Performance Of Algorithm Research Articles

Advancements in Optimization: Critical Analysis of Evolutionary, Swarm, and Behavior-Based Algorithms

The research work on optimization has witnessed significant growth in the past few years, particularly within multi- and single-objective optimization algorithm areas. This study provides a comprehensive overview and critical evaluation of a wide range of optimization algorithms from conventional methods to innovative metaheuristic techniques. The methods used for analysis include bibliometric analysis, keyword analysis, and content analysis, focusing on studies from the period 2000–2023. Databases such as IEEE Xplore, SpringerLink, and ScienceDirect were extensively utilized. Our analysis reveals that while traditional algorithms like evolutionary optimization (EO) and particle swarm optimization (PSO) remain popular, newer methods like the fitness-dependent optimizer (FDO) and learner performance-based behavior (LPBB) are gaining attraction due to their adaptability and efficiency. The main conclusion emphasizes the importance of algorithmic diversity, benchmarking standards, and performance evaluation metrics, highlighting future research paths including the exploration of hybrid algorithms, use of domain-specific knowledge, and addressing scalability issues in multi-objective optimization.

ARQUIN: Architectures for Multinode Superconducting Quantum Computers

Many proposals to scale quantum technology rely on modular or distributed designs wherein individual quantum processors, called nodes, are linked together to form one large multinode quantum computer (MNQC). One scalable method to construct an MNQC is using superconducting quantum systems with optical interconnects. However, internode gates in these systems may be two to three orders of magnitude noisier and slower than local operations. Surmounting the limitations of internode gates will require improvements in entanglement generation, use of entanglement distillation, and optimized software and compilers. Still, it remains unclear what performance is possible with current hardware and what performance algorithms require. In this article, we employ a systems analysis approach to quantify overall MNQC performance in terms of hardware models of internode links, entanglement distillation, and local architecture. We show how to navigate tradeoffs in entanglement generation and distillation in the context of algorithm performance, lay out how compilers and software should balance between local and internode gates, and discuss when noisy quantum internode links have an advantage over purely classical links. We find that a factor of 10–100× better link performance is required and introduce a research roadmap for the co-design of hardware and software towards the realization of early MNQCs. While we focus on superconducting devices with optical interconnects, our approach is general across MNQC implementations.

A genetic algorithm approach for flexible power point tracking in partial shading conditions

A novel Flexible Power Point Tracking (FPPT) algorithm based on Genetic Algorithms (GAs) is presented in this study. The algorithm is designed to effectively handle fluctuating solar irradiation levels and partial shading scenarios. The main objective is to enhance power output and tracking accuracy under dynamic environmental conditions, based on extensive simulations. The results showed superior performance for power output, tracking accuracy, and convergence speed than traditional techniques. Its adaptability to changing environmental conditions renders it suitable for real-time implementation, thereby contributing to efficient power generation in PV systems. Simulations demonstrate the efficacy of the algorithm in maximizing power extraction and meeting grid-code requirements under dynamic conditions. The comparative analyses underscore the superiority of the GA-based FPPT algorithm, highlighting its potential to significantly improve power point tracking in photovoltaic systems. This shows significance of adaptive algorithms in modern PV systems, particularly for reducing the effects of partial shading on energy yield. More research is required on the refinement of the algorithm's performance for long-term reliability and scalability in larger PV installations fostering advancements in photovoltaic power point tracking toward sustainable energy systems.

Data-driven portfolio management for motion pictures industry: A new data-driven optimization methodology using a large language model as the expert

Portfolio management is one of the unresponded problems of the Motion Pictures Industry (MPI). To design an optimal portfolio for an MPI distributor, it is essential to predict the box office of each project. Moreover, for an accurate box office prediction, it is critical to consider the effect of the celebrities involved in each MPI project, which was impossible with any precedent expert-based method. Additionally, the asymmetric characteristic of MPI data decreases the performance of any predictive algorithm. In this paper, firstly, the fame score of the celebrities is determined using a large language model. Then, to tackle the asymmetric character of MPI’s data, projects are classified. Furthermore, the box office prediction takes place for each class of projects. Finally, using a hybrid multi-attribute decision-making technique, the preferability of each project for the distributor is calculated, and benefiting from a bi-objective optimization model, the optimal portfolio is designed. To validate our approach, we conducted experiments using a dataset of movies released in the United States from 1980 to 2020 and employed the proposed approach to predict box office performance. Our results demonstrate that the proposed methodology significantly improves prediction accuracy and provides a robust framework for effective portfolio management.

