Stochastic Optimization and Metaheuristic Optimization: Theory and Applications

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Logic".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 1264

Special Issue Editors

College of ICT, School of Engineering and Technology, CQUniversity, Brisbane, Australia
Interests: evolutionary optimisation; machine learning; STEM education

E-Mail Website
Guest Editor
School of IT, Deakin University, Waurn Ponds 3216, Australia
Interests: distributed system; networking; cyber security

Special Issue Information

Dear Colleagues,

Stochastic and metaheuristic optimization methods are optimization algorithms incorporating probabilistic elements, either in problem data or in algorithms themselves. Due to their flexible representations and higher performances, stochastic and metaheuristic algorithms have been widely applied in solving complicated optimization problems. In machine learning, these methods can be applied in multi-layer neural network optimization, wireless sensor network optimization, data clustering, and image processing, etc. 

The aim of this Special Issue is to invite researchers to report their latest and most innovative research on the development of stochastic and metaheuristic methods in machine learning applications. Contributions to this Special Issue should fall within the scope of Axioms, which comprises the following topics:

  • Mathematical logic;
  • Mathematical problems of artificial intelligence;
  • Complex networks from mathematical viewpoints;
  • Reasoning under uncertainty;
  • Interdisciplinary applications of mathematical theory.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Stochastic and metaheuristic modelling;
  • Mathematical modelling;
  • Statistical analysis on stochastic methods;
  • Stochastic methods in artificial neural network optimization;
  • Stochastic and metaheuristic methods in image processing;
  • Resource scheduling applications;
  • Location science applications;
  • Distributed computing applications;
  • Stochastic and metaheuristic methods in cyber security. 

We look forward to receiving your contributions. 

Dr. Lily D. Li
Dr. Shang Gao
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 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. Axioms 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 2400 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 optimization
  • metaheuristic
  • machine learning
  • stochastic and metaheuristic modelling
  • mathematical modelling
  • statistical analysis on stochastic methods
  • stochastic methods in artificial neural network optimization
  • stochastic and metaheuristic methods in image processing
  • resource scheduling applications
  • location science applications
  • distributed computing applications
  • stochastic and metaheuristic methods in cyber security

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

14 pages, 1371 KiB  
Article
Incorporating Socio-Economic Factors in Maximizing Two-Dimensional Demand Coverage and Minimizing Distance to Uncovered Demand: A Dual-Objective MCLP Approach for Fire Station Location Selection
by Albertus Untadi, Lily D. Li, Michael Li and Roland Dodd
Axioms 2024, 13(1), 13; https://doi.org/10.3390/axioms13010013 - 25 Dec 2023
Viewed by 948
Abstract
In this study, we employ a dual-objective optimization model, utilizing the Implicit Modified Coverage Location Problem (MCLP-Implicit) approach, to determine an appropriate fire station allocation. Our objectives encompass maximizing coverage in areas with a heightened projected demand, based on socioeconomic predictors of building [...] Read more.
In this study, we employ a dual-objective optimization model, utilizing the Implicit Modified Coverage Location Problem (MCLP-Implicit) approach, to determine an appropriate fire station allocation. Our objectives encompass maximizing coverage in areas with a heightened projected demand, based on socioeconomic predictors of building fires, and concurrently minimizing the distance of uncovered demand zones to the closest fire station. The challenges of this model reflect the criticality of strategic placement, aiming for not just swift response times but also the complete coverage of a region with priority given to subregions with a pronounced potential for incidents. The applicability of the proposed approach is demonstrated through a case study in south-east Queensland. Our findings indicate that there is a tangible justification for adopting this model. The broader coverage and wider spread of locations it produces can greatly aid in the dynamic deployment of personnel during surges caused by seasonal fluctuations or unforeseen calamities. By weaving in socioeconomic aspects into demand predictions, our model has also maintained appropriate coverage to socioeconomically disadvantaged communities in the region. Nevertheless, it is paramount to underline the recommendation that further research should account for road connectivity—a pivotal factor when pinpointing these locations in the real world. This research paves the way for enriched insights in long-term urban planning and fire response protocol crafting, especially in regions mirroring similar socioeconomic profiles or risks of natural disasters, not to mention its commercial implications to the relevant industry servicing the fire and rescue authorities. Full article
Show Figures

Figure 1

Back to TopTop