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Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications

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A Special Issue of Algorithms (ISSN 1999-4893) belonging to the section "Analysis of Algorithms and Complexity Theory".

Deadline for manuscript submissions: closed (31 January 2026) | Viewed by 16033

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Editors

Prof. Dr. Carolina Del-Valle-Soto

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Guest Editor

Academy of Computing, School of Engineering, Álvaro del Portillo 49, Universidad Panamericana, Zapopan 45010, Jalisco, 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

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Guest Editor

School of Engineering, Josemaría Escrivá de Balaguer 101, Universidad Panamericana, 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 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-anonymized 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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Related Special Issue

Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications (2nd Edition) in Algorithms (4 articles)

Published Papers (11 papers)

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Research

15 pages, 1290 KB

Open AccessArticle

Efficient Deep Learning-Based M-PSK Detection for OFDM V2V Systems Using MobileNetV3

by Luis E. Tonix-Gleason, José A. Del-Puerto-Flores, Fernando Peña-Campos, Dunstano del Puerto-Flores, Juan-Carlos López-Pimentel, Carolina Del-Valle-Soto and Luis René Vela-Garcia

Algorithms 2026, 19(3), 210; https://doi.org/10.3390/a19030210 - 11 Mar 2026

Cited by 1 | Viewed by 983

Abstract

This paper investigates M-PSK symbol detection in Orthogonal Frequency Division Multiplexing (OFDM) systems for wideband Vehicle-to-Vehicle (V2V) communications using lightweight convolutional neural networks. In doubly dispersive channels, Inter-Carrier Interference (ICI) degrades subcarrier orthogonality, rendering conventional equalization ineffective. Current ICI mitigation techniques face a trade-off between Bit-Error Rate (BER) performance and computational complexity, limiting their applicability in dynamic vehicular scenarios. To address this issue, a low-complexity MobileNetV3-based receiver is proposed, incorporating a signal-model-driven preprocessing stage that compensates for Doppler-induced phase distortions responsible for ICI. Simulation results show that the proposed receiver improves BER performance compared to conventional equalizers and recent neural-based schemes in the low-SNR regime (below 15 dB) while maintaining computational complexity close to linear least-squares detection. Full article

(This article belongs to the Special Issue Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications)

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21 pages, 20696 KB

Open AccessArticle

Optimizing Facial Muscle Activation Features for Emotion Recognition: A Metaheuristic Approach Using Inner Triangle Points

by Erick G. G. de Paz, Ivan Cruz-Aceves, Arturo Hernandez-Aguirre and Miguel-Angel Gil-Rios

Algorithms 2026, 19(1), 57; https://doi.org/10.3390/a19010057 - 8 Jan 2026

Viewed by 976

Abstract

Facial Expression Recognition (FER) is a critical component of affective computing, with deep learning models dominating performance metrics. In contrast, geometric approaches based on the Facial Action Coding System (FACS) offer explainability through using triangles aligned to facial landmarks. The notable points of these triangles capture the deformation of muscles. However, restricting the feature extraction to notable points may be suboptimal. This paper introduces a novel method for optimizing the extraction of features by searching for optimal inner points in 22 facial triangles applying three metaheuristics: Differential Evolution (DE), Particle Swarm Optimization (PSO), and Convex Partition (CP). This results in a set of 59 geometric-based descriptors that capture muscle deformation more accurately. The proposed method was evaluated using five machine learning classifiers on two benchmark databases: the Karolinska Directed Emotional Faces (KDEF) and the Japanese Female Facial Expression (JAFFE). Experimental results demonstrate significant performance improvements. The combination of DE with a Multi-Layer Perceptron (MLP) achieved an accuracy of

0.91

on the KDEF database, while Support Vector Machine (SVM) optimized via CP attained an accuracy of

0.81

on the JAFFE database. Statistical analysis confirms that optimized descriptors yield higher accuracy than previous geometric methods. Full article

(This article belongs to the Special Issue Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications)

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26 pages, 1266 KB

Open AccessEditor’s ChoiceArticle

Hybrid Evolutionary Multi-Objective Method for Automatic Design of a Lightweight CNN Architecture Applied to Coronary Stenosis Classification

by Miguel-Angel Gil-Rios, Ivan Cruz-Aceves, Arturo Hernandez-Aguirre, Erick-G. G.-de-Paz and Juan-Manuel Lopez-Hernandez

