Optimization Techniques Research Articles

Retraction Note: Crime activities prediction using hybridization of firefly optimization technique and fuzzy cognitive map neural networks

Retraction Note: Crime activities prediction using hybridization of firefly optimization technique and fuzzy cognitive map neural networks

Advanced Method Optimization for Sampling and Analysis Instrumentation.

This work presents a generalized approach for analytical method optimization that branches the gap between techniques historically employed and accurate modern optimization techniques suitable for various applications. The novelty of the described strategy is the utilization of multivariate, multiobjective optimization with Karush-Kuhn-Tucker conditions to bound the optimization space to solutions within the physical limitations of instrumentation. Briefly, the basic steps outlined in this paper are to (1) determine the objective(s) that should be maximized or minimized based on the goals of the analytical application, (2) conduct a screening experiment, (3) perform ANOVA to determine the parameters which have a statistically significant effect on the objective, (4) conduct an experiment (e.g., Box-Behnken design) to collect data for fitting the objective equation, and (5) determine the physical constraints of the parameters and solve the Lagrangian to determine the optimal method parameters. A broad approach to optimization target selection allows for robust method tuning to develop improved data sets amenable for chemometrics and machine learning algorithm development. Gas chromatography-mass spectrometry was selected as a use case due to its broad use across scientific fields and time-consuming method development involving numerous parameters. This strategy can reduce the cost of research, improve data quality, and enable the rapid development of new analytical techniques.

Establishing a unified viscoplastic constitutive equation for EA4T steel: Comparative analysis with Arrhenius model

Quasi-static and impact compression experiments were conducted on EA4T steel using a Gleeble-3800 thermal simulation machine. The study aimed to investigate the complex microstructural evolution and thermal deformation behavior of EA4T steel under various experimental conditions, including temperatures ranging from 970 °C to 1170 °C and strain rates ranging from 0.01s−1 to 1s−1. To precisely elucidate these phenomena, we meticulously constructed a unified visco-plastic constitutive model using the internal variable methodology. The model's parameterization was achieved through the effective application of genetic algorithm optimization techniques. Rigorous validation of the model was performed by meticulously comparing its outputs with experimental data, including key metrics such as average grain size, recrystallized fraction, and effective flow stress. In addition,a comparative analysis with the improved Arrhenius model highlights the superior performance of the unified visco-plastic constitutive equation in capturing the intricate microstructural evolution and thermal deformation behavior exhibited by EA4T steel.

Advancements in Fused Deposition Modeling for Aerospace: Optimizing Lightweight and High- Strength Components

Fused Deposition Modeling (FDM) has emerged as a pivotal technology in aerospace manufacturing, enabling the creation of lightweight and high-strength components. Recent advancements in FDM materials, process optimization, and design methodologies have significantly enhanced its applicability in producing aerospace parts that meet stringent performance criteria. This paper reviews the latest developments in FDM technology, focusing on material innovations, structural optimization techniques, design for additive manufacturing and practical applications in the aerospace sector. Key advancements include the use of high-performance thermoplastics, carbon fiber composites, and hybrid materials, as well as improved printing techniques that reduce defects and enhance mechanical properties. The potential of FDM to revolutionize aerospace manufacturing through cost- effective and efficient production of complex geometries is explored, highlighting ongoing research and future directions in this dynamic field.

TOPCOAT: towards practical two-party Crystals-Dilithium

The development of threshold protocols based on lattice-signature schemes has been of increasing interest in the past several years. The main research focus has been towards protocols constructed for various variants of Crystals-Dilithium, future NIST digital signature standard known as ML-DSA. In this work, we propose TOPCOAT, a two-party lattice-based signature algorithm that embodies Dilithium’s compression techniques. The aforesaid result is achieved by introducing a new hinting mechanism that allows parties to collaboratively calculate HighBits\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf {HighBits}$$\\end{document}. Our hinting mechanism allows public key compression similar to Dilithium. Additionally, we suggest an optimization technique to minimize number of restarts both parties need to produce a valid signature. Our approach allows to produce ≈10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx 10$$\\end{document} KB signatures within 3 rounds of communication. We prove security of our scheme under MLWE and MSIS assumptions in ROM, and provide implementation of our proposed scheme. As additional contribution, we present vulnerabilities and inconsistencies found in Liu et al. work (Future Generation Computer Systems 2023) which aimed to construct distributed lattice-based signature protocol.

