Forthcoming and Online First Articles

International Journal of Computational Intelligence Studies

International Journal of Computational Intelligence Studies (IJCIStudies)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Computational Intelligence Studies (1 paper in press)

Regular Issues

  • Solving differential equations with global optimization techniques   Order a copy of this article
    by Ioannis Tsoulos, Alexandros Tzallas, Dimitrios Tsalikakis 
    Abstract: The solution of differential equations finds many applications in a huge range of problems, and many techniques have been developed to approximate their solutions. For example, differential equations can be applied to physics problems, chemistry problems, economics, modelling, etc. This manuscript presents a number of global optimisation techniques that have been successfully applied to train machine learning models to approximate differential equation solutions. More specifically, two modified versions of genetic algorithms and particle swarm optimisation methods are proposed here. These methods have been successfully applied to solving ordinary differential equations and systems of differential equations as well as partial differential equations with Dirichlet boundary conditions.
    Keywords: differential equations; global optimisation; stochastic methods; machine learning.
    DOI: 10.1504/IJCISTUDIES.2024.10062128