Port environmental path planning based on key obstacles

This paper proposes an improved hybrid algorithm for automated guided vehicles (AGVs) in port environments based on the concept of key obstacles for the JPS and DWA algorithms. Given the complexity of the port environment and the abundance of obstacles, the traditional heuristic function of the JPS algorithm is improved by adding the key obstacle heuristic function. Simultaneously, improvements are made to the evaluation function of the traditional DWA algorithm, where the braking distance is segmented into key obstacle distance and non-key obstacle distance, utilizing the concept of key obstacles. Simulation experiments are conducted using Matlab to demonstrate the effectiveness of the improved algorithm. Moreover, the performance of the hybrid algorithm is compared with five mainstream algorithms in a real simulated port environment, and the final results show the significant enhancement of this paper’s algorithm in several key performance metrics. Thus, this study provides a feasible strategy for improved path planning efficiency for AGV in the port environment.

Quantum visual feature encoding revisited

Although quantum machine learning has been introduced for a while, its applications in computer vision are still limited. This paper, therefore, revisits the quantum visual encoding strategies, the initial step in quantum machine learning. Investigating the root cause, we uncover that the existing quantum encoding design fails to ensure information preservation of the visual features after the encoding process, thus complicating the learning process of the quantum machine learning models. In particular, the problem, termed the “Quantum Information Gap” (QIG), leads to an information gap between classical and corresponding quantum features. We provide theoretical proof and practical examples with visualization for that found and underscore the significance of QIG, as it directly impacts the performance of quantum machine learning algorithms. To tackle this challenge, we introduce a simple but efficient new loss function named Quantum Information Preserving (QIP) to minimize this gap, resulting in enhanced performance of quantum machine learning algorithms. Extensive experiments validate the effectiveness of our approach, showcasing superior performance compared to current methodologies and consistently achieving state-of-the-art results in quantum modeling.

Multiple elite strategy enhanced RIME algorithm for 3D UAV path planning

With the wave of artificial intelligence sweeping the world in recent years, UAVs is widely used in various fields. UAV path planning has attracted much attention from scientists as an essential part of UAV work. In order to design an efficient and reasonable 3D UAV path planning program, recent researchers have invented and improved many algorithms. This paper proposes an elite RIME algorithm for 3D UAV path planning. First, we propose an elite reverse learning population selection strategy based on piecewise mapping to enhance the population diversity of the algorithm for better exploration. Second, this paper proposes a stochastic factor-controlled elite pool exploration strategy so that the algorithm is difficult to enter the local optimum and can better explore the global optimum. Then, this paper proposes a hard frost puncture exploitation strategy based on the sine–cosine function so that the algorithm can find the global optimum faster during the exploitation process. Meanwhile, in order to test the performance of the algorithm proposed in this paper, we compare it with 13 other intelligent optimization algorithms that are classical and popular nowadays on 52 test functions in three test sets, CEC2017, CEC2020, and CEC2022, and obtain competitive results. Finally, we applied it to the 3D UAV path planning problem in three different terrain scenarios, and the ELRIME algorithm achieved good results in all of them. Especially in the 7-peak model, the ELRIME algorithm improves the performance of the RIME algorithm by a factor of two. In the 9-peak model, the average value aspect also reduce the cost by 91 compared to the RIME algorithm, and more importantly, it has the smallest fluctuation in 30 runs, which is among the most stable of all the compared algorithms. In the 12-peak model, its stability is also significantly enhanced, and in terms of worst-case cost, it improves the cost by 340 compared to RIME.

