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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 November 2025 | Viewed by 18637

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Special Issue Editors

Dr. Amin Amini

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School of Engineering and Computing, University of Central Lancashire (UCLan), Preston PR1 2HE, UK
Interests: artificial intelligence; computer vision; digital healthcare; image processing; computational thinking; assisted living

Dr. Bacha Rehman

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Department of Science and Engineering, Southampton Solent University, Southampton SO14 0YN, UK
Interests: affective computing; investigating multimodal data; hybrid DNNs; applications of AI; data science; computer vision; time-series and financial market analysis; FinTech
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chih-Lung Lin

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Graduate Institute of Intelligent Robotics, Hwa Hsia University of Technology, New Taipei City 235, Taiwan
Interests: artificial intelligence; machine learning; image processing; biometrics; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue of Electronics, “Advances in Algorithm Optimization and Computational Intelligence,” is a pivotal scholarly contribution to the dynamic domain of computer science. Our aim is to provide a conduit for academics and industry professionals in terms of disseminating their research outcomes and methodologies in the realms of algorithmic optimization and computational intelligence.

This Special Issue’s objective is to spotlight avant-garde research and methodologies that augment the efficacy, robustness, and versatility of algorithms. This aligns seamlessly with the journal’s overarching mission of fostering state-of-the-art research in computer science and its intersecting disciplines. The focus of this Issue is the exploration and development of novel algorithmic strategies, the application of machine learning techniques for optimization, and the advancement of artificial intelligence paradigms for complex problem solving.

Potential article themes for this Special Issue include, but are not limited to, machine learning algorithms, evolutionary computation, swarm intelligence, artificial neural networks, fuzzy systems, and decision support systems. These themes reflect the current trends and future directions in the realm of computational intelligence and algorithm optimization. This Issue encourages submissions that offer novel insights, propose new methodologies, or apply existing techniques in innovative ways to solve complex problems. This is an excellent opportunity for scholars to contribute to and shape discourse in this crucial research area.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

Machine learning algorithms
Evolutionary computation
Swarm intelligence
Artificial neural networks
Fuzzy systems
Decision support systems
Optimization algorithms
Deep learning
Natural language processing
Computer vision

Dr. Amin Amini
Dr. Bacha Rehman
Prof. Dr. Chih-Lung Lin
Guest Editors

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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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

artificial intelligence
image processing
computer vision
algorithm optimization
computational intelligence

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Published Papers (13 papers)

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14 pages, 1728 KiB

Open AccessArticle

Accelerating High-Frequency Circuit Optimization Using Machine Learning-Generated Inverse Maps for Enhanced Space Mapping

by Jorge Davalos-Guzman, Jose L. Chavez-Hurtado and Zabdiel Brito-Brito

Electronics 2025, 14(15), 3097; https://doi.org/10.3390/electronics14153097 - 3 Aug 2025

Viewed by 290

Abstract

The optimization of high-frequency circuits remains a computationally intensive task due to the need for repeated high-fidelity electromagnetic (EM) simulations. To address this challenge, we propose a novel integration of machine learning-generated inverse maps within the space mapping (SM) optimization framework to significantly accelerate circuit optimization while maintaining high accuracy. The proposed approach leverages Bayesian Neural Networks (BNNs) and surrogate modeling techniques to construct an inverse mapping function that directly predicts design parameters from target performance metrics, bypassing iterative forward simulations. The methodology was validated using a low-pass filter optimization scenario, where the inverse surrogate model was trained using electromagnetic simulations from COMSOL Multiphysics 2024 r6.3 and optimized using MATLAB R2024b r24.2 trust region algorithm. Experimental results demonstrate that our approach reduces the number of high-fidelity simulations by over 80% compared to conventional SM techniques while achieving high accuracy with a mean absolute error (MAE) of 0.0262 (0.47%). Additionally, convergence efficiency was significantly improved, with the inverse surrogate model requiring only 31 coarse model simulations, compared to 580 in traditional SM. These findings demonstrate that machine learning-driven inverse surrogate modeling significantly reduces computational overhead, accelerates optimization, and enhances the accuracy of high-frequency circuit design. This approach offers a promising alternative to traditional SM methods, paving the way for more efficient RF and microwave circuit design workflows. Full article