SAARC super smart grid: Navigating the future - unleashing the power of an energy-efficient integration of renewable energy resources in the saarc region

The major problems in the power system network of the South Asian Association for Regional Cooperation (SAARC) region include the utilization of substantial fossil fuels, which leads to significant emissions of CO2. Moreover, due to the depletion of fossil fuels, it is very difficult to manage the demand requirement of the SAARC regions, which leads to significant power shortage issues. These problems would be resolved through the integration of the substantial amount of renewable energy resources (RERs) that are available in excess in the SAARC regions into the power system networks. This will provide a futuristic roadmap to a sustainable and green energy environment in the SAARC region. In this regard, the SAARC region has devised a new plan to transform its smart grid infrastructure into the super smart grid (SSG), which would connect the SAARC countries using the high voltage transmission lines utilizing each country's substantial RERs. To accelerate this ecofriendly effort, the key objective of this review study is to evaluate the potential of the SAARC SSG for demand management and emissions reduction. This study provides a comprehensive overview of SSG technology, covering its technical background, management, power flow improvement techniques, power stability techniques, simulation models, and future steps for development in the SAARC region. First, the regional conflicts, energy deficiency, and potential of the SAARC region have been discussed. Second, a potential architecture of the SAARC SSG has been simulated. Third, a novel Markov chain modeling has been presented for demand management in the SAARC regions. The results and discussions section presents findings from simulations and models. The paper also discusses CO2 reduction techniques, including methods for capturing, storing, and utilizing carbon in biofuel. The study also discusses futuristic optimization techniques in SSG, including blockchain, federated learning, reinforcement learning, the metaverse, digital twin technology, artificial intelligence, and cloud computing. The SSG challenges and limitations section summarizes the challenges and limitations faced by SSG. Finally, a roadmap to a sustainable and green energy environment for the SAARC power grids has been identified to support the implementation of the futuristic SAARC SSG. The results exhibited that there is a good potential for successful demand management and emissions reduction through the SAARC SSG.

Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization

Software-Defined Networking is today a mature technology, which is developed in many networks and also embedded in novel architectures like 5G and 6G. The SDN control centralization concept brings significant advantages for management and control in SDN together with the programmability of the data plane. SDN represents a paradigm shift towards agile, efficient, and secure network infrastructures, moving away from traditional, hardware-centric models to embrace dynamic, software-driven paradigms. SDN is compliant also with the virtualization architecture defined in the Network Function Virtualization framework. However, SDN should cooperate seamlessly for some years with the distributed TCP/IP control developed during the years all over the world. Among others, the traditional tasks of routing, forwarding, load balancing, QoS assurance, security, and privacy should be solved. The SDN native centralization brings also some new challenges and problems which are different from the traditional distributed control IP networks. The algorithms and protocols usable in SDN should meet requirements like scalability, convergence, redundancy assurance, sustainability, and good real-time response, and allow orchestrated automation in enhancing network resilience and adaptability. This work presents a theoretical review of state-of-the-art SDN optimization techniques, offering a critical and comparative discussion of various algorithms having tasks such as routing (including dynamic ones), forwarding, load balancing and traffic optimization, and forwarding delay minimization. Attention is pointed to general algorithms which can offer pragmatic solutions for large systems or multiple metric routing.