A TMSBL underwater acoustic channel estimation method based on dictionary learning denoising

The shallow sea underwater acoustic channel exhibits a significant sparse multipath structure. The temporally multiple sparse Bayesian learning (TMSBL) algorithm can effectively estimate this sparse multipath channel. However, the complexity of the algorithm is high, the signal-to-noise ratio (SNR) of shallow-sea underwater acoustic communication is low, and the estimation performance of the TMSBL algorithm is greatly affected by noise. To address this problem, an improved TMSBL underwater acoustic channel estimation method based on a dictionary learning noise reduction algorithm is proposed. Firstly, the K-Singular Value Decomposition (K-SVD) dictionary learning method is used to reduce the noise of the received pilot matrix, reducing the influence of noise on the signal. Then, the Generalized Orthogonal Matching Pursuit (GOMP) channel estimation method is combined to obtain a priori information such as the perceptual matrix and hyperparameter matrix for TMSBL channel estimation; and the noise variance is obtained by using the null subcarrier calculation instead of iteratively updating the noise variance in the TMSBL, to improve the estimation accuracy and reduce the algorithmic complexity. Finally, the TMSBL channel estimation method is used to estimate the underwater acoustic channels of different symbols jointly. The simulation results show that the normalized mean square error of the channel estimation of the improved TMSBL method is reduced by about 92.2% compared with the TMSBL algorithm, obtaining higher estimation accuracy; running time is reduced by about 45.6%, and there is also better performance in terms of the running speed, which provides a reference for underwater acoustic channel estimation.

Development of a Robotic Platform with Autonomous Navigation System for Agriculture

The development of autonomous agricultural robots using a global navigation satellite system aided by real-time kinematics and an inertial measurement unit for position and orientation determination must address the accuracy, reliability, and cost of these components. This study aims to develop and evaluate a robotic platform with autonomous navigation using low-cost components. A navigation algorithm was developed based on the kinematics of a differential vehicle, combined with a proportional and integral steering controller that followed a point-to-point route until the desired route was completed. Two route mapping methods were tested. The performance of the platform control algorithm was evaluated by following a predefined route and calculating metrics such as the maximum cross-track error, mean absolute error, standard deviation of the error, and root mean squared error. The strategy of planning routes with closer waypoints reduces cross-track errors. The results showed that when adopting waypoints every 3 m, better performance was obtained compared to waypoints only at the vertices, with maximum cross-track error being 44.4% lower, MAE 64.1% lower, SD 39.4% lower, and RMSE 52.5% lower. This study demonstrates the feasibility of developing autonomous agricultural robots with low-cost components and highlights the importance of careful route planning to optimize navigation accuracy.

Resource Allocation Based on Task Priority and Resource Consumption in Edge Computing

The computational power of Internet of Things (IoT) devices is usually low, which makes it necessary to process data and extract relevant information on devices with higher processing capacity. Edge Computing emerged as a complementary solution to cloud computing, providing devices at the network edge with computational resources to handle the data processing and analysis that constrained IoT devices eventually cannot perform. This solution allows data processing closer to the IoT devices, reducing latency for IoT applications. However, the resource constraints of edge nodes, which have lower computational power than the cloud nodes, make resource allocation and processing massive requests challenging. This study proposes an edge resource allocation mechanism based on task priority and machine learning. The proposed approach efficiently allocates resources for IoT requests based on their task priorities while monitoring the resource consumption of edge nodes. This study evaluates the performance of different classification algorithms by using well-known metrics for classifying models. The most efficient classifier achieved an accuracy of 92% and a precision of 90%. The results indicate good performance when using this classifier in the evaluated approach. The proposed mechanism demonstrated that resource management can be done more efficiently with significantly lower resource utilization when compared to an allocation method based only on distance. The study tested different scenarios regarding the number of requests, edge nodes, and a proposed failure mechanism to schedule failed node tasks to functional nodes. This failure control mechanism is a significant contribution of the proposal. Therefore, the proposed method in this study can become a valuable tool for efficient resource management with reduced computational cost and efficient resource allocation.