(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)

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21 pages, 677 KiB

Open AccessArticle

Exploring Tabu Tenure Policies with Machine Learning

by Anna Konovalenko and Lars Magnus Hvattum

Electronics 2025, 14(13), 2642; https://doi.org/10.3390/electronics14132642 - 30 Jun 2025

Viewed by 280

Abstract

Tabu search is a well-known local search-based metaheuristic, widely used for tackling complex combinatorial optimization problems. As with other metaheuristics, its performance is sensitive to parameter configurations, requiring careful tuning. Among the critical parameters of tabu search is the tabu tenure. This study aims to identify key search attributes and instance characteristics that can help establish comprehensive guidelines for a robust tabu tenure policy. First, a review different tabu tenure policies is provided. Next, critical baselines to understand the fundamental relationship between tabu tenure settings and solution quality are established. We verified that generalizable parameter selection rules provide value when implementing metaheuristic frameworks, specifically showing that a more robust tabu tenure policy can be achieved by considering whether a move is improving or non-improving. Finally, we explore the integration of machine learning techniques that exploits both dynamic search attributes and static instance characteristics to obtain effective and robust tabu tenure policies. A statistical analysis confirms that the integration of machine learning yields statistically significant performance gains, achieving a mean improvement of 12.23 (standard deviation 137.25,

n =

10,000 observations) when compared to a standard randomized tabu tenure selection (p-value < 0.001). While the integration of machine learning introduces additional computational overhead, it may be justified in scenarios where heuristics are repeatedly applied to structurally similar problem instances, and even small improvements in solution quality can accumulate to large overall gains. Nonetheless, our methods have limitations. The influence of the tabu tenure parameter is difficult to detect in real time during the search process, complicating the reliable identification of when and how tenure adjustments impact search performance. Additionally, the proposed policies exhibit similar performance on the chosen instances, further complicating the evaluation and differentiation of policy effectiveness. Full article

(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)

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19 pages, 1273 KiB

Open AccessArticle

Beyond the Benchmark: A Customizable Platform for Real-Time, Preference-Driven LLM Evaluation

by George Zografos and Lefteris Moussiades

Electronics 2025, 14(13), 2577; https://doi.org/10.3390/electronics14132577 - 26 Jun 2025

Viewed by 738

Abstract

The rapid progress of Large Language Models (LLMs) has intensified the demand for flexible evaluation frameworks capable of accommodating diverse user needs across a growing variety of applications. While numerous standardized benchmarks exist for evaluating general-purpose LLMs, they remain limited in both scope and adaptability, often failing to capture domain-specific quality criteria. In many specialized domains, suitable benchmarks are lacking, leaving practitioners without systematic tools to assess the suitability of LLMs for their specific tasks. This paper presents LLM PromptScope (LPS), a customizable, real-time evaluation framework that enables users to define qualitative evaluation criteria aligned with their domain-specific needs. LPS integrates a novel LLM-as-a-Judge mechanism that leverages multiple language models as evaluators, minimizing human involvement while incorporating subjective preferences into the evaluation process. We validate the proposed framework through experiments on widely used datasets (MMLU, Math, and HumanEval), comparing conventional benchmark rankings with preference-driven assessments across multiple state-of-the-art LLMs. Statistical analyses demonstrate that user-defined evaluation criteria can significantly impact model rankings, particularly in open-ended tasks where standard benchmarks offer limited guidance. The results highlight LPS’s potential as a practical decision-support tool, particularly valuable in domains lacking mature benchmarks, offering both flexibility and rigor in model selection for real-world deployment. Full article

(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)

