Optimization Techniques Research Articles

Optimization of the PVT performance with various orientations of jets and MFFNN-RSA prediction model for smart buildings

The combined thermal and photovoltaic technology in PV/T systems is considered as a greatly promising technology for smart buildings. Thus, investigations for enhancing the PV/T performance are still proceeding. This research presents an investigation for novel configurations of cooling jets for the PVT system. The linear and circular distribution for the inlet jets considering regular and irregular positioning for all the jets as new cooling configurations are implemented. Moreover, the proposed geometrical configurations are optemized regarding the performance to identify the most suitable configuration that achieves the optimum efficiency and temperature. Furthermore, a novel hybrid ANN model is presented for predicting the performance of the PVT systems. This model combines the multi-feedforward neural network (MFFNN) with an optimization technique called reptile search algorithm (RSA). The proposed model can process the studied parameters to predict the PVT performance parameters (top surface temperature, temperature un-uniformity, outlet temperature, and efficiencies). The proposed MFFNN-RSA model minimized the mean square error to less than 0.4857×10-3. The maximum temperature decrease achieved by the presented configuration reached 60.62K compared to the uncooled case, while the minimum temperature un-uniformity reached 1K and 6K for 400 and 1000 W/m2, respectively. The increase of the ambient temperature found to decrease the temperature un-uniformity in all the cases. The irregular jet with the linear distribution was found to achieve the optimum performance of the overall, thermal, and electrical efficiencies of 63.5%, 49.6%, and 14.25%, respectively. Furthermore, the electricity production cost was reduced by 11.6%, and the yearly CO2 emissions were reduced by 215.3 kg/m2 compared to the normal PV system. The proposed irregular-line distribution of the jets is found to be the best configuration regarding the temperature of the PV model and the overall efficiency considering the pumping losses.

Multi-layer perceptron hyperparameter optimization using Jaya algorithm for disease classification

This study introduces an innovative hyperparameter optimization approach for enhancing multilayer perceptrons (MLP) using the Jaya algorithm. Addressing the crucial role of hyperparameter tuning in MLP’s performance, the Jaya algorithm, inspired by social behavior, emerges as a promising optimization technique without algorithm-specific parameters. Systematic application of Jaya dynamically adjusts hyperparameter values, leading to notable improvements in convergence speeds and model generalization. Quantitatively, the Jaya algorithm consistently achieves convergences at first iteration, faster convergence compared to conventional methods, resulting in 7% higher accuracy levels on several datasets. This research contributes to hyperparameter optimization, offering a practical and effective solution for optimizing MLP in diverse applications, with implications for improved computational efficiency and model performance.

Multilevel Thresholding-based Medical Image Segmentation using Hybrid Particle Cuckoo Swarm Optimization

Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work. Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm. Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency. Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field.

Techno-economic optimization and working fluid selection of a biogas-based dual-loop bi-evaporator ejector cooling cycle involving power-to-hydrogen and water facilities

Biogas-fueled decentralized energy systems featuring gas turbines emerge as pivotal players in the quest for more adaptable and eco-friendly energy solutions. This study presents the design of a cutting-edge multigeneration system that harnesses the potential of a gas turbine coupled with ejector-driven dual-loop bi-evaporator technology, reverse osmosis desalination, proton exchange membrane electrolysis, and an organic Rankine cycle. The research encompasses an extensive sensitivity analysis and deploys a genetic algorithm-based optimization technique. Through meticulous Optimization, the system achieves remarkable outcomes, including electricity generation, cooling capacity, heating capacity, desalinated water production, and hydrogen yield, measured at 957.3 kW, 231.4 kW, 272.3 kW, 7.336 kg/s, and 0.99 kg/h, respectively. Notably, this performance surpasses the base case by 2% and 8.9%, yielding exergy efficiency and total unit cost improvements of 33.3% and 16.4 $/GJ. Among the critical decisions made was selecting an organic working fluid for the organic Rankine cycle. Through rigorous evaluation, isobutene emerged as the optimal choice, demonstrating significantly improved output power compared to alternatives. Isobutane as the working fluid led to a substantial increase in overall exergy efficiency and a resultant total unit cost of 18.06 $/GJ, accompanied by an impressive 23.48% exergy efficiency.