Application of Metaheuristic algorithm in intelligent logistics scheduling and environmental sustainability

In response to the problems of low efficiency, high cost, and serious environmental pollution faced by traditional logistics scheduling methods, this article introduced the Metaheuristic algorithm into intelligent logistics scheduling and environmentally sustainable development. This article took the Metaheuristic algorithm as the research object. It was based on an in-depth analysis of its core ideas and unique advantages, combined intelligent logistics scheduling with relevant theories and methods such as green environmental protection, and innovatively constructed an intelligent logistics scheduling model based on the Metaheuristic algorithm. This article experimentally compared the effects of different Metaheuristic algorithms on total driving distance, transportation time, fuel consumption, and carbon emissions. The experimental findings indicated that the ant colony optimization (ACO) algorithm in this article performed the best among them, and the performance of traditional algorithms and Metaheuristic algorithms was also tested in terms of performance. The findings indicated that the computational accuracy of the Metaheuristic algorithm reached 97%, which was better than the traditional 80%. Experimental results have shown that the Metaheuristic algorithm is an efficient and feasible method that can improve the efficiency of logistics scheduling and environmental sustainability.

An optimized Transformer algorithm: Implementation for intelligent English translation

There is an increasing demand for high-quality translations in the realm of intelligent English translation. This paper optimized the traditional Transformer algorithm by enhancing position coding and the softmax layer. Bidirectional long short-term memory (BiLST) was employed to realize position encoding, capturing both contextual and positional information simultaneously. Additionally, the softmax function was replaced with the sparsemax function to obtain sparser results. The translation performance of some algorithms on Chinese and English datasets was compared and analyzed. It was found that that the optimized Transformer algorithm performed better than the RNNSearch, ConvS2S, and Transformer-base algorithms in terms of bilingual evaluation understudy (BLEU) score on the test set. It achieved an average BLEU score of 23.72, representing an improvement of 1.56 over the RNNSearch algorithm, 1.17 over the ConvS2S algorithm, and 0.73 over the Transformer-base algorithm. The parameter quantity of the optimized algorithm was 6.83 M, which was 0.05 M higher than the Transformer-base algorithm. Furthermore, its running time was 4,862.76 s, showing a marginal increase of 0.21% compared to the Transformer-base algorithm. These findings validate the reliability of the optimized Transformer algorithm for intelligent English translation and its potential practical application.

Training neural networks with structured noise improves classification and generalization

Abstract The beneficial role of noise-injection in learning is a consolidated concept in the field of artificial neural networks, suggesting that even biological systems might take advantage of similar mechanisms to optimize their performance. The training-with-noise algorithm proposed by Gardner and collaborators is an emblematic example of a noise-injection procedure in recurrent networks, which can be used to model biological neural systems. We show how adding structure to noisy training data can substantially improve the algorithm performance, allowing the network to approach perfect retrieval of the memories and wide basins of attraction, even in the scenario of maximal injected noise. We also prove that the so-called Hebbian Unlearning rule coincides with the training-with-noise algorithm when noise is maximal and data are stable fixed points of the network dynamics.

A Giza Pyramids Construction metaheuristic approach based on upper bound calculation for solving the network reliability problem

Network reliability optimization is an optimization problem that focuses on finding an optimal solution for a reliable network design. In network reliability optimization, the goal is to maximize network reliability so that the overall cost of the network is reduced at the same time. The discussion of the reliability of various systems in the field of industry and engineering is of great importance, that's why reliability optimization has received a lot of attention in recent decades. Since this problem is included in the category of NP-Hard problems, the use of soft computing methods will be highly effective in solving it. In this paper, an approach based on the Giza Pyramids Construction (GPC) metaheuristic algorithm is proposed to solve the network reliability problem. For this purpose, two independent single objective functions with constraints are defined, and then the reliability of the network is calculated through optimistic estimation using the upper bound method. In order to compare the performance, 12 types of diverse and complex network models have been generated and the proposed algorithm has been compared with 10 popular and state-of-the-art algorithms. Statistical analysis has been used to find significant differences in the performance of algorithms. Also, a real model of the university network has been generated, investigated, and solved as a case study. The results of experiments, statistical analysis, and observations show that the proposed algorithm has a better performance than other metaheuristic algorithms and the proposed approach in solving reliability is an effective and low-cost approach.

