works will be published with fully waived article processing charges.

Dr. Bernabe Dorronsoro
Dr. Juan Carlos de la Torre
Dr. Patricia Ruiz
Prof. Pascal Bouvry
Prof. El-ghazali Talbi
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

  • heuristics and metaheuristics
  • exact optimization algorithms
  • new high-impact applications
  • parameter tuning
  • 4th industrial revolution
  • bioinformatics
  • smart cities
  • intelligent transportation systems
  • optimization for sustainability
  • new research challenges
  • hybridization issues
  • simulation-based optimization
  • meta-modeling
  • high-performance and exascale computing
  • surrogate modelling
  • multi-objective optimization
  • parallel optimization algorithms
  • optimization for machine learning
  • machine learning for optimization
  • optimization and learning under uncertainty

Published Papers (3 papers)

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Research

Open AccessArticle
A New Chaotic-Based Approach for Multi-Objective Optimization
Algorithms 2020, 13(9), 204; https://doi.org/10.3390/a13090204 - 20 Aug 2020
Viewed by 937
Abstract
Multi-objective optimization problems (MOPs) have been widely studied during the last decades. In this paper, we present a new approach based on Chaotic search to solve MOPs. Various Tchebychev scalarization strategies have been investigated. Moreover, a comparison with state of the art algorithms [...] Read more.
Multi-objective optimization problems (MOPs) have been widely studied during the last decades. In this paper, we present a new approach based on Chaotic search to solve MOPs. Various Tchebychev scalarization strategies have been investigated. Moreover, a comparison with state of the art algorithms on different well known bound constrained benchmarks shows the efficiency and the effectiveness of the proposed Chaotic search approach. Full article
(This article belongs to the Special Issue Optimization Algorithms and Applications)
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Open AccessArticle
Machine Learning-Guided Dual Heuristics and New Lower Bounds for the Refueling and Maintenance Planning Problem of Nuclear Power Plants
Algorithms 2020, 13(8), 185; https://doi.org/10.3390/a13080185 - 30 Jul 2020
Cited by 1 | Viewed by 1637
Abstract
This paper studies the hybridization of Mixed Integer Programming (MIP) with dual heuristics and machine learning techniques, to provide dual bounds for a large scale optimization problem from an industrial application. The case study is the EURO/ROADEF Challenge 2010, to optimize the refueling [...] Read more.
This paper studies the hybridization of Mixed Integer Programming (MIP) with dual heuristics and machine learning techniques, to provide dual bounds for a large scale optimization problem from an industrial application. The case study is the EURO/ROADEF Challenge 2010, to optimize the refueling and maintenance planning of nuclear power plants. Several MIP relaxations are presented to provide dual bounds computing smaller MIPs than the original problem. It is proven how to get dual bounds with scenario decomposition in the different 2-stage programming MILP formulations, with a selection of scenario guided by machine learning techniques. Several sets of dual bounds are computable, improving significantly the former best dual bounds of the literature and justifying the quality of the best primal solution known. Full article
(This article belongs to the Special Issue Optimization Algorithms and Applications)
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Open AccessArticle
A Generalized Alternating Linearization Bundle Method for Structured Convex Optimization with Inexact First-Order Oracles
Algorithms 2020, 13(4), 91; https://doi.org/10.3390/a13040091 - 14 Apr 2020
Cited by 1 | Viewed by 1366
Abstract
In this paper, we consider a class of structured optimization problems whose objective function is the summation of two convex functions: f and h, which are not necessarily differentiable. We focus particularly on the case where the function f is general and [...] Read more.
In this paper, we consider a class of structured optimization problems whose objective function is the summation of two convex functions: f and h, which are not necessarily differentiable. We focus particularly on the case where the function f is general and its exact first-order information (function value and subgradient) may be difficult to obtain, while the function h is relatively simple. We propose a generalized alternating linearization bundle method for solving this class of problems, which can handle inexact first-order information of on-demand accuracy. The inexact information can be very general, which covers various oracles, such as inexact, partially inexact and asymptotically exact oracles, and so forth. At each iteration, the algorithm solves two interrelated subproblems: one aims to find the proximal point of the polyhedron model of f plus the linearization of h; the other aims to find the proximal point of the linearization of f plus h. We establish global convergence of the algorithm under different types of inexactness. Finally, some preliminary numerical results on a set of two-stage stochastic linear programming problems show that our method is very encouraging. Full article
(This article belongs to the Special Issue Optimization Algorithms and Applications)
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