Efficient Optimization Research Articles

A new adaptive elastic loss for robust unsupervised feature selection

The sparse-based unsupervised dimension reduction technique has been demonstrated to be an effective tool for dealing with high-dimensional data. Among them, the sparse regularization technique is used to introduce the prior knowledge of the model and helps unsupervised dimension reduction algorithms to complete the feature selection. However, the sparse regularization only considers the sparsity of representation. It ignores the group effect of variables among which the pairwise correlations are very high. To solve these issues, we present a novel regularization approach based on adaptive elastic loss in this study. Then, utilizing the regularization method, an efficient unsupervised feature selection methodology is given. Additionally, we devise an efficient alternative optimization algorithm to resolve the complex optimization issue of our proposed method and conduct a theoretical analysis of its computational complexity and convergence. Eventually, we conduct comprehensive experiments to test the validity of our proposed method, and real-world data and a simulation study demonstrate that our proposed method outperforms many state-of-the-art unsupervised feature selection methods in terms of clustering and classification.

Optimization of efficiency of CZTS-based solar cell through exclusive BSF layer engineering method

Optimization of efficiency of CZTS-based solar cell through exclusive BSF layer engineering method

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.

Hardware-Aware Energy Efficiency Optimization in Wireless Communications Using a Gearbox-PHY

Hardware-Aware Energy Efficiency Optimization in Wireless Communications Using a Gearbox-PHY

A energy efficiency optimization routing processing method for Linear Wireless Sensor Networks

Linear wireless Sensor networks (LWSNs) are extensively utilized to monitor linear infrastructure such as railways, bridges, mines, and borders. In the large-scale deployment of sensor networks, extending network life and improving network energy balance remains a significant challenge. The existing routing protocols adopt the path planning method based on the routing information database, which can effectively transmit data packets. However, the methods consume additional energy when establishing and maintaining routing tables, significantly reducing the nodes’ lifespan and leading to unstable data transmission quality. This paper proposes a dynamic random multipath routing method (DRMRM) for LWSNs. The technique combines the node depth and residual energy models to select the optimal next-hop relay node without relying on the transmission routing table. At the same time, we designed a data loss retransmission mechanism and a data loop retreat mechanism to prevent data packets from reaching a dead end. The experimental results demonstrate that our routing method is superior to existing energy consumption balance and network lifespan protocols.

Fault causes and its detection in standalone PV system using ANN and GEO technique

Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems.

Universal Phase-Shift Modulation Scheme and Efficiency Optimization for Modular Multiactive Bridge Converter

Universal Phase-Shift Modulation Scheme and Efficiency Optimization for Modular Multiactive Bridge Converter

Enhanced Wireless Communication Optimization with Neural Networks, Proximal Policy Optimization and Edge Computing for Latency and Energy Efficiency

This research proposes a novel approach for efficient resource allocation in wireless communication systems. It combines dynamic neural networks, Proximal Policy Optimization (PPO), and Edge Computing Orchestrator (ECO) for latency-aware and energy-efficient resource allocation. The proposed system integrates multiple components, including a dynamic neural network, PPO, ECO, and a Mobile Edge Computing (MEC) server. The experimental methodology involves utilizing the NS-3 simulation platform to assess latency and energy efficiency in resource allocation within a wireless communication network, incorporating an ECO, MEC server, and dynamic task scheduling algorithms. It demonstrates a holistic and adaptable approach to resource allocation in dynamic environments, showcasing a notable reduction in latency for devices and tasks. Latency values range from 5 to 20 milliseconds, with corresponding resource utilization percentages varying between 80% and 95%. Additionally, energy-efficient resource allocation demonstrates a commendable reduction in energy consumption, with measured values ranging from 10 to 30 watts, coupled with efficient resource usage percentages ranging from 70% to 85%. These outcomes validate the efficacy of achieving both latency-aware and energy-efficient resource allocation for enhanced wireless communication systems. The proposed system has broad applications in healthcare, smart cities, IoT, real-time analytics, autonomous vehicles, and augmented reality, offering a valuable solution to optimize energy consumption, reduce latency, and enhance system efficiency in these industries.