Machine Learning Based Compiler Optimization Technique

Since the last decade to upcoming era the need of machine learning based compilation approaches has become indispensable for every aspect of growing technology especially artificial intelligence and network computing areas. These approaches are used to enhance the result quality by their improved performance as well as handles compiler optimization problems namely optimization selection and phase ordering. It has enthused from a vague research place in middle of the road movement. Wealth of present compiler optimization techniques leads to finding the best heuristic parameters to tune each optimization tactic using machine learning. In this research paper, we highlighted the term machine learning and compiler along with relationship between compiler optimization and machine learning with the identity of the concept of models, training, and approaches.

Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization

Software-Defined Networking is today a mature technology, which is developed in many networks and also embedded in novel architectures like 5G and 6G. The SDN control centralization concept brings significant advantages for management and control in SDN together with the programmability of the data plane. SDN represents a paradigm shift towards agile, efficient, and secure network infrastructures, moving away from traditional, hardware-centric models to embrace dynamic, software-driven paradigms. SDN is compliant also with the virtualization architecture defined in the Network Function Virtualization framework. However, SDN should cooperate seamlessly for some years with the distributed TCP/IP control developed during the years all over the world. Among others, the traditional tasks of routing, forwarding, load balancing, QoS assurance, security, and privacy should be solved. The SDN native centralization brings also some new challenges and problems which are different from the traditional distributed control IP networks. The algorithms and protocols usable in SDN should meet requirements like scalability, convergence, redundancy assurance, sustainability, and good real-time response, and allow orchestrated automation in enhancing network resilience and adaptability. This work presents a theoretical review of state-of-the-art SDN optimization techniques, offering a critical and comparative discussion of various algorithms having tasks such as routing (including dynamic ones), forwarding, load balancing and traffic optimization, and forwarding delay minimization. Attention is pointed to general algorithms which can offer pragmatic solutions for large systems or multiple metric routing.

Design strategies and applications of atomically dispersed aerogel catalysts prepared from metal hydrogel formation

Abstract The preparation of metal hydrogel precursors toward atomically dispersed aerogel catalysts is a captivating subject within the catalysis research domain, exhibiting promising applications in various fields. This minireview intend to summarize those novel design principles and practical applications of metal hydrogel precursors in the creation of atomically dispersed aerogel catalysts. The manuscript focuses on crucial aspects such as the meticulous selection of metal sources and initiators, the intricate process of gel preparation, a comparative analysis of different drying methods, and strategic optimization techniques to enhance the performance of metal hydrogel precursors. Moreover, this article delves into the exploration of atomically dispersed aerogel catalysts derived from metal hydrogel precursors in the realms of photocatalysis and electrocatalysis. Lastly, existing challenges are addressed, and prospective future directions for development are outlined.

Design strategies and applications of atomically dispersed aerogel catalysts prepared from metal hydrogel formation

Abstract The preparation of metal hydrogel precursors toward atomically dispersed aerogel catalysts is a captivating subject within the catalysis research domain, exhibiting promising applications in various fields. This minireview intend to summarize those novel design principles and practical applications of metal hydrogel precursors in the creation of atomically dispersed aerogel catalysts. The manuscript focuses on crucial aspects such as the meticulous selection of metal sources and initiators, the intricate process of gel preparation, a comparative analysis of different drying methods, and strategic optimization techniques to enhance the performance of metal hydrogel precursors. Moreover, this article delves into the exploration of atomically dispersed aerogel catalysts derived from metal hydrogel precursors in the realms of photocatalysis and electrocatalysis. Lastly, existing challenges are addressed, and prospective future directions for development are outlined.