Algorithms 2026, 19(1), 47; https://doi.org/10.3390/a19010047 - 5 Jan 2026

Cited by 1 | Viewed by 923

Abstract

This paper presents a novel method based on a Hybrid Multi-Objective Evolutionary strategy for the automatic design of a lightweight convolutional neural network used for coronary stenosis classification. The hybrid methodology consists of two search stages, starting with the Multi-Objective Evolutionary Algorithm based on Decomposition, to generate a set of optimal solutions focused on the minimization of two objectives: the accuracy classification error and the number of learning parameters in the convolutional neural network. Subsequently, the Simulated Annealing algorithm is applied to improve a subset of the solutions produced in the previous step. After the method was complete, a convolutional neural network model consisting of 3498 learning parameters was found by the proposed hybrid strategy, which is a considerably low number compared with the other architectures reported in the literature. Consequently, the found model achieved the highest classification performance rate in terms of the Accuracy and Jaccard Similarity Coefficient metrics with values of

0.94

and

0.89

, respectively, using a database consisting of 608 images of regions with positive and negative coronary stenosis cases. On a second test, the model was tested using a database consisting of 2788 instances of natural and synthetic images of coronary stenosis cases. Corresponding maximum classification rates of

0.97

and

0.93

for the Accuracy and Jaccard Similarity Coefficient metrics, respectively, were achieved. In addition, the average required time to classify a single instance was

0.009

seconds. The obtained results showed that the proposed method is feasible for the automatic design of lightweight convolutional neural networks that can be used as a part of decision-making systems in clinical practice. Full article

(This article belongs to the Special Issue Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications)

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32 pages, 6752 KB

Open AccessArticle

Bayesian Optimisation and Adaptive Evolutionary Algorithms for Higher-Order Fuzzy Models with Application on Wind Speed Prediction

by Panagiotis Korkidis and Anastasios Dounis

Algorithms 2026, 19(1), 46; https://doi.org/10.3390/a19010046 - 5 Jan 2026

Viewed by 929

Abstract

To cope with the highly stochastic nature of wind speed, we explored the development of a predictive methodology. Considering an absence of studies pertaining to wind speed prediction that utilise state-of-the-art fuzzy models, the proposed approach adopted a novel higher-order Takagi–Sugeno–Kang fuzzy model intermixed with variational mode decomposition. The novelty of the predictive fuzzy model arises from the enhancement of rule consequents to include generalised terms and the incorporation of model complexity into the training scheme. To optimise the model, two approaches are considered: an adaptive differential evolution and a surrogate-based optimisation algorithm. The evolutionary approach employed two populations and a dual mutation scheme. The surrogate-based optimisation employed a Bayesian framework by fitting a Gaussian process model to the objective function. The latter approach yielded accurate predictive results while rapidly reducing the training time of the fuzzy model. A sequential wrapper-based algorithm was developed to effectively determine the feature space. The variational mode decomposed wind speed data were predicted individually, using an associated optimised fuzzy model. The proposed method was applied to a real-world wind speed dataset with exceptional approximation results. Comparisons with several artificial intelligence models highlighted the effectiveness and statistical significance of the methodology. Full article

(This article belongs to the Special Issue Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications)

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26 pages, 16853 KB

Open AccessArticle

Semi-Fragile Watermarking Scheme for High-Resolution Color Images: Tamper Identification, Ownership Authentication, and Self-Recovery

by Manuel Cedillo-Hernandez, Antonio Cedillo-Hernandez, Francisco Javier Garcia-Ugalde and Juan Carlos Sanchez-Garcia

Algorithms 2026, 19(1), 28; https://doi.org/10.3390/a19010028 - 26 Dec 2025

Cited by 2 | Viewed by 1723

Abstract

The advancements in communication and information technologies have substantially enabled the extensive distribution and modification of high-resolution color images. Although this accessibility provides many advantages, it also presents risks related to security. Specifically, when image modification is conducted with malicious intent, exceeding typical artistic or enhancement objectives, it can cause significant moral or economic harm to the image owner. To address this security requirement, this study presents an innovative semi-fragile watermarking algorithm designed specifically for high-resolution color images. The proposed method utilizes Discrete Cosine Transform domain watermarking implemented via Quantization Index Modulation with Dither Modulation. It incorporates several elements, such as convolutional encoding, a denoising convolutional neural network, and a very deep super-resolution neural network. This comprehensive strategy aims to provide ownership verification using a logo watermark, in conjunction with tamper detection and content self-recovery mechanisms. The self-recovery criterion is determined using a thumbnail image, created by downscaling to standard definition and applying JPEG2000 lossy compression. The resultant multifunctional design enhances the overall security of the information. Experimental validation confirms the enhanced imperceptibility, robustness, and capacity of the proposed method. Its efficacy was additionally corroborated through comparative analyses using contemporary state-of-the-art algorithms. Full article

(This article belongs to the Special Issue Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications)

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28 pages, 11264 KB

Open AccessArticle

A New Genetic Algorithm-Based Optimization Methodology for Energy Efficiency in Buildings

by Luis Angel Iturralde Carrera, Omar Rodríguez-Abreo, Jose Manuel Álvarez-Alvarado, Gerardo I. Pérez-Soto, Carlos Gustavo Manriquez-Padilla and Juvenal Rodríguez-Reséndiz