Enhancing Energy Efficiency in Telehealth IoT through MultiObjective Optimization on a Hybrid Fog/Cloud Computing Platform

In the rapidly evolving field of Telehealth Internet of Things (IoT), the pursuit of energy-efficient solutions that coexist with optimal system performance is a critical concern. This paper introduces a novel approach to address this challenge by integrating multi-objective optimization techniques within a hybrid fog/ cloud computing platform. Building upon established research on a fog-based telehealth model, this study extends its investigation to encompass a broader spectrum of performance metrics, including energy efficiency, response time, throughput, and resource utilization. The study employs well-established multiobjective optimization algorithms, specifically NSGA-II (Non-dominated Sorting Genetic Algorithm II) and SPEA2 (Strength Pareto Evolutionary Algorithm 2), to construct a comprehensive optimization framework. An intricate objective function is meticulously formulated to quantify the trade-offs between energy efficiency and other key performance metrics, facilitating the identification of Pareto-optimal solutions. The resulting Pareto front offers illuminating insights into the nuanced interplay between energy efficiency and performance attributes, providing decision-makers with tailored options that cater to their specific priorities. Rigorous evaluation of these solutions through simulated experiments reveals a harmonious landscape where energy-saving imperatives coalesce harmoniously with response time, throughput, and resource utilization goals. The implications of this multi-objective optimization approach are analyzed in depth, underscoring its potential to reshape optimization paradigms for Telehealth IoT deployments within a fog/cloud hybrid platform. This research represents a pioneering stride towards reconciling energy efficiency and performance in Telehealth IoT systems, offering a nuanced perspective for informed decision-making and a sustainable future for energy-saving initiatives in Telehealth IoT applications

Application of Scheffe's Simplex Lattice Model in concrete mixture design and performance enhancement

This comprehensive literature review delves into the application of Scheffe's Simplex Lattice Model for optimizing cement concrete mixtures, with a particular emphasis on its impact on material properties and sustainability. The review meticulously outlines the principles, historical context, and advantages of Scheffe's model, providing a nuanced understanding of its significance. Comparative analyses with traditional and alternative optimization techniques in concrete mix design illuminate the distinct advantages of statistical methods, especially Scheffe's model. The review critically examines the challenges and limitations associated with applying Scheffe's model, addressing issues related to the complexity of concrete mixtures and computational demands. Potential avenues for improvement are explored, suggesting refinements to handle non-linearity, incorporate advanced optimization algorithms, and streamline computational requirements. Additionally, the review highlights emerging trends in statistical modeling for concrete mixture optimization, such as the integration of machine learning and data-driven approaches, signaling the evolving landscape of concrete technology. In conclusion, the literature underscores Scheffe's Simplex Lattice Model as a valuable and versatile tool with far-reaching implications for the advancement of concrete mixture design methodologies. The call to action encourages ongoing research and development to refine the model, explore emerging trends, and address practical challenges, positioning Scheffe's model as a cornerstone in the pursuit of sustainable, resilient, and high-performance concrete materials. The study highlights Scheffe's Simplex Lattice Model as a robust statistical tool for optimizing cement concrete mixtures, demonstrating its efficacy in improving performance indicators like compressive strength and durability. This systematic approach offers a paradigm shift from empirical methods, fostering a nuanced understanding of concrete components' relationships and paving the way for tailored material properties and enhanced sustainability in construction. The call to action urges further research to refine the model, address challenges, and explore hybrid approaches, ultimately advancing concrete mixture design methodologies towards greater efficiency and precision.