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19 pages, 1296 KiB

Open AccessArticle

Investment Portfolios Optimization with Genetic Algorithm: An Approach Applied to the Spanish Market (IBEX 35)

by Sandra Millán-Palacios and Javier Sánchez-Soriano

Electronics 2025, 14(13), 2559; https://doi.org/10.3390/electronics14132559 - 24 Jun 2025

Viewed by 1177

Abstract

The results of this study validate the use of single-objective genetic algorithms as an effective tool for portfolio optimization in the Spanish market. Through an evolutionary approach with advanced objective functions and a phased structure (training, validation, and testing), the quality and stability of the generated portfolios were significantly improved. The single-objective genetic algorithms with a Complex Objective Function (SGA-COF-1) model delivered outstanding returns with high robustness and were adaptable to different risk profiles, including the ESG criteria. These contributions open multiple future research directions, such as the incorporation of predictive models, expansion to international markets, and the use of more sophisticated evolutionary algorithms. The proposed methodological framework (flexible and scalable) provides a solid foundation for the development of automated and sustainable quantitative investment solutions. Full article

(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)

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19 pages, 970 KiB

Open AccessArticle

A Method for the Predictive Maintenance Resource Scheduling of Aircraft Based on Heterogeneous Hypergraphs

by Long Kang, Muhua He, Jiahui Zhou, Yiran Hou, Bo Xu and Haifeng Liu

Electronics 2025, 14(4), 782; https://doi.org/10.3390/electronics14040782 - 17 Feb 2025

Viewed by 934

Abstract

The resource scheduling optimization problem in predictive maintenance is a complex operational research challenge involving reasoning about stochastic failure models and the dynamic allocation of repair resources. In recent years, resource scheduling methods based on deep learning have been increasingly applied in this field, demonstrating promising performances. Among these, resource scheduling algorithms based on heterogeneous graphs have shown exceptional results in multi-objective optimization tasks. However, conventional graph neural networks primarily operate on binary relational graphs, which struggle to effectively utilize data in multi-relational settings, thereby limiting the scheduler’s performance. To address this limitation, this paper proposes a heterogeneous hypergraph-based resource scheduling algorithm for aircraft maintenance tasks to tackle the challenges of higher-order and many-to-many relationship processing inherent in traditional graph neural networks. Specifically, the proposed algorithm defines aircraft nodes and maintenance personnel nodes while introducing decision nodes and state nodes to construct hyperedges. It employs hypergraph convolution with a multi-head attention mechanism to learn the long-term value of decisions, followed by policy selection based on a Markov decision process. This method offers a lightweight, non-parametric dynamic scheduling solution capable of robust learning in highly stochastic environments. Comparative experiments conducted on three datasets of varying scales demonstrate that the proposed method outperforms both heuristic algorithms and existing deep learning methods in terms of its optimization performance on M1 and M2 metrics. Furthermore, it surpasses resource scheduling algorithms based on heterogeneous graph neural networks across multiple metrics. Full article

(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)

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24 pages, 597 KiB

Open AccessArticle

Phase-Angle-Encoded Snake Optimization Algorithm for K-Means Clustering

by Dan Xue, Sen-Yuan Pang, Ning Liu, Shang-Kun Liu and Wei-Min Zheng

Electronics 2024, 13(21), 4215; https://doi.org/10.3390/electronics13214215 - 27 Oct 2024

Cited by 1 | Viewed by 984

Abstract

The rapid development of metaheuristic algorithms proves their advantages in optimization. Data clustering, as an optimization problem, faces challenges for high accuracy. The K-means algorithm is traditaaional but has low clustering accuracy. In this paper, the phase-angle-encoded snake optimization algorithm (

θ

-SO), [...] Read more.