A novel algorithm combined X-ray fluorescence and Neural Network (XRF-NN) for coal ash content prediction: Algorithm design and performance evaluation

This paper investigated a precise algorithm combining X-ray Fluorescence and Neural Network (XRF-NN) for predicting ash content. The 261 sets of XRF tests show that the 34 elements in the chosen Guqiao coal can be categorized as major, secondary and tiny elements, whose cumulative ratio were ~95%, 4-5% and <1%, respectively. Referring to the machine learning theory, the construction strategy of Element-Ash dataset was determined viz. value determination → standardization → division → optimization of generalization ability. Then, the hyperparameters optimization displays that XRF-NN model with 34 inputs and 1 output was suitable to predict coal ash content, where the activation function, loss function, and optimizer were ReLU, MAE, and Momentum, respectively. After iterative training, the new XRF-NN model provides the precise prediction of coal ash content with the absolute errors between -2.0% and 2%. Moreover, the prediction accuracy rose from 57.69% to 100%, as the expected relative error increased from 1% to 5%. Furthermore, the comparisons between different prediction methods reveal that the minimum MRE of 1.22% can be obtained by XRF-NN with total elements, which was only half of those given by the conventional Multiple Linear Regression and Partial Least Squares Regression. Besides, XRF-NN model presents the root mean squared error 0.797%, the mean absolute error 0.625% and the coefficient of determination 0.999, which were significantly superior to those calculated by Dual-Energy Gamma-ray Transmission, Ploy, RFR, XGBoost and DNN model. The results of this study suggest the excellent performance of the new XRF-NN model in predicting ash content.

Enhancing Cybersecurity in Healthcare: Evaluating Ensemble Learning Models for Intrusion Detection in the Internet of Medical Things.

This study investigates the efficacy of machine learning models for intrusion detection in the Internet of Medical Things, aiming to enhance cybersecurity defenses and protect sensitive healthcare data. The analysis focuses on evaluating the performance of ensemble learning algorithms, specifically Stacking, Bagging, and Boosting, using Random Forest and Support Vector Machines as base models on the WUSTL-EHMS-2020 dataset. Through a comprehensive examination of performance metrics such as accuracy, precision, recall, and F1-score, Stacking demonstrates exceptional accuracy and reliability in detecting and classifying cyber attack incidents with an accuracy rate of 98.88%. Bagging is ranked second, with an accuracy rate of 97.83%, while Boosting yielded the lowest accuracy rate of 88.68%.

Dense pedestrian face detection in complex environments

To address the problem of dense crowd face detection in complex environments, this paper proposes a face detection model named Deep and Compact Face Detection (DCFD), which adopts an improved lightweight EfficientNetV2 network to replace the backbone network of RetinaFace. A large kernel attention mechanism is introduced to address the face detection task more accurately. The backbone network, an improved efficient channel attention (ECA) mechanism, is added to further improve the algorithm performance. The feature fusion module is an improved neural architecture search feature pyramid network (NAS-FPN) that significantly improves the face detection accuracy in different scenes. To balance the training process of positive and negative samples, we use the focus loss function to replace the traditional cross-entropy loss function. In different environments, the DCFD algorithm has shown efficient face detection performance. This algorithm provides not only a feasible and effective solution for solving the problem of face detection in dense groups but also an important basis for improving the accuracy of face detection models in practical applications.