Inverse designed WS2 planar chiral metasurface with geometric phase

Increasing attention is being paid to chiral metasurfaces due to their ability to selectively manipulate right-hand circularly polarized light or left-hand circularly polarized light. The thin nature of metasurfaces, however, poses a challenge in creating a device with effective phase modulation. Plasmonic chiral metasurfaces have attempted to address this issue by increasing light–matter interaction, but they suffer from metallic loss. Dielectric metasurfaces made from high-index materials enable phase modulation while being thin. Very few materials, however, have high refractive index and low loss at visible wavelengths. Recently, some 2D materials have been shown to exhibit high refractive index and low loss in the visible wavelengths, positioning them as promising platforms for meta-optics. This study introduces and details a planar chiral metasurface with a geometric phase composed of WS2 meta-units. By employing adjoint optimization techniques, we achieved broadband circular dichroism ( > 0.5 in the wavelength range of 653–796 nm) and a high extinction ratio (19.6 dB at λ = 675 nm).

AdaBelief Optimizasyon Tekniğinin Derin Öğrenmeye Dayalı Yaya Rotası Tahmin Uygulamalarına Etkisinin “Yakınsama” açısından İncelenmesi

Son yıllarda, görüntü işleme teknikleri kullanılarak yayaların takip edebileceği rotanın tahmini, hızla dikkat çeken bir araştırma konusu haline gelmiştir. Rota tahmin uygulamalarında Derin Öğrenmenin kullanımı, mühendislik çalışmalarıyla yapılan geleneksel parametre belirleme işlemlerine ihtiyaç duymayan ve daha doğru tahminler yapabilen yeni uygulamaların geliştirilmesini sağlamıştır. Rota tahmini için sıklıkla veriye dayalı olan gözetimli derin öğrenme modelleri kullanılmaktadır. Ancak, bu modellerin eğitimi yüksek hesaplama maliyeti getirmektedir. Bu maliyetleri azaltmak ve doğrulukları arttırmak için iyi yakınsama ve genelleştirme özelliklerine sahip optimizasyon yöntemlerini seçmek önemlidir. Bu çalışma, ETH/UCY veri kümeleri kullanılarak derin öğrenme mimarisi temelli geliştirilmiş rota tahmini algoritmalarının optimizasyon yöntemi açısından performansını incelemektedir. Özellikle, modelin eğitimi aşamasında yakınsama açısından AdaBelief optimizasyon tekniğinin avantajları ve dezavantajlarına odaklanılmaktadır. Çalışmanın sonuçları, AdaBelief’in eğitim sürecine pozitif bir katkıda bulunduğunu ve rota tahmini algoritmalarının genel performansını arttırabileceğini göstermektedir.

OptiCharge: A firefly algorithm-based approach for minimizing electric vehicle waiting time at charging stations

The use of electric vehicles has raised need for infrastructure for effective charging. Wait periods at charging stations are a problem for both owners and operators of electric vehicles. Owners of electric vehicles are irritated by lengthy wait periods, and charging facilities are underutilised. Wait times at electric car charging stations are decreased using “OptiCharge,” a Firefly Algorithm-based solution. Scheduling charging stations makes sense given the Firefly Algorithm’s ability to adjust to changing conditions and solve challenging problems. In the present paper, we incorporate dynamic scheduling and waiting time computation mathematically into OptiCharge. Extensive testing and comparative analysis with different optimisation techniques demonstrate that OptiCharge decreases waiting times and enhances charging station performance. The results show how OptiCharge may enhance EV charging and promote intelligent, sustainable transportation.

Development of optimised software fault prediction model using machine learning

Software fault prediction is a crucial task, especially with the rapid improvements in software technology and increasing complexity of software. As identifying and addressing bugs early in the development process can significantly minimize the costs and enhance the software quality. Software fault prediction using machine learning algorithms has gained significant attention due to its potential to improve software quality and save time in the testing phase. This research paper investigates the impact of classification models on bug prediction performance and explores the use of bio-inspired optimization techniques to enhance model results. Through experiments, it is demonstrated that applying bio-inspired algorithms improves the accuracy of fault prediction models. The evaluation is based on multiple performance metrics and the results show that KNN with BACO (Binary Ant Colony Optimization) generally outperform the other models in terms of accuracy. The BACO-KNN fault prediction model attains the accuracy of 96.39% surpassing the previous work.