θ

-SO), based on mapping strategy, is proposed for data clustering. The disadvantages of traditional snake optimization include slow convergence speed and poor optimization accuracy. The improved

θ

-SO uses phase angles for boundary setting and enables efficient adjustments in the phase angle vector to accelerate convergence, while employing a Gaussian distribution strategy to enhance optimization accuracy. The optimization performance of

θ

-SO is evaluated by CEC2013 datasets and compared with other metaheuristic algorithms. Additionally, its clustering optimization capabilities are tested on Iris, Wine, Seeds, and CMC datasets, using the classification error rate and sum of intra-cluster distances. Experimental results show

θ

-SO surpasses other algorithms on over 2/3 of CEC2013 test functions, hitting a 90% high-performance mark across all clustering optimization tasks. The method proposed in this paper effectively addresses the issues of data clustering difficulty and low clustering accuracy. Full article

(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)

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11 pages, 2205 KiB

Open AccessArticle

A Novel Approach for Solving the N-Queen Problem Using a Non-Sequential Conflict Resolution Algorithm

by Omid Moghimi and Amin Amini

Electronics 2024, 13(20), 4065; https://doi.org/10.3390/electronics13204065 - 16 Oct 2024

Cited by 2 | Viewed by 2726

Abstract

The N-Queens problem is a fundamental challenge in combinatorial optimization, commonly used as a benchmark for assessing the efficiency of algorithms. Traditional algorithms, such as Backtracking with Forward Checking (BFC), constraint satisfaction problem (CSP) techniques, Lookahead algorithms, and heuristic-based methods, often face challenges with exponential time complexity, making them less practical for large-scale instances. This paper introduces a novel algorithm, non-sequential conflict resolution (NSCR), which improves performance over traditional algorithms through dynamic conflict resolution. The NSCR algorithm iteratively resolves conflicts among queens by adjusting their positions, aiming to optimize both time complexity and memory usage. While NSCR also operates within exponential time bounds, it demonstrates improved scalability and efficiency compared to traditional methods. A significant strength of the NSCR algorithm lies in its space complexity, which is O(n), and a time complexity that, while typically lower than traditional methods, can reach O(n³) in the worst-case scenario. This linear space complexity is highly advantageous, particularly when dealing with large problem sizes, as it ensures efficient use of memory resources. Comparative analysis with the aforementioned algorithms shows that NSCR offers superior resource management, using up to 60% less memory and reducing runtime by approximately 50%, making it an efficient option for large-scale instances of the N-Queens problem. The algorithm’s performance, evaluated on problem sizes ranging from 8 to 1000 queens, highlights its ability to manage computational resources effectively, despite the inherent challenges of exponential time complexity. Full article

(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)

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19 pages, 1236 KiB

Open AccessArticle

Multi-Task Diffusion Learning for Time Series Classification

by Shaoqiu Zheng, Zhen Liu, Long Tian, Ling Ye, Shixin Zheng, Peng Peng and Wei Chu

Electronics 2024, 13(20), 4015; https://doi.org/10.3390/electronics13204015 - 12 Oct 2024

Viewed by 2242

Abstract

Current deep learning models for time series often face challenges with generalizability in scenarios characterized by limited samples or inadequately labeled data. By tapping into the robust generative capabilities of diffusion models, which have shown success in computer vision and natural language processing, we see potential for improving the adaptability of deep learning models. However, the specific application of diffusion models in generating samples for time series classification tasks remains underexplored. To bridge this gap, we introduce the MDGPS model, which incorporates multi-task diffusion learning and gradient-free patch search (MDGPS). Our methodology aims to bolster the generalizability of time series classification models confronted with restricted labeled samples. The multi-task diffusion learning module integrates frequency-domain classification with random masked patches diffusion learning, leveraging frequency-domain feature representations and patch observation distributions to improve the discriminative properties of generated samples. Furthermore, a gradient-free patch search module, utilizing the particle swarm optimization algorithm, refines time series for specific samples through a pre-trained multi-task diffusion model. This process aims to reduce classification errors caused by random patch masking. The experimental results on four time series datasets show that the proposed MDGPS model consistently surpasses other methods, achieving the highest classification accuracy and F1-score across all datasets: 95.81%, 87.64%, 82.31%, and 100% in accuracy; and 95.21%, 82.32%, 78.57%, and 100% in F1-Score for Epilepsy, FD-B, Gesture, and EMG, respectively. In addition, evaluations in a reinforcement learning scenario confirm MDGPS’s superior performance. Ablation and visualization experiments further validate the effectiveness of its individual components. Full article

