Algorithms for Complex Problems

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: 31 January 2025 | Viewed by 671

Special Issue Editors


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Guest Editor
School of Computer Science and Information Technology, University College Cork, T12 K8AF Cork, Ireland
Interests: artificial intelligence; machine learning; operations research; constraint programming; satisfiability; optimization; forecasting

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Guest Editor
Department of Enterprise Engineering, University of Rome “Tor Vergata”, 00133 Roma, Italy
Interests: scheduling; graph theory; optimization; mathematical modeling; supply chain optimization; logistics; transportation; production systems
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Special Issue Information

Dear Colleagues,

Most work on optimization focuses on simple situations with one decision-making agent, one objective, and a number of constraints. These problems can be hard to solve to optimality, yet they ignore several real-world complexities. In an uncertain world, industry is increasingly concerned with risk, uncertainty, robustness, and balancing conflicting goals, and less interested in optimal solutions to oversimplified models.

This Special Issue aims to publish recent advances in algorithms for problems that may be multi-agent, multi-objective, multi-level, multi-stage, or have incomplete information. Of particular interest are problems combining complexities such as those studied in multi-objective multi-agent reinforcement learning, bilevel optimization under uncertainty, and influence diagrams. Research areas of interest include the following:

  • Stochastic programming;
  • Bilevel programming;
  • Dynamic programming;
  • Reinforcement learning;
  • Game theory;
  • Robust optimization;
  • Metaheuristics;
  • Greedy and heuristic algorithms;
  • Machine learning;
  • Simulation optimization.

Dr. Steven Prestwich
Prof. Dr. Massimiliano Caramia
Guest Editors

Manuscript Submission Information

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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

  • multi-objective optimization
  • multi-level optimization
  • multi-agent reinforcement learning

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Published Papers (1 paper)

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Research

20 pages, 4623 KiB  
Article
Automatic Vertical Parking Reference Trajectory Based on Improved Immune Shark Smell Optimization
by Yan Chen, Gang Liu, Longda Wang and Bing Xia
Algorithms 2024, 17(7), 308; https://doi.org/10.3390/a17070308 - 11 Jul 2024
Viewed by 429
Abstract
Parking path optimization is the principal problem of automatic vertical parking (AVP); however, it is difficult to determine a collision avoiding, smooth, and accurate optimized parking path using traditional parking reference trajectory optimization methods. In order to implement high-performance automatic parking reference trajectory [...] Read more.
Parking path optimization is the principal problem of automatic vertical parking (AVP); however, it is difficult to determine a collision avoiding, smooth, and accurate optimized parking path using traditional parking reference trajectory optimization methods. In order to implement high-performance automatic parking reference trajectory optimization, we establish an automatic parking reference trajectory optimization model using cubic spline interpolation, and we propose an improved immune shark smell optimization (IISSO) to solve it. Firstly, we take the length of the parking reference trajectory as the optimization objective, and we introduce an intelligent automatic parking path optimization model using cubic spline interpolation. Secondly, the improved immune shark optimization algorithm combines the immune, refraction, and Gaussian variation mechanisms, thus effectively improving its global optimization ability. The simulation results for the parking path optimization experiments indicate that the proposed IISSO has a higher optimization accuracy and faster calculation speed; hence, it can obtain a parking path with higher optimization performance. Full article
(This article belongs to the Special Issue Algorithms for Complex Problems)
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