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Mathematics
Special Issues
Computational Intelligence in Real-World Optimization Problems: From Theory to Practice
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Computational Intelligence in Real-World Optimization Problems: From Theory to Practice

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 400

Special Issue Editor

Dr. Yoel Tenne

E-Mail Website
Guest Editor
Mechanical Engineering Department, Ariel University, Ariel, Israel
Interests: computational intelligence; applied optimization; informatics; simulation-driven design

Special Issue Information

Researchers in engineering and science are facing optimization problems of ever-increasing complexity. These challenges encompass non-analytic ‘black-box’ functions, noisy landscapes, dynamic and uncertain settings, and big data, to name a few.

In these settings, classical mathematical optimization and analysis methods may perform poorly. This has motivated the development and application of computational intelligence (CI) methods for handling such problems.

Given the tremendous attention that CI methods have gained, both in academy and in the industry, the goal of this Special Issue is to present a curated collection of papers that describe the current state of the art in the theory and application of CI-based methods to real-world optimization problems. Papers must be original and previously unpublished. Papers discussing practical considerations and implementations are encouraged.

As such, relevant topics for this Special Issue include (but are not limited to) the following:

  • CI-based optimization search methods;
  • Applied optimization;
  • Fuzzy logic and neural networks applications in optimization;
  • CI in informatics, data mining and big data problems;
  • Pertinent mathematical methods for enhancing the performance of CI-based methods;
  • Real-world applications of CI in science and engineering.

Dr. Yoel Tenne
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • applied optimization
  • big data
  • computational intelligence
  • data mining
  • fuzzy logic
  • informatics
  • modelling and simulations
  • neural networks
  • real-world applications

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

21 pages, 354 KiB  
Article
Adaptive Broadcast Scheme with Fuzzy Logic and Reinforcement Learning Dynamic Membership Functions in Mobile Ad Hoc Networks
by Akobir Ismatov, Beom-Kyu Suh, Jian Kim, Yong-Beom Park and Ki-Il Kim
Mathematics 2025, 13(15), 2367; https://doi.org/10.3390/math13152367 - 23 Jul 2025
Viewed by 39
Abstract
Broadcasting in Mobile Ad Hoc Networks (MANETs) is significantly challenged by dynamic network topologies. Traditional fuzzy logic-based schemes that often rely on static fuzzy tables and fixed membership functions are limiting their ability to adapt to evolving network conditions. To address these limitations, [...] Read more.
Broadcasting in Mobile Ad Hoc Networks (MANETs) is significantly challenged by dynamic network topologies. Traditional fuzzy logic-based schemes that often rely on static fuzzy tables and fixed membership functions are limiting their ability to adapt to evolving network conditions. To address these limitations, in this paper, we conduct a comparative study of two innovative broadcasting schemes that enhance adaptability through dynamic fuzzy logic membership functions for the broadcasting problem. The first approach (Model A) dynamically adjusts membership functions based on changing network parameters and fine-tunes the broadcast (BC) versus do-not-broadcast (DNB) ratio. Model B, on the other hand, introduces a multi-profile switching mechanism that selects among distinct fuzzy parameter sets optimized for various macro-level scenarios, such as energy constraints or node density, without altering the broadcasting ratio. Reinforcement learning (RL) is employed in both models: in Model A for BC/DNB ratio optimization, and in Model B for action decisions within selected profiles. Unlike prior fuzzy logic or reinforcement learning approaches that rely on fixed profiles or static parameter sets, our work introduces adaptability at both the membership function and profile selection levels, significantly improving broadcasting efficiency and flexibility across diverse MANET conditions. Comprehensive simulations demonstrate that both proposed schemes significantly reduce redundant broadcasts and collisions, leading to lower network overhead and improved message delivery reliability compared to traditional static methods. Specifically, our models achieve consistent packet delivery ratios (PDRs), reduce end-to-end Delay by approximately 23–27%, and lower Redundancy and Overhead by 40–60% and 40–50%, respectively, in high-density and high-mobility scenarios. Furthermore, this comparative analysis highlights the strengths and trade-offs between reinforcement learning-driven broadcasting ratio optimization (Model A) and parameter-based dynamic membership function adaptation (Model B), providing valuable insights for optimizing broadcasting strategies. Full article
(This article belongs to the Special Issue Computational Intelligence in Real-World Optimization Problems: From Theory to Practice)
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