(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)

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22 pages, 7227 KiB

Open AccessArticle

Robust Reversible Watermarking Scheme in Video Compression Domain Based on Multi-Layer Embedding

by Yifei Meng, Ke Niu, Yingnan Zhang, Yucheng Liang and Fangmeng Hu

Electronics 2024, 13(18), 3734; https://doi.org/10.3390/electronics13183734 - 20 Sep 2024

Viewed by 1438

Abstract

Most of the existing research on video watermarking schemes focus on improving the robustness of watermarking. However, in application scenarios such as judicial forensics and telemedicine, the distortion caused by watermark embedding on the original video is unacceptable. To solve this problem, this paper proposes a robust reversible watermarking (RRW)scheme based on multi-layer embedding in the video compression domain. Firstly, the watermarking data are divided into several sub-secrets by using Shamir’s (t, n)-threshold secret sharing. After that, the chroma sub-block with more complex texture information is filtered out in the I-frame of each group of pictures (GOP), and the sub-secret is embedded in that frame by modifying the discrete cosine transform (DCT) coefficients within the sub-block. Finally, the auxiliary information required to recover the coefficients is embedded into the motion vector of the P-frame of each GOP by a reversible steganography algorithm. In the absence of an attack, the receiver can recover the DCT coefficients by extracting the auxiliary information in the vectors, ultimately recovering the video correctly. The watermarking scheme demonstrates strong robustness even when it suffers from malicious attacks such as recompression attacks and requantization attacks. The experimental results demonstrate that the watermarking scheme proposed in this paper exhibits reversibility and high visual quality. Moreover, the scheme surpasses other comparable methods in the robustness test session. Full article

(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)

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15 pages, 10699 KiB

Open AccessArticle

Frequency-Auxiliary One-Shot Domain Adaptation of Generative Adversarial Networks

by Kan Cheng, Haidong Liu, Jiayu Liu, Bo Xu and Xinyue Liu

Electronics 2024, 13(13), 2643; https://doi.org/10.3390/electronics13132643 - 5 Jul 2024

Viewed by 1402

Abstract

Generative domain adaptation in a one-shot scenario involves transferring a pretrained generator from one domain to another using only a single reference image. To address the issue of extremely scarce data, existing methods resort to complex parameter constraints and leverage additional semantic knowledge from CLIP models to mitigate it. However, these methods still suffer from overfitting and underfitting issues due to the lack of prior knowledge about the domain adaptation task. In this paper, we firstly introduce the perspective of the frequency domain into the generative domain adaptation task to support the model in understanding the adaptation goals in a one-shot scenario and propose a method called frequency-auxiliary GAN (FAGAN). The FAGAN contains two core modules: a low-frequency fusion module (LFF-Module) and high-frequency guide module (HFG-Module). Specifically, the LFF-Module aims to inherit the domain-sharing information of the source module by fusing the low-frequency features of the source model. In addition, the HFG-Module is designed to select the domain-specific information of the reference image and guide the model to fit them by utilizing high-frequency guidance. These two modules are dedicated to alleviating overfitting and underfitting issues, thereby enchancing the diversity and fidelity of generated images. Extensive experimental results showed that our method leads to better quantitative and qualitative results than the existing methods under a wide range of task settings. Full article

(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)

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14 pages, 398 KiB

Open AccessArticle

FCL: Pedestrian Re-Identification Algorithm Based on Feature Fusion Contrastive Learning

by Yuangang Li, Yuhan Zhang, Yunlong Gao, Bo Xu and Xinyue Liu

Electronics 2024, 13(12), 2368; https://doi.org/10.3390/electronics13122368 - 17 Jun 2024