Brain image segmentation with fuzzy entropy clustering and PSO-GWO optimization techniques

In the field of brain MRI analysis, image segmentation serves various purposes such as quantifying and visualizing anatomical structures, analyzing brain changes, delineating pathological regions, and aiding in surgical planning and image-guided interventions. Over the past few decades, diverse segmentation techniques with varying degrees of accuracy and complexity have been developed. Real-world brain MRI images often encounter intensity in homogeneity, posing a significant challenge in accurate segmentation. The prevailing image segmentation algorithms, predominantly region-based, typically rely on the homogeneity of image intensities in specific regions of interest. However, these methods often fall short of providing precise segmentation results due to intensity in homogeneity. To address these challenges and enhance segmentation performance, this paper introduce a novel objective function named Fuzzy Entropy Clustering with Local Spatial Information and Bias Correction (FECSB). Additionally, we propose a novel hybrid algorithm that combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to maximize the effectiveness of the FECSB function in MRI brain image segmentation. The proposed algorithm undergoes rigorous evaluation using benchmark MRI brain images, including those from the McConnell Brain Imaging Center (BrainWeb). The experimental results unequivocally demonstrate the superiority of the PSO-GWO clustering method over the traditional Fuzzy C Means (FCM) method. Across various image slices, the PSO-GWO method consistently outperforms FCM in terms of accuracy, showing improvements ranging from 1.28% to 1.46%, approximately achieving 99.37% accuracy.

Enhancing 3D-Printed PP-CF parts: A novel resin-filling technique for mechanical property optimization

This study investigates a novel post-processing technique aimed at enhancing the mechanical properties of 3D-printed polypropylene-carbon fiber (PP-CF) composite parts. The method involves printing components with internal voids to reduce weight and printing time, subsequently filling these voids with a low-cost resin known for its superior mechanical properties. Through systematic experimentation varying infill density and pattern, key quantitative findings were obtained. Tensile strength generally increased with higher infill density, reaching a maximum of 55.664 MPa for the resin-filled triangle infill pattern with 60% infill density. Impact energy showed a decreasing trend with increasing infill density, with the highest impact energy of 0.5 J recorded for the resin-filled triangle infill pattern with 60% infill density. Microstructural analysis revealed that the triangle infill pattern at 60% infill density exhibited the most effective resin penetration, contributing to superior mechanical performance. These findings emphasize the importance of infill pattern selection in resin distribution and mechanical enhancement in 3D-printed composite materials.

Variance-Reduced Stochastic Optimization for Efficient Inference of Hidden Markov Models

Hidden Markov models (HMMs) are popular models to identify a finite number of latent states from sequential data. However, fitting them to large datasets can be computationally demanding because most likelihood maximization techniques require iterating through the entire underlying dataset for every parameter update. We propose a novel optimization algorithm that updates the parameters of an HMM without iterating through the entire dataset. Namely, we combine a partial E step with variance-reduced stochastic optimization within the M step. We prove the algorithm converges under certain regularity conditions. We test our algorithm empirically using a simulation study as well as a case study of kinematic data collected using suction-cup attached biologgers from eight northern resident killer whales (Orcinus orca) off the western coast of Canada. In both, our algorithm converges in fewer epochs, with less computation time, and to regions of higher likelihood compared to standard numerical optimization techniques. Our algorithm allows practitioners to fit complicated HMMs to large time-series datasets more efficiently than existing baselines. Supplemental materials are available online.

Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers

This work presents the PORTALS framework (Rodriguez-Fernandez et al 2022 Nucl. Fusion 62 076036), which leverages surrogate modeling and optimization techniques to enable the prediction of core plasma profiles and performance with nonlinear gyrokinetic simulations at significantly reduced cost, with no loss of accuracy. The efficiency of PORTALS is benchmarked against standard methods, and its full potential is demonstrated on a unique, simultaneous 5-channel (electron temperature, ion temperature, electron density, impurity density and angular rotation) prediction of steady-state profiles in a DIII-D ITER Similar Shape plasma with GPU-accelerated, nonlinear CGYRO (Candy et al 2016 J. Comput. Phys. 324 73–93). This paper also provides general guidelines for accurate performance predictions in burning plasmas and the impact of transport modeling in fusion pilot plants studies.

Adaptive RAO ensembled dichotomy technique for the accurate parameters extraction of solar PV system

The parameter extraction process for PV models poses a complex nonlinear and multi-model optimization challenge. Accurately estimating these parameters is crucial for optimizing the efficiency of PV systems. To address this, the paper introduces the Adaptive Rao Dichotomy Method (ARDM) which leverages the adaptive characteristics of the Rao algorithm and the Dichotomy Technique. ARDM is compared with the several recent optimization techniques, including the tuna swarm optimizer, African vulture’s optimizer, and teaching–learning-based optimizer. Statistical analyses and experimental results demonstrate the ARDM's superior performance in the parameter extraction for the various PV models, such as RTC France and PWP 201 polycrystalline, utilizing manufacturer-provided datasheets. Comparisons with competing techniques further underscore ARDM dominance. Simulation results highlight ARDM quick processing time, steady convergence, and consistently high accuracy in delivering optimal solutions.

Machine learning assisted discovery of effective viscous material laws for shear-thinning fiber suspensions

AbstractIn this article, we combine a Fast Fourier Transform based computational approach and a supervised machine learning strategy to discover models for the anisotropic effective viscosity of shear-thinning fiber suspensions. Using the Fast Fourier Transform based computational approach, we study the effects of the fiber orientation state and the imposed macroscopic shear rate tensor on the effective viscosity for a broad range of shear rates of engineering process interest. We visualize the effective viscosity in three dimensions and find that the anisotropy of the effective viscosity and its shear rate dependence vary strongly with the fiber orientation state. Combining the results of this work with insights from literature, we formulate four requirements a model of the effective viscosity should satisfy for shear-thinning fiber suspensions with a Cross-type matrix fluid. Furthermore, we introduce four model candidates with differing numbers of parameters and different theoretical motivations, and use supervised machine learning techniques for non-convex optimization to identify parameter sets for the model candidates. By doing so, we leverage the flexibility of automatic differentiation and the robustness of gradient based, supervised machine learning. Finally, we identify the most suitable model by comparing the prediction accuracy of the model candidates on the fiber orientation triangle, and find that multiple models predict the anisotropic shear-thinning behavior to engineering accuracy over a broad range of shear rates.

What can go wrong when doing right? A pictorial review of iatrogenic genitourinary complications.

A growing number of treatments for genitourinary diseases can result in various iatrogenic complications. Multimodality imaging in the post-procedural setting is essential for early and accurate diagnosis to limit morbidity and mortality. We review common and uncommon treatment-induced pathologies affecting the genitourinary system via a case-based approach. We illustrate notable complications affecting the kidneys, ureters, bladder, and urethra induced by percutaneous procedures, external beam radiation, immunotherapy, laparoscopic/robotic pelvic surgery, and intravesicular BCG. Finally, we provide guidance on optimal imaging techniques for diagnosis and highlight the role of image-guided interventions for mitigation of complications.

Enhanced Recovery Protocol Versus Conventional Care in Patients Undergoing Esophagectomy for Cancer: Advantages in Clinical and Patient-Reported Outcomes.