A hierarchical optimization approach to maximize hosting capacity for electric vehicles and renewable energy sources through demand response and transmission expansion planning

Within the scope of sustainable development, integrating electric vehicles (EVs) and renewable energy sources (RESs) into power grids offers a number of benefits. These include reducing greenhouse gas emissions, diversifying energy sources, and promoting the use of green energy. Although the literature on hosting capacity (HC) models has grown, there is still a noticeable gap in the discussion of models that successfully handle transmission expansion planning (TEP), demand response (DR), and HC objectives simultaneously. Combining TEP, DR, and HC objectives in one model optimizes resource use, enhances grid stability, supports renewable and EV integration, and aligns with regulatory and market demands, resulting in a more efficient, reliable, and sustainable power system. This research presents an innovative two-layer HC model, including considerations for TEP and DR. The model determines the highest degree of load shifting appropriate for incorporation into power networks in the first layer. Meanwhile, the second layer focuses on augmenting the RES and EVs’ hosting capability and modernizing the network infrastructure. System operators can choose the best scenario to increase the penetration level of EVs and RESs with the aid of the proposed model. The proposed model, which is formulated as a multi-objective mixed-integer nonlinear optimization problem, uses a hierarchical optimization technique to identify effective solutions by combining the particle swarm optimization algorithm and the crayfish optimizer. When compared to traditional methods, the results obtained from implementing the proposed hierarchical optimization algorithm on the Garver network and the IEEE 24-bus system indicated how effective it is at solving the presented HC model. The case studies demonstrated that integrating DR into the HC problem reduced peak load by 10.4–23.25%. The findings also highlighted that DR did not impact the total energy consumed by EVs throughout the day, but it did reshape the timing of EV charging, creating more opportunities for integration during periods of high demand. Implementing DR reduced the number of projects needed and, in some cases, led to cost savings of up to 12.3%.

Machine Learning Based Compiler Optimization Technique

Since the last decade to upcoming era the need of machine learning based compilation approaches has become indispensable for every aspect of growing technology especially artificial intelligence and network computing areas. These approaches are used to enhance the result quality by their improved performance as well as handles compiler optimization problems namely optimization selection and phase ordering. It has enthused from a vague research place in middle of the road movement. Wealth of present compiler optimization techniques leads to finding the best heuristic parameters to tune each optimization tactic using machine learning. In this research paper, we highlighted the term machine learning and compiler along with relationship between compiler optimization and machine learning with the identity of the concept of models, training, and approaches.

A High-Performance and Ultra-Low-Power Accelerator Design for Advanced Deep Learning Algorithms on an FPGA

This article addresses the growing need in resource-constrained edge computing scenarios for energy-efficient convolutional neural network (CNN) accelerators on mobile Field-Programmable Gate Array (FPGA) systems. In particular, we concentrate on register transfer level (RTL) design flow optimization to improve programming speed and power efficiency. We present a re-configurable accelerator design optimized for CNN-based object-detection applications, especially suitable for mobile FPGA platforms like the Xilinx PYNQ-Z2. By not only optimizing the MAC module using Enhanced clock gating (ECG), the accelerator can also use low-power techniques such as Local explicit clock gating (LECG) and Local explicit clock enable (LECE) in memory modules to efficiently minimize data access and memory utilization. The evaluation using ResNet-20 trained on the CIFAR-10 dataset demonstrated significant improvements in power efficiency consumption (up to 22%) and performance. The findings highlight the importance of using different optimization techniques across multiple hardware modules to achieve better results in real-world applications.

Advancements in Soft Soil Stabilization by Employing Novel Materials through Response Surface Methodology

Stabilization using industrial by-products is presently gaining importance in the construction sector for improving the geotechnical characteristics of soft soils. The optimum dosage of stabilisers has become of great interest to experimenters in terms of improved strength, time, and economy for construction projects. This work presents the utilization of biomedical waste ash for improving the strength of soft soil. In this paper, response surface methodology (RSM) was adopted to determine the optimum combination curing period (C) and biomedical waste ash (BA) quantity for attaining the maximum unconfined compressive strength (UCS) of soft soil and to reduce the number of trial tests required. The response factors C and BA were varied from 0 to 14 days and 4% to 20%, respectively, and the experiments were conducted according to the experimental plan provided by the RSM design. Based on a Face-centred Central Composite Design (FCCCD), a mathematical equation was created for the experimental results. Analysis of variance (ANOVA) was used to determine the generated model’s significance, and the results indicated a statically significant model (p ≤ 0.05). The results revealed that the curing period imparts more influence towards strength improvement, and the optimum dosage was 19.912% BA, with curing of 14 days to yield a maximum UCS of 203.008 kPa. This optimization technique may be suggested to obtain a preliminary estimation of strength prior to stabilization.