Cited by 5 | Viewed by 1453

Abstract

Pedestrian re-identification leverages computer vision technology to achieve cross-camera matching of pedestrians; it has recently led to significant progress and presents numerous practical applications. However, current algorithms face the following challenges: (1) most of the methods are supervised, heavily relying on specific datasets, and lacking robust generalization capabilities; (2) it is hard to extract features because the elongated and narrow shape of pedestrian images introduces uneven feature distributions; (3) the substantial imbalance between positive and negative samples. To address these challenges, we introduce a novel pedestrian re-identification unsupervised algorithm called Feature Fusion Contrastive Learning (FCL) to extract more effective features. Specifically, we employ circular pooling to merge network features across different levels for pedestrian re-identification to improve robust generalization capability. Furthermore, we propose a feature fusion pooling method, which facilitates a more efficient distribution of feature representations across pedestrian images. Finally, we introduce FocalLoss to compute the clustering-level loss, mitigating the imbalance between positive and negative samples. Through extensive experiments conducted on three prominent datasets, our proposed method demonstrates promising performance, with an average 3.8% improvement in FCL’s mAP indicators compared to baseline results. Full article

(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)

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14 pages, 1985 KiB

Open AccessArticle

An Improvement of Adam Based on a Cyclic Exponential Decay Learning Rate and Gradient Norm Constraints

by Yichuan Shao, Jiapeng Yang, Wen Zhou, Haijing Sun, Lei Xing, Qian Zhao and Le Zhang

Electronics 2024, 13(9), 1778; https://doi.org/10.3390/electronics13091778 - 4 May 2024

Cited by 6 | Viewed by 2945

Abstract

Aiming at a series of limitations of the Adam algorithm, such as hyperparameter sensitivity and unstable convergence, in this paper, an improved optimization algorithm, the Cycle-Norm-Adam (CN-Adam) algorithm, is proposed. The algorithm integrates the ideas of a cyclic exponential decay learning rate (CEDLR) and gradient paradigm constraintsand accelerates the convergence speed of the Adam model and improves its generalization performance by dynamically adjusting the learning rate. In order to verify the effectiveness of the CN-Adam algorithm, we conducted extensive experimental studies. The CN-Adam algorithm achieved significant performance improvementsin both standard datasets. The experimental results show that the CN-Adam algorithm achieved 98.54% accuracy in the MNIST dataset and 72.10% in the CIFAR10 dataset. Due to the complexity and specificity of medical images, the algorithm was tested in a medical dataset and achieved an accuracy of 78.80%, which was better than the other algorithms. The experimental results show that the CN-Adam optimization algorithm provides an effective optimization strategy for improving model performance and promoting medical research. Full article

(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)

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Review

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25 pages, 1436 KiB

Open AccessReview

Large Language Models for Structured and Semi-Structured Data, Recommender Systems and Knowledge Base Engineering: A Survey of Recent Techniques and Architectures

by Alma Smajić, Ratomir Karlović, Mieta Bobanović Dasko and Ivan Lorencin

Electronics 2025, 14(15), 3153; https://doi.org/10.3390/electronics14153153 - 7 Aug 2025

Viewed by 278

Abstract

Large Language Models (LLMs) are reshaping recommendation systems through enhanced language understanding, reasoning, and integration with structured data. This systematic review analyzes 88 studies published between 2023 and 2025, categorized into three thematic areas: data processing, technical identification, and LLM-based recommendation architectures. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the review highlights key trends such as the use of knowledge graphs, Retrieval-Augmented Generation (RAG), domain-specific fine-tuning, and robustness improvements. Findings reveal that while LLMs significantly advance semantic reasoning and personalization, challenges remain in hallucination mitigation, fairness, and domain adaptation. Technical innovations, including graph-augmented retrieval methods and human-in-the-loop validation, show promise in addressing these limitations. The review also considers the broader macroeconomic implications associated with the deployment of LLM-based systems, particularly as they relate to scalability, labor dynamics, and resource-intensive implementation in real-world recommendation contexts, emphasizing both productivity gains and potential labor market shifts. This work provides a structured overview of current methods and outlines future directions for developing reliable and efficient LLM-based recommendation systems. Full article

(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)

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