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Journals
Algorithms
Special Issues
Recent Advances in Computational Intelligence for Path Planning
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Recent Advances in Computational Intelligence for Path Planning

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 1958

Special Issue Editors

Prof. Dr. Yu-Huei Cheng

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan
Interests: artificial intelligence; automatic control; bioinformatics; biomedical engineering; computational intelligence; embedded systems; electric and hybrid vehicles; Internet of Things; machine learning; mobile medical; power electronics; renewable energy
Special Issues, Collections and Topics in MDPI journals
Dr. Che-Nan Kuo

E-Mail Website
Guest Editor
Department of Artificial Intelligence, CTBC Business School, Tainan 709, Taiwan
Interests: artificial intelligence; interconnection networks; discrete mathematics; computation theory; graph theory; algorithm analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Path planning based on computational intelligence is an important technology in the research of traffic engineering, packet-switched networks, self-driving vehicles, and mobile robots. We invite you to submit your latest research on the development of path planning algorithms to this Special Issue, “Recent Advances in Computational Intelligence for Path Planning”. We look for innovative and effective approaches for path planning. High-quality papers are solicited to address both theoretical and practical issues of path-planning algorithms. Submissions are welcome both for improved path-planning algorithms, as well as new applications.

Prof. Dr. Yu-Huei Cheng
Dr. Che-Nan Kuo
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

  • obstacle avoidance problems
  • mobile robot navigation
  • multi-robot path planning
  • traffic engineering
  • packet-switched network
  • self-driving vehicles
  • vehicle routing
  • evolutionary algorithms
  • routing algorithms
  • approximation algorithms
  • complexity issues
  • energy-optimal path planning
  • reinforcement learning

Published Papers (1 paper)

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Research

26 pages, 21938 KiB  
Article
Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
by Pornrawee Tatit, Kiki Adhinugraha and David Taniar
Algorithms 2024, 17(1), 29; https://doi.org/10.3390/a17010029 - 10 Jan 2024
Viewed by 1344
Abstract
Using spatial data in mobile applications has grown significantly, thereby empowering users to explore locations, navigate unfamiliar areas, find transportation routes, employ geomarketing strategies, and model environmental factors. Spatial databases are pivotal in efficiently storing, retrieving, and manipulating spatial data to fulfill users’ [...] Read more.
Using spatial data in mobile applications has grown significantly, thereby empowering users to explore locations, navigate unfamiliar areas, find transportation routes, employ geomarketing strategies, and model environmental factors. Spatial databases are pivotal in efficiently storing, retrieving, and manipulating spatial data to fulfill users’ needs. Two fundamental spatial query types, k-nearest neighbors (kNN) and range search, enable users to access specific points of interest (POIs) based on their location, which are measured by actual road distance. However, retrieving the nearest POIs using actual road distance can be computationally intensive due to the need to find the shortest distance. Using straight-line measurements could expedite the process but might compromise accuracy. Consequently, this study aims to evaluate the accuracy of the Euclidean distance method in POIs retrieval by comparing it with the road network distance method. The primary focus is determining whether the trade-off between computational time and accuracy is justified, thus employing the Open Source Routing Machine (OSRM) for distance extraction. The assessment encompasses diverse scenarios and analyses factors influencing the accuracy of the Euclidean distance method. The methodology employs a quantitative approach, thereby categorizing query points based on density and analyzing them using kNN and range query methods. Accuracy in the Euclidean distance method is evaluated against the road network distance method. The results demonstrate peak accuracy for kNN queries at k=1, thus exceeding 85% across classes but declining as k increases. Range queries show varied accuracy based on POI density, with higher-density classes exhibiting earlier accuracy increases. Notably, datasets with fewer POIs exhibit unexpectedly higher accuracy, thereby providing valuable insights into spatial query processing. Full article
(This article belongs to the Special Issue Recent Advances in Computational Intelligence for Path Planning)
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