Special Issue "Stochastic Optimization: Algorithms and Applications"

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

Deadline for manuscript submissions: closed (30 April 2019).

Special Issue Editor

Guest Editor
Dr. Patrizia Beraldi Website E-Mail
Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria, Via P. Bucci, Rende (CS), Italy
Interests: stochastic programming

Special Issue Information

Dear Colleagues,

Stochastic Optimization (SO) represents a mathematical framework to deal with decision-making problems involving uncertain parameters. In the last few decades, SO has been receiving increasing attention from scholars and practitioners, and has been notably spreading its application domain.

The open access journal Algorithms will host a Special Issue on “Stochastic Optimization: 
Algorithms and Applications”.

Submissions of original research papers, surveys, or case studies, in all areas of stochastic optimization, with a focus on both algorithms and applications, are welcome.

The following is a (non-exhaustive) list of topics of interest:    

  • Stochastic programming
  • Robust optimization
  • Risk management
  • Stochastic optimization algorithms
  • Innovative applications
Dr. Patrizia Beraldi
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 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 1000 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

  • Stochastic programming
  • Robust optimization
  • Risk management
  • Stochastic optimization algorithms
  • Innovative applications 

Published Papers (3 papers)

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Research

Open AccessArticle
Poisson Twister Generator by Cumulative Frequency Technology
Algorithms 2019, 12(6), 114; https://doi.org/10.3390/a12060114 - 28 May 2019
Abstract
The widely known generators of Poisson random variables are associated with different modifications of the algorithm based on the convergence in probability of a sequence of uniform random variables to the created stochastic number. However, in some situations, this approach yields different discrete [...] Read more.
The widely known generators of Poisson random variables are associated with different modifications of the algorithm based on the convergence in probability of a sequence of uniform random variables to the created stochastic number. However, in some situations, this approach yields different discrete Poisson probability distributions and skipping in the generated numbers. This article offers a new approach for creating Poisson random variables based on the complete twister generator of uniform random variables, using cumulative frequency technology. The simulation results confirm that probabilistic and frequency distributions of the obtained stochastic numbers completely coincide with the theoretical Poisson distribution. Moreover, combining this new approach with the tuning algorithm of basic twister generation allows for a significant increase in length of the created sequences without using additional RAM of the computer. Full article
(This article belongs to the Special Issue Stochastic Optimization: Algorithms and Applications)
Open AccessArticle
Degradation Trend Prediction for Rotating Machinery Using Long-Range Dependence and Particle Filter Approach
Algorithms 2018, 11(7), 89; https://doi.org/10.3390/a11070089 - 26 Jun 2018
Abstract
Timely maintenance and accurate fault prediction of rotating machinery are essential for ensuring system availability, minimizing downtime, and contributing to sustainable production. This paper proposes a novel approach based on long-range dependence (LRD) and particle filter (PF) for degradation trend prediction of rotating [...] Read more.
Timely maintenance and accurate fault prediction of rotating machinery are essential for ensuring system availability, minimizing downtime, and contributing to sustainable production. This paper proposes a novel approach based on long-range dependence (LRD) and particle filter (PF) for degradation trend prediction of rotating machinery, taking the rolling bearing as an example. In this work, the degradation prediction is evaluated based on two health indicators time series; i.e., equivalent vibration severity (EVI) time series and kurtosis time series. Specifically, the degradation trend prediction issues here addressed have the following two distinctive features: (i) EVI time series with weak LRD property and (ii) kurtosis time series with sharp transition points (STPs) in the forecasted region. The core idea is that the parameters distribution of the LRD model can be updated recursively by the particle filter algorithm; i.e., the parameters degradation of the LRD model are restrained, and thus the prognostic results could be generated real-time, wherein the initial LRD model is designed randomly. The prediction results demonstrate that the significant improvements in prediction accuracy are obtained with the proposed method compared to some state-of-the-art approaches such as the autoregressive–moving-average (ARMA) model and the fractional order characteristic (FOC) model, etc. Full article
(This article belongs to the Special Issue Stochastic Optimization: Algorithms and Applications)
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Open AccessArticle
An Improved Bacterial-Foraging Optimization-Based Machine Learning Framework for Predicting the Severity of Somatization Disorder
Algorithms 2018, 11(2), 17; https://doi.org/10.3390/a11020017 - 06 Feb 2018
Cited by 4
Abstract
It is of great clinical significance to establish an accurate intelligent model to diagnose the somatization disorder of community correctional personnel. In this study, a novel machine learning framework is proposed to predict the severity of somatization disorder in community correction personnel. The [...] Read more.
It is of great clinical significance to establish an accurate intelligent model to diagnose the somatization disorder of community correctional personnel. In this study, a novel machine learning framework is proposed to predict the severity of somatization disorder in community correction personnel. The core of this framework is to adopt the improved bacterial foraging optimization (IBFO) to optimize two key parameters (penalty coefficient and the kernel width) of a kernel extreme learning machine (KELM) and build an IBFO-based KELM (IBFO-KELM) for the diagnosis of somatization disorder patients. The main innovation point of the IBFO-KELM model is the introduction of opposition-based learning strategies in traditional bacteria foraging optimization, which increases the diversity of bacterial species, keeps a uniform distribution of individuals of initial population, and improves the convergence rate of the BFO optimization process as well as the probability of escaping from the local optimal solution. In order to verify the effectiveness of the method proposed in this study, a 10-fold cross-validation method based on data from a symptom self-assessment scale (SCL-90) is used to make comparison among IBFO-KELM, BFO-KELM (model based on the original bacterial foraging optimization model), GA-KELM (model based on genetic algorithm), PSO-KELM (model based on particle swarm optimization algorithm) and Grid-KELM (model based on grid search method). The experimental results show that the proposed IBFO-KELM prediction model has better performance than other methods in terms of classification accuracy, Matthews correlation coefficient (MCC), sensitivity and specificity. It can distinguish very well between severe somatization disorder and mild somatization and assist the psychological doctor with clinical diagnosis. Full article
(This article belongs to the Special Issue Stochastic Optimization: Algorithms and Applications)
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