AI and Computational Methods in Engineering and Science: 2nd Edition

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 567

Special Issue Editors

Special Issue Information

Dear Colleagues,

AI methods have shown great potential in many fields. Computational simulation or modeling is also a key concept and plays an important role in the environment, ecology, science and engineering, providing a fantastic tool to help us to understand the world; it is therefore vital that we apply such powerful AI to computational modeling or combine AI and modeling to further enhance our worldly knowledge.

This Special Issue welcomes original research articles, reviews, and case studies that explore the diverse applications of AI and computational methods in engineering and science. Contributions may cover a wide range of topics, including, but not limited to, the following:

  • Machine learning and deep learning algorithms for engineering and scientific modeling;
  • Intelligent systems and decision support in engineering and scientific processes;
  • Optimization techniques and evolutionary algorithms for engineering design and problem-solving;
  • Data-driven approaches for predictive modeling, anomaly detection, and fault diagnosis;
  • Simulation and modeling techniques enhanced by AI and computational methods;
  • Big data analytics and data mining in engineering and scientific domains;
  • Integration of AI with Internet of Things (IoT) and cyber–physical systems;
  • AI-enabled robotics and automation in engineering and scientific applications;
  • Computational intelligence in renewable energy systems, environmental sciences, and sustainability;
  • AI-driven image processing, computer vision, and pattern recognition in engineering and science.

This Special Issue will provide a platform for researchers, academics, and industry professionals to share their latest findings, methodologies, and real-world applications in the field of AI and computational methods within engineering and science. We encourage both theoretical and practical contributions that demonstrate the potential and impact of AI-driven approaches to address complex engineering and scientific challenges.

Prof. Dr. Dunhui Xiao
Prof. Dr. Shuai (Steven) Li
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 250 words) can be sent to the Editorial Office for assessment.

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 1800 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

  • machine learning
  • deep learning
  • AI applications
  • AI applications in environment
  • AI in ecology
  • AI in science and engineering

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

Published Papers (1 paper)

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

Research

19 pages, 1202 KB  
Article
Optimizing Navigation in Mobile Robots: Modified Particle Swarm Optimization and Genetic Algorithms for Effective Path Planning
by Mohamed Amr, Ahmed Bahgat, Hassan Rashad, Azza Ibrahim and Ayman Youssef
Algorithms 2025, 18(11), 719; https://doi.org/10.3390/a18110719 - 14 Nov 2025
Viewed by 403
Abstract
Mobile robots are increasingly integral to diverse applications, with path-planning algorithms being essential for efficient and secure mobile robot navigation. Mobile robot path planning is defined as the design of the least time-consuming, shortest-distance, and most collision-free path from the starting point to [...] Read more.
Mobile robots are increasingly integral to diverse applications, with path-planning algorithms being essential for efficient and secure mobile robot navigation. Mobile robot path planning is defined as the design of the least time-consuming, shortest-distance, and most collision-free path from the starting point to the endpoint for the mobile robot’s autonomous movement. This study investigates and assesses two widely used algorithms in artificial intelligence (AI)—Improved Particle Swarm Optimization (IPSO) and Improved Genetic Algorithm (IGA)—for path planning of mobile robot navigation problems. In this work Manhattan movements are proposed as a distance formula to modify both algorithms in the path planning of the mobile robot navigation problem. Unlike the traditional GA and PSO, which can use horizontal search, the proposed algorithm relies on vertical search, which gives us an advantage. The results demonstrate the effectiveness of these modified algorithms in barrier detection and obstacle avoidance. Six different experiments were run using both improved algorithms to show their ability to achieve their goal and avoid obstacles in various scenarios with different complexities. Across various scenarios, the tested AI algorithms performed effectively, regardless of the map scale and complexity. This paper proposes a complete comparison between the two improved algorithms in different scenarios. The results show that the algorithms’ performance is influenced more by the density of walls and obstacles than by the size or complexity of the map. Full article
(This article belongs to the Special Issue AI and Computational Methods in Engineering and Science: 2nd Edition)
Show Figures

Figure 1

Back to TopTop