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Algorithms
Special Issues
Optimization under Uncertainty 2022
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Optimization under Uncertainty 2022

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 2314

Special Issue Editor

Prof. Dr. Jairo Montoya-Torres

E-Mail Website
Guest Editor
School of Engineering, Universidad de La Sabana, Chia 250001, Colombia
Interests: applied mathematics; manufacturing engineering; industrial engineering; supply chain management; sustainable logistics

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research in the area of optimization algorithms under uncertainty to this Special Issue, “Optimisation under Uncertainty”. We are looking for new and innovative approaches for solving complex combinatorial problems where there are uncertainties involved in the data or in the model, arising in several research fields.

High-quality papers are solicited to address both theoretical and practical issues. Submissions are welcome both for traditional combinatorial optimzation problems or stochastic optimization problems, as well as emerging applications. Works studying mono-objective and multi-objective problems are welcome, in a broad spectrum of traditional and emerging applications. The link with data analytics techniques is especially encouraged.

Prof. Dr. Jairo Montoya-Torres
Guest Editor

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. 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 1600 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

  • Mathematical programming
  • Stochastic optimization
  • Robust optimization
  • Fuzzy programming and optimization
  • Hybrid simlation – optimization algorithms
  • Simheuristics
  • Heuristics and metaheuristics
  • Applications in: scheduling, logistics and transportation, supply chain, finance, informatics, telecomunications, energy systems, healthcare, etc.

Published Papers (1 paper)

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Research

23 pages, 391 KiB  
Article
Non-Stationary Stochastic Global Optimization Algorithms
by Jonatan Gomez and Andres Rivera
Algorithms 2022, 15(10), 362; https://doi.org/10.3390/a15100362 - 29 Sep 2022
Cited by 1 | Viewed by 1470
Abstract
Studying the theoretical properties of optimization algorithms such as genetic algorithms and evolutionary strategies allows us to determine when they are suitable for solving a particular type of optimization problem. Such a study consists of three main steps. The first step is considering [...] Read more.
Studying the theoretical properties of optimization algorithms such as genetic algorithms and evolutionary strategies allows us to determine when they are suitable for solving a particular type of optimization problem. Such a study consists of three main steps. The first step is considering such algorithms as Stochastic Global Optimization Algorithms (SGoals ), i.e., iterative algorithm that applies stochastic operations to a set of candidate solutions. The second step is to define a formal characterization of the iterative process in terms of measure theory and define some of such stochastic operations as stationary Markov kernels (defined in terms of transition probabilities that do not change over time). The third step is to characterize non-stationary SGoals, i.e., SGoals having stochastic operations with transition probabilities that may change over time. In this paper, we develop the third step of this study. First, we generalize the sufficient conditions convergence from stationary to non-stationary Markov processes. Second, we introduce the necessary theory to define kernels for arithmetic operations between measurable functions. Third, we develop Markov kernels for some selection and recombination schemes. Finally, we formalize the simulated annealing algorithm and evolutionary strategies using the systematic formal approach. Full article
(This article belongs to the Special Issue Optimization under Uncertainty 2022)
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