A proposed framework for supplier selection and order allocation using machine learning clustering and optimization techniques

A proposed framework for supplier selection and order allocation using machine learning clustering and optimization techniques

Advancements in Soft Soil Stabilization by Employing Novel Materials through Response Surface Methodology

Stabilization using industrial by-products is presently gaining importance in the construction sector for improving the geotechnical characteristics of soft soils. The optimum dosage of stabilisers has become of great interest to experimenters in terms of improved strength, time, and economy for construction projects. This work presents the utilization of biomedical waste ash for improving the strength of soft soil. In this paper, response surface methodology (RSM) was adopted to determine the optimum combination curing period (C) and biomedical waste ash (BA) quantity for attaining the maximum unconfined compressive strength (UCS) of soft soil and to reduce the number of trial tests required. The response factors C and BA were varied from 0 to 14 days and 4% to 20%, respectively, and the experiments were conducted according to the experimental plan provided by the RSM design. Based on a Face-centred Central Composite Design (FCCCD), a mathematical equation was created for the experimental results. Analysis of variance (ANOVA) was used to determine the generated model’s significance, and the results indicated a statically significant model (p ≤ 0.05). The results revealed that the curing period imparts more influence towards strength improvement, and the optimum dosage was 19.912% BA, with curing of 14 days to yield a maximum UCS of 203.008 kPa. This optimization technique may be suggested to obtain a preliminary estimation of strength prior to stabilization.

A High-Performance and Ultra-Low-Power Accelerator Design for Advanced Deep Learning Algorithms on an FPGA

This article addresses the growing need in resource-constrained edge computing scenarios for energy-efficient convolutional neural network (CNN) accelerators on mobile Field-Programmable Gate Array (FPGA) systems. In particular, we concentrate on register transfer level (RTL) design flow optimization to improve programming speed and power efficiency. We present a re-configurable accelerator design optimized for CNN-based object-detection applications, especially suitable for mobile FPGA platforms like the Xilinx PYNQ-Z2. By not only optimizing the MAC module using Enhanced clock gating (ECG), the accelerator can also use low-power techniques such as Local explicit clock gating (LECG) and Local explicit clock enable (LECE) in memory modules to efficiently minimize data access and memory utilization. The evaluation using ResNet-20 trained on the CIFAR-10 dataset demonstrated significant improvements in power efficiency consumption (up to 22%) and performance. The findings highlight the importance of using different optimization techniques across multiple hardware modules to achieve better results in real-world applications.

A proposed framework for supplier selection and order allocation using machine learning clustering and optimization techniques

A proposed framework for supplier selection and order allocation using machine learning clustering and optimization techniques

Probing flavor constrained SMEFT operators through tc production at the muon collider

We investigate flavor violating four-Fermi Standard Model Effective Field Theory (SMEFT) operators of dimension-six that can be probed via tct¯c+tc¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\left(\\overline{t}c+t\\overline{c}\\right) $$\\end{document} production at the multi-TeV muon collider. We study different FCNC and FCCC processes related to B, Bs, K and D decays and mixings, sensitive to these operators and constrain the corresponding couplings. The tensor operator turns out to be most tightly bound. We perform event simulation of the final state signal from tc production together with the SM background to show that operators after flavor constraint can reach the discovery limit at 10 TeV muon collider. We further adopt the optimal observable technique (OOT) to determine the optimal statistical sensitivity of the Wilson coefficients and compare them with the flavor constraints. We use the limits to predict the observational sensitivities of the rare processes like KL → π0ℓℓ, D0 → μμ, t → cℓℓ.