Meta-Heuristics and Machine Learning in Modelling, Developing and Optimising Complex Systems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Analysis of Algorithms and Complexity Theory".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 1539

Special Issue Editors


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Optical Metrology Division, Centro de Investigaciones en Óptica. A.C., Lomas del Bosque 115, León 37150, Mexico
Interests: computational intelligence; evolutionary algorithms; computer vision; optical metrology
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Special Issue Information

Dear Colleagues,

In recent years, complex systems have been applied in much of real life, such as studying self-organisation and critical phenomena from physics, spontaneous order from the social sciences, chaos from mathematics, adaptation from biology, and many others. Complex systems are, therefore, often used as a broad term encompassing a research approach to problems in many diverse disciplines, including statistical physics, information theory, nonlinear dynamics, computer science, meteorology, anthropology, sociology, economics, psychology, and biology. A metaheuristic is an advanced program that proposes a set of procedures or strategies to design heuristic optimisation algorithms. Machine learning is a branch of artificial intelligence (AI) that focuses on using data and algorithms to imitate how humans learn, gradually improving its accuracy. Advanced machine learning and optimisation algorithms have been rapidly expanding to solve various aspects of complex systems.

This Special Issue is devoted to showing the application of classic, hybrid, combinatorial, and novel metaheuristics and machine learning in the modelling, development, and optimisation of complex systems. Review and survey articles on the following topics are also encouraged for submission.

Topics of interest for publication include but are not limited to:

  1. Rethinking and application of the classic optimisation and machine learning algorithms for complex systems;
  2. Hybridisation insights into optimisation and machine learning algorithms for solving complex systems;
  3. Machine-learning-based predictive modelling in modelling complex systems;
  4. Advanced metaheuristics and machine learning algorithms in complex systems optimisation challenges;
  5. Adversarial machine learning (ML) applications for complex systems modelling;
  6. The application of modern optimisation and machine learning algorithms handling nonlinearity, spontaneous order, and adaptation in complex systems.

Dr. Mehdi Neshat
Dr. Francisco Cuevas De La Rosa
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. 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

  • complex systems
  • meta-heuristics
  • machine learning
  • deep learning
  • transfer learning
  • optimisation
  • evolutionary algorithms
  • swarm intelligence
  • genetic algorithms
  • mathematical optimization

Published Papers (1 paper)

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Research

13 pages, 2205 KiB  
Article
Research on a Fast Image-Matching Algorithm Based on Nonlinear Filtering
by Chenglong Yin, Fei Zhang, Bin Hao, Zijian Fu and Xiaoyu Pang
Algorithms 2024, 17(4), 165; https://doi.org/10.3390/a17040165 - 19 Apr 2024
Viewed by 534
Abstract
Computer vision technology is being applied at an unprecedented speed in various fields such as 3D scene reconstruction, object detection and recognition, video content tracking, pose estimation, and motion estimation. To address the issues of low accuracy and high time complexity in traditional [...] Read more.
Computer vision technology is being applied at an unprecedented speed in various fields such as 3D scene reconstruction, object detection and recognition, video content tracking, pose estimation, and motion estimation. To address the issues of low accuracy and high time complexity in traditional image feature point matching, a fast image-matching algorithm based on nonlinear filtering is proposed. By applying nonlinear diffusion filtering to scene images, details and edge information can be effectively extracted. The feature descriptors of the feature points are transformed into binary form, occupying less storage space and thus reducing matching time. The adaptive RANSAC algorithm is utilized to eliminate mismatched feature points, thereby improving matching accuracy. Our experimental results on the Mikolajcyzk image dataset comparing the SIFT algorithm with SURF-, BRISK-, and ORB-improved algorithms based on the SIFT algorithm conclude that the fast image-matching algorithm based on nonlinear filtering reduces matching time by three-quarters, with an overall average accuracy of over 7% higher than other algorithms. These experiments demonstrate that the fast image-matching algorithm based on nonlinear filtering has better robustness and real-time performance. Full article
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