Algorithms 2026, 19(1), 27; https://doi.org/10.3390/a19010027 - 26 Dec 2025

Viewed by 1612

Abstract

This study aims to develop a methodology for implementing solar photovoltaic systems (SSFV) in Caribbean hotels. It begins with an analysis of building characteristics to design and size the SSFV, considering panel support structures, system layout, and grid integration. The methodology also evaluates economic and environmental impacts at both company and national levels. Machine learning analysis identified the variables (Degree Days (DG) and Hotel Days Occupied (HDO))

H D O \times D G

as key determinants of energy consumption, with a high coefficient of determination (

R^{2}

= 0.97). Implementing a target energy-saving line achieved a 5.3% reduction (1028 kWh) relative to the baseline. Using a genetic algorithm to optimize the SSFV azimuth angle increased photovoltaic energy production by 14.75%, enhancing efficiency and installation area use. Economic assessments showed a challenging scenario for hotels, with a negative internal rate of return of −10%, a 17 year payback period, and a net present value of USD 20,000. However, on a national scale, significant annual savings of USD 225,990.8 from reduced fuel imports were projected. Additionally, carbon emissions reductions of 18,751.4 tons (

t C O_{2}

) were estimated. The findings highlight the feasibility and benefits of SSFV implementation, emphasizing its potential to improve energy efficiency, reduce costs, and enhance sustainability in the Caribbean hotel sector. Full article

(This article belongs to the Special Issue Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications)

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29 pages, 1328 KB

Open AccessArticle

A Resilient Energy-Efficient Framework for Jamming Mitigation in Cluster-Based Wireless Sensor Networks

by Carolina Del-Valle-Soto, José A. Del-Puerto-Flores, Leonardo J. Valdivia, Aimé Lay-Ekuakille and Paolo Visconti

Algorithms 2025, 18(10), 614; https://doi.org/10.3390/a18100614 - 29 Sep 2025

Cited by 3 | Viewed by 1259

Abstract

This paper presents a resilient and energy-efficient framework for jamming mitigation in cluster-based wireless sensor networks (WSNs), addressing a critical vulnerability in hostile or interference-prone environments. The proposed approa ch integrates dynamic cluster reorganization, adaptive MAC-layer behavior, and multipath routing strategies to restore communication capabilities and sustain network functionality under jamming conditions. The framework is evaluated across heterogeneous topologies using Zigbee and Bluetooth Low Energy (BLE); both stacks were validated in a physical testbed with matched jammer and traffic conditions, while simulation was used solely to tune parameters and support sensitivity analyses. Results demonstrate significant improvements in Packet Delivery Ratio, end-to-end delay, energy consumption, and retransmission rate, with BLE showing particularly high resilience when combined with the mitigation mechanism. Furthermore, a comparative analysis of routing protocols including AODV, GAF, and LEACH reveals that hierarchical protocols achieve superior performance when integrated with the proposed method. This framework has broader applicability in mission-critical IoT domains, including environmental monitoring, industrial automation, and healthcare systems. The findings confirm that the framework offers a scalable and protocol-agnostic defense mechanism, with potential applicability in mission-critical and interference-sensitive IoT deployments. Full article

(This article belongs to the Special Issue Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications)

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20 pages, 1860 KB

Open AccessArticle

Backward Signal Propagation: A Symmetry-Based Training Method for Neural Networks

by Kun Jiang and Zhihong Fu

Algorithms 2025, 18(10), 594; https://doi.org/10.3390/a18100594 - 23 Sep 2025

Viewed by 1180

Abstract

While backpropagation (BP) has long served as the cornerstone of training deep neural networks, it relies heavily on strict differentiation logic and global gradient information, lacking biological plausibility. In this paper, we systematically present a novel neural network training paradigm that depends solely on signal propagation, which we term Backward Signal Propagation (BSP). The core idea of this framework is to reinterpret network training as a symmetry-driven process of discovering inverse causal relationships. Starting from symmetry principles, we define symmetric differential equations and leverage their inherent properties to implement a learning mechanism analogous to differentiation. Furthermore, we introduce the concept of causal distance, a core invariant that bridges the forward propagation and inverse learning processes. It quantifies the influence strength between any two elements in the network, leading to a generalized form of the chain rule. With these innovations, we achieve precise, pointwise adjustment of model parameters. Unlike traditional BP, the BSP method enables parameter updates based solely on local signal features. This work offers a new direction toward efficient and biologically plausible learning algorithms. Full article

(This article belongs to the Special Issue Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications)

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28 pages, 2209 KB

Open AccessArticle

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

Cited by 2 | Viewed by 2556

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 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, 461 KB

Open AccessArticle

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 1242

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 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

Open AccessArticle

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

Cited by 3 | Viewed by 1739

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 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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