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Algorithms
Special Issues
Algorithms for Complex Problems
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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: closed (31 January 2025) | Viewed by 3076

Special Issue Editors

Dr. Steven Prestwich

E-Mail Website
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
Prof. Dr. Massimiliano Caramia

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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

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

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

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

Published Papers (3 papers)

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17 pages, 1344 KiB  
Article
A Two-Stage Multi-Objective Optimization Algorithm for Solving Large-Scale Optimization Problems
by Jiaqi Liu and Tianyu Liu
Algorithms 2025, 18(3), 164; https://doi.org/10.3390/a18030164 - 13 Mar 2025
Viewed by 347
Abstract
For large-scale multi-objective optimization, it is particularly challenging for evolutionary algorithms to converge to the Pareto Front. Most existing multi-objective evolutionary algorithms (MOEAs) handle convergence and diversity in a mutually dependent manner during the evolution process. In this case, the performance degradation of [...] Read more.
For large-scale multi-objective optimization, it is particularly challenging for evolutionary algorithms to converge to the Pareto Front. Most existing multi-objective evolutionary algorithms (MOEAs) handle convergence and diversity in a mutually dependent manner during the evolution process. In this case, the performance degradation of one solution may lead to the deterioration of the performance of the other solution. This paper proposes a two-stage multi-objective optimization algorithm based on decision variable clustering (LSMOEA-VT) to solve large-scale optimization problems. In LSMOEA-VT, decision variables are divided into two categories and use dimensionality reduction methods to optimize the variables that affect evolutionary convergence. Following this, we performed an interdependence analysis to break down the convergence variables into multiple subcomponents that are more tractable. Furthermore, a non-dominated dynamic weight aggregation method is used to enhance the diversity of the population. To evaluate the performance of our proposed algorithm, we performed extensive comparative experiments against four optimization algorithms across a diverse set of benchmark problems, including eight multi-objective optimization problems and nine large-scale optimization problems. The experimental results show that our proposed algorithm performs well on some test functions and has certain competitiveness. Full article
(This article belongs to the Special Issue Algorithms for Complex Problems)
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15 pages, 418 KiB  
Article
Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems
by Salam Fraihat, Qusai Shambour, Mohammed Azmi Al-Betar and Sharif Naser Makhadmeh
Algorithms 2024, 17(12), 561; https://doi.org/10.3390/a17120561 - 8 Dec 2024
Cited by 1 | Viewed by 1293
Abstract
In recent years, recommender systems have become a crucial tool, assisting users in discovering and engaging with valuable information and services. Multi-criteria recommender systems have demonstrated significant value in assisting users to identify the most relevant items by considering various aspects of user [...] Read more.
In recent years, recommender systems have become a crucial tool, assisting users in discovering and engaging with valuable information and services. Multi-criteria recommender systems have demonstrated significant value in assisting users to identify the most relevant items by considering various aspects of user experiences. Deep learning (DL) models demonstrated outstanding performance across different domains: computer vision, natural language processing, image analysis, pattern recognition, and recommender systems. In this study, we introduce a deep learning model using VAE to improve multi-criteria recommendation systems. Specifically, we propose a variational autoencoder-based model for multi-criteria recommendation systems (VAE-MCRS). The VAE-MCRS model is sequentially trained across multiple criteria to uncover patterns that allow for better representation of user–item interactions. The VAE-MCRS model utilizes the latent features generated by the VAE in conjunction with user–item interactions to enhance recommendation accuracy and predict ratings for unrated items. Experiments carried out using the Yahoo! Movies multi-criteria dataset demonstrate that the proposed model surpasses other state-of-the-art recommendation algorithms, achieving a Mean Absolute Error (MAE) of 0.6038 and a Root Mean Squared Error (RMSE) of 0.7085, demonstrating its superior performance in providing more precise recommendations for multi-criteria recommendation tasks. Full article
(This article belongs to the Special Issue Algorithms for Complex Problems)
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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 848
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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