This study was designed to compare the clinical and patient-reported outcomes (PROs) between the enhanced recovery after surgery (ERAS) protocol and conventional care in patients undergoing esophagectomy for cancer, which have not previously been compared. This single-center retrospective study included prospective PRO data from August 2019 to June 2021. Clinical outcomes included perioperative complications and postoperative length of stay (PLOS). Patient-reported outcomes were assessed by using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core-30 (QLQ-C30) and esophagus-specific module (QLQ-OES18) preoperatively to 6 months postoperatively. Mixed-effects models were used to longitudinally compare quality of life (QOL) scores between the two modes. Patients undergoing conventional care and ERAS were analyzed (n = 348 and 109, respectively). The ERAS group had fewer overall complications, pneumonia, arrhythmia, and a shorter PLOS than the conventional group, and outperformed the conventional group in five functional QLQ-C30 domains and five symptom QLQ-OES18 domains, including less dysphagia (p < 0.0001), trouble talking (p = 0.0006), and better eating (p < 0.0001). These advantages persisted for 3 months postoperatively. For the cervical circular stapled anastomosis, the initial domains and duration of benefit were reduced in the ERAS group. The ERAS protocol has significant advantages over conventional care in terms of clinical outcomes, lowering postoperative symptom burden, and improving functional QOL in patients who have undergone esophagectomy. Selection of the optimal technique for cervical anastomosis is a key operative component of ERAS that maintains the symptom domains and duration of the advantages of PROs.

Enhancing RAG Systems: A Survey of Optimization Strategies for Performance and Scalability

Retrieval Augmented Generation (RAG) systems offer significant advancements in natural language processing by combining large language models (LLMs) with external knowledge sources to improve factual accuracy and contextual relevance. However, the computational complexity of RAG pipelines presents challenges in terms of efficiency and scalability. This research paper conducts a comprehensive survey of optimization techniques across four key areas: tokenizer performance, encoder performance, vector database search strategies, and LLM agent integra- tion. The study explores approaches to accelerate tokenization using Rust-based implementations and investigates hardware optimizations like CUDA and Ten- sor cores to boost encoder efficiency. Additionally, it delves into algorithms and indexing strategies for efficient vector database searches and examines meth- ods for optimising the interaction between retrieved knowledge and LLM agents. By analysing recent research and evaluating various optimization strategies, this paper aims to provide valuable insights into enhancing the performance and practicality of RAG systems for real-world applications. Keywords: Retrieval-Augmented Generation, Performance optimization, Tokenization, WordPiece, Rust, FastTokenizer,Encoding, GPU, CUDA, Tensor Cores, Parallel Processing, Vector Databases, Indexing, HNSW, FAISS, DiskANN, LLM Agents, Prompt Engineering, Retriever

DYNAMIC FIREWALL CONFIGURATION FOR VIRTUAL NETWORKS: A COMPREHENSIVE AUTOMATION FRAMEWORK

In recent years, segment routing has emerged as a revolutionary traffic engineering architecture. Segment routing, however, results in control overheads since more packet headers need to be included. When segment headers get excessively long, the overheads can significantly lower the forwarding efficiency for a big network. We suggest the intelligent routing scheme for traffic engineering (IRTE), which can provide load balancing with minimal control overheads, in order to meet the better of two goals. We first frame the problem as a mapping problem that maps various flows to important diversion sites in order to obtain optimal performance. Second, by reducing the problem to a k-dense subgraph problem, we demonstrate that it is nondeterministic polynomial (NP)-hard. We create improved ant colony optimisation (IACO), a popular ant colony optimisation technique for network optimisation problems, to address this issue. We also develop and examine the theoretical performance of the load balancing algorithm with diversion routing (LBA-DR). Ultimately, we test the IRTE in various real-world topologies, and the findings demonstrate that the IRTE works better than previous algorithms. For example, while testing on Bell Canada topology, the maximum bandwidth is 24.6% less than that of traditional algorithms. Keywords: Segment Routing, Traffic Engineering, Control Overheads, Packet Headers, Forwarding Efficiency, Intelligent Routing Scheme.