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Algorithms
Special Issues
Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications
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Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications

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: 30 November 2025 | Viewed by 1147

Special Issue Editors

Prof. Dr. Carolina Del Valle Soto

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Guest Editor
Academy of Computing, School of Engineering, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, Mexico
Interests: algorithm design; optimization techniques; wireless sensor networks; jamming detection; artificial intelligence; routing in complex networks; wearable and IoT systems; energy-efficient protocols
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ramiro Velázquez

E-Mail Website
Guest Editor
School of Engineering, Universidad Panamericana, Josemaría Escrivá de Balaguer 101, Aguascalientes 20296, Mexico
Interests: assistive technology; mechatronic systems; sensors and actuators; wearables; haptics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, “Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications”, invites high-quality contributions that explore the design, analysis, and application of novel algorithms. Its aim is to unify diverse computational approaches—ranging from theoretical models to real-world deployments—across fields such as sensor networks, artificial intelligence, robotics and mechatronics, healthcare systems, and smart environments.

We welcome submissions that demonstrate methodological rigor and practical relevance, particularly those that address optimization under constraints, performance analysis, or cross-disciplinary integration. Studies that leverage metaheuristics, neural network-based models, or algorithmic frameworks for emergent applications such as IoT, smart healthcare, environmental monitoring, and resilient networks are especially encouraged.

This Special Issue also aligns with the Algorithms journal’s mission to support reproducibility and interdisciplinary impact. As such, detailed methodological documentation and openly shared resources (e.g., source code, datasets) are highly encouraged.

Prof. Dr. Carolina Del Valle Soto
Prof. Dr. Ramiro Velázquez
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 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

  • algorithm design and optimization
  • energy-aware computing
  • artificial intelligence and machine learning algorithms
  • robotics and mechatronics
  • routing protocols
  • wireless sensor networks and IoT
  • metaheuristics and hybrid algorithms
  • resilient and secure networks
  • interdisciplinary algorithm applications

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

Published Papers (3 papers)

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28 pages, 2209 KB  
Article
A Reinforcement Learning Hyper-Heuristic with Cumulative Rewards for Dual-Peak Time-Varying Network Optimization in Heterogeneous Multi-Trip Vehicle Routing
by Xiaochuan Wang, Na Li and Xingchen Jin
Algorithms 2025, 18(9), 536; https://doi.org/10.3390/a18090536 - 22 Aug 2025
Viewed by 258
Abstract
Urban logistics face complexity due to traffic congestion, fleet heterogeneity, warehouse constraints, and driver workload balancing, especially in the Heterogeneous Multi-Trip Vehicle Routing Problem with Time Windows and Time-Varying Networks (HMTVRPTW-TVN). We develop a mixed-integer linear programming (MILP) model with dual-peak time discretization [...] Read more.
Urban logistics face complexity due to traffic congestion, fleet heterogeneity, warehouse constraints, and driver workload balancing, especially in the Heterogeneous Multi-Trip Vehicle Routing Problem with Time Windows and Time-Varying Networks (HMTVRPTW-TVN). We develop a mixed-integer linear programming (MILP) model with dual-peak time discretization and exact linearization for heterogeneous fleet coordination. Given the NP-hard nature, we propose a Hyper-Heuristic based on Cumulative Reward Q-Learning (HHCRQL), integrating reinforcement learning with heuristic operators in a Markov Decision Process (MDP). The algorithm dynamically selects operators using a four-dimensional state space and a cumulative reward function combining timestep and fitness. Experiments show that, for small instances, HHCRQL achieves solutions within 3% of Gurobi’s optimum when customer nodes exceed 15, outperforming Large Neighborhood Search (LNS) and LNS with Simulated Annealing (LNSSA) with stable, shorter runtime. For large-scale instances, HHCRQL reduces gaps by up to 9.17% versus Iterated Local Search (ILS), 6.74% versus LNS, and 5.95% versus LNSSA, while maintaining relatively stable runtime. Real-world validation using Shanghai logistics data reduces waiting times by 35.36% and total transportation times by 24.68%, confirming HHCRQL’s effectiveness, robustness, and scalability. Full article
(This article belongs to the Special Issue Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications)
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14 pages, 460 KB  
Article
Modeling Local Search Metaheuristics Using Markov Decision Processes
by Rubén Ruiz-Torrubiano, Deepak Dhungana, Sarita Paudel and Himanshu Buckchash
Algorithms 2025, 18(8), 512; https://doi.org/10.3390/a18080512 - 14 Aug 2025
Viewed by 176
Abstract
Local search metaheuristics like tabu search or simulated annealing are popular heuristic optimization algorithms for finding near-optimal solutions for combinatorial optimization problems. However, it is still challenging for researchers and practitioners to analyze their behavior and systematically choose one over a vast set [...] Read more.
Local search metaheuristics like tabu search or simulated annealing are popular heuristic optimization algorithms for finding near-optimal solutions for combinatorial optimization problems. However, it is still challenging for researchers and practitioners to analyze their behavior and systematically choose one over a vast set of possible metaheuristics for the particular problem at hand. In this paper, we introduce a theoretical framework based on Markov Decision Processes (MDPs) for analyzing local search metaheuristics. This framework not only helps in providing convergence results for individual algorithms but also provides an explicit characterization of the exploration–exploitation tradeoff and a theory-grounded guidance for practitioners for choosing an appropriate metaheuristic for the problem at hand. We present this framework in detail and show how to apply it in the case of hill climbing and the simulated annealing algorithm, including computational experiments. Full article
(This article belongs to the Special Issue Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications)
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29 pages, 6462 KB  
Article
A Clustering-Based Dimensionality Reduction Method Guided by POD Structures and Its Application to Convective Flow Problems
by Qingyang Yuan and Bo Zhang
Algorithms 2025, 18(6), 366; https://doi.org/10.3390/a18060366 - 17 Jun 2025
Viewed by 395
Abstract
Proper orthogonal decomposition (POD) is a widely used linear dimensionality reduction technique, but it often fails to capture critical features in complex nonlinear flows. In contrast, clustering methods are effective for nonlinear feature extraction, yet their application in dimensionality reduction methods is hindered [...] Read more.
Proper orthogonal decomposition (POD) is a widely used linear dimensionality reduction technique, but it often fails to capture critical features in complex nonlinear flows. In contrast, clustering methods are effective for nonlinear feature extraction, yet their application in dimensionality reduction methods is hindered by unstable cluster initialization and inefficient mode sorting. To address these issues, we propose a clustering-based dimensionality reduction method guided by POD structures (C-POD), which uses POD preprocessing to stabilize the selection of cluster centers. Additionally, we introduce an entropy-controlled Euclidean-to-probability mapping (ECEPM) method to improve modal sorting and assess mode importance. The C-POD approach is evaluated using the one-dimensional Burgers’ equation and a two-dimensional cylinder wake flow. Results show that C-POD achieves higher accuracy in dimensionality reduction than POD. Its dominant modes capture more temporal dynamics, while higher-order modes offer better physical interpretability. When solving an inverse problem using sparse sensor data, the Gappy C-POD method improves reconstruction accuracy by 19.75% and enhances the lower bound of reconstruction capability by 13.4% compared to Gappy POD. Overall, C-POD demonstrates strong potential for modeling and reconstructing complex nonlinear flow fields, providing a valuable tool for dimensionality reduction methods in fluid dynamics. Full article
(This article belongs to the Special Issue Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications)
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