Resonant-mode metasurface thermal super mirror by deep learning-assisted optimization algorithms

A “super-mirror” having ultrahigh infrared reflectance is achieved by an optimized photonic contrast grating metasurface. Finding ways to achieve this exceptional performance can be enabled by implementing global optimization and machine learning elements, such as Bayesian optimization and genetic algorithm. Here, we acquired an optimized grating design made of high-index germanium, which excites resonances that result in ultralow emittance at certain wavelengths. Our optimizations assisted in the discovery of hybridized coupling of Fabry-Pérot modes and guided modes in a monolithic microscale multilayered coating. We demonstrate constraints in the given geometric variable ranges improves the overall performance of algorithms. We also show the enhanced performance of a deep learning Feedforward Neural Network, which is implemented as the inverse design using the network trained with dataset obtained from Bayesian optimization and Genetic Algorithm approaches. The performance of the Feedforward Neural Network-assisted design produced normal emissivity difference by only +3.5 %, with lower sensitivity to grating dimensional parameter variations. The improvement is achieved by predicting and better understanding of the optical physics of resonant gratings. The proposed few-layer grating coating can be applied to space components, enclosures, and vessels to suppress thermal radiative heat loss.

Comparative performance of pharmacogenetics-based warfarin dosing algorithms in Chinese population: use of a pharmacokinetic/pharmacodynamic model to explore dosing regimen through clinical trial simulation.

Warfarin has a narrow therapeutic window and large variability in dosing that are affected by clinical and genetic factors. To help guide the dosing of warfarin, the Clinical Pharmacogenetics Implementation Consortium has recommended the use of pharmacogenetic algorithms, such as the ones developed by the International Warfarin Pharmacogenetics Consortium (IWPC) and by Gage et al. when genotype information is available. In this study, simulations were performed in Chinese cohorts to explore how dosing differences between Western (by IWPC and Gage et al.) and Chinese algorithms (by Miao et al.) would mean in terms of anticoagulation effect in clinical trials. We first tried to replicate a published clinical trial comparing genotype-guided dosing to routine clinical dosing in Chinese patients. We then made simulations where Chinese cohorts received daily doses recommended by Gage, IWPC, and Miao algorithms. We found that in simulation conditions where dosing specifications were strictly followed, genotype-guided dosing by IWPC and Lenzini formulae was more likely to overshoot the upper limit of the therapeutic window by day 15, and thus may have a lower % time in therapeutic range (%TTR) than that of clinical dosing group. Also, in comparing Gage, IWPC, and Miao algorithms, we found that the Miao dosing cohort has the highest %TTR and the lowest risk of over-anticoagulation by day 28. In summary, our results confirmed that algorithms developed based on data from local patients may be more suitable for achieving therapeutic international normalized ratio window in Chinese population.

Accuracy of Speech Sound Analysis: Comparison of an Automatic Artificial Intelligence Algorithm With Clinician Assessment.

Automatic speech analysis (ASA) and automatic speech recognition systems are increasingly being used in the treatment of speech sound disorders (SSDs). When utilized as a home practice tool or in the absence of the clinician, the ASA system has the potential to facilitate treatment gains. However, the feedback accuracy of such systems varies, a factor that may impact these gains. The current research analyzes the feedback accuracy of a novel ASA algorithm (Amplio Learning Technologies), in comparison to clinician judgments. A total of 3,584 consonant stimuli, produced by 395 American English-speaking children and adolescents with SSDs (age range: 4-18 years), were analyzed with respect to automatic classification of the ASA algorithm, clinician-ASA agreement, and interclinician agreement. Further analysis of results as related to phoneme acquisition categories (early-, middle-, and late-acquired phonemes) was conducted. Agreement between clinicians and ASA classification for sounds produced accurately was above 80% for all phonemes, with some variation based on phoneme acquisition category (early, middle, late). This variation was also noted for ASA classification into "acceptable," "unacceptable," and "unknown" (which means no determination of phoneme accuracy) categories, as well as interclinician agreement. Clinician-ASA agreement was reduced for misarticulated sounds. The initial findings of Amplio's novel algorithm are promising for its potential use within the context of home practice, as it demonstrates high feedback accuracy for correctly produced sounds. Furthermore, complexity of sound influences consistency of perception, both by clinicians and by automated platforms, indicating variable performance of the ASA algorithm across phonemes. Taken together, the ASA algorithm may be effective in facilitating speech sound practice for children with SSDs, even in the absence of the clinician.