A Technique for Selecting Quadrature Points for Dimension Reduction Method to Improve Efficiency in Reliability-based Design Optimization

A Technique for Selecting Quadrature Points for Dimension Reduction Method to Improve Efficiency in Reliability-based Design Optimization

Exploring the potential of swarm intelligence for optimal energy efficiency in IoT downlink system

This study delves into the energy optimization problem in Internet of Things (IoT) networks. We consider the downlink from multiple antenna Gateway (GW) and single antenna IoT devices. For this challenging nonconvex problem, we initially introduced the well-known zero-forcing beamforming (ZFBF) to eliminate inter-user interference, thereby transforming the energy efficiency maximization problem into a concave-convex fractional problem. Then, instead of applying a combination of ZFBF with power allocation, we propose the Particle Swarm Optimization (PSO) algorithm to allocate power to find the optimized beamforming matrix. Through extensive numerical analysis, we demonstrate the effectiveness of our proposed scheme in terms of energy efficiency and power achieved at the GW. The results underscore the significant benefits of our approach over conventional methods, paving the way for practical and efficient energy optimization in IoT networks.

Bearing-DETR: A Lightweight Deep Learning Model for Bearing Defect Detection Based on RT-DETR.

Detecting bearing defects accurately and efficiently is critical for industrial safety and efficiency. This paper introduces Bearing-DETR, a deep learning model optimised using the Real-Time Detection Transformer (RT-DETR) architecture. Enhanced with Dysample Dynamic Upsampling, Efficient Model Optimization (EMO) with Meta-Mobile Blocks (MMB), and Deformable Large Kernel Attention (D-LKA), Bearing-DETR offers significant improvements in defect detection while maintaining a lightweight framework suitable for low-resource devices. Validated on a dataset from a chemical plant, Bearing-DETR outperformed the standard RT-DETR, achieving a mean average precision (mAP) of 94.3% at IoU = 0.5 and 57.5% at IoU = 0.5-0.95. It also reduced floating-point operations (FLOPs) to 8.2 G and parameters to 3.2 M, underscoring its enhanced efficiency and reduced computational demands. These results demonstrate the potential of Bearing-DETR to transform maintenance strategies and quality control across manufacturing environments, emphasising adaptability and impact on sustainability and operational costs.

Efficient reliability-based concurrent topology optimization method under PID-driven sequential decoupling framework

The continuous improvement of advanced equipment performance has promoted the development of detailed structural design. The detailed design of the structure should meet many requirements such as high reliability, strong robustness, lightweight, and high efficiency. This paper proposes a proportional-integral-derivative-driven efficient reliability-based concurrent topology optimization strategy under the sequential framework. Considering the uncertainty of load position, load direction, load amplitude, and material properties, an efficient reliability-based concurrent topology optimization model is constructed based on the sequential strategy. Based on effectively quantifying the nonlinear characteristics brought by multi-source uncertainty, a constraint refinement movement strategy based on the proportional-integral-derivative controller is proposed, which to some extent overcomes the tedious calculation of uncertainty quantification. This paper provides sensitivity information on macroscopic and microscopic level set functions under displacement constraints. Four numerical examples demonstrate the effectiveness, efficiency, and generalization ability of the proposed method.

A novel and efficient mathematical optimization model for multi-stage assembly flow shop considering post-processing

ABSTRACT We addressed the multistage assembly flow shop problem with post-processing and makespan minimization, a production environment commonly encountered in diverse industries such as automotive, dental, medical equipment, and clothing manufacturing. In this context, we presented an innovative mixed-integer linear programming model with a position-based strategy. Our proposed formulation demonstrated remarkable efficiency when compared to the model of the literature. It achieved optimal solutions in 77.16% of instances, with an average optimality gap of 10.59%. This study constitutes a significant contribution to the efficient resolution of a practical and frequently encountered scheduling problem that has received relatively limited attention in existing literature. The findings highlight the crucial role of mathematical optimization models as valuable decision-making tools for scheduling within the addressed production system.

Efficient simultaneous optimization approach for the synthesis of large-scale heat-integrated water networks

This study develops a generalized superstructure and a compact mixed-integer nonlinear programming (MINLP) model for achieving efficient energy and water utilization in designing integrated process water systems with large sizes. Utilizing non-isothermal mixing reduces the need for many heat exchangers for indirect heat recovery. Hence, an exchanger-centric modeling strategy for heat integration is embraced over a hot–cold streams matching-based approach. A novel two-step approach is proposed to address the nonconvex and nonlinear challenges and ensure the efficiency of getting high-quality solutions. Initially, a targeting model incorporating linear heat integration is adopted to reduce the superstructure and partially initialize the design model. Subsequently, in the synthesizing stage, a two-level global search procedure is devised, integrating adaptive exchanger adjustment with multi-start local optimization. The robustness and efficiency of the proposed approach are confirmed through the resolution of four large-scale problems within one hour, yielding improved results compared to the best-known existing solutions.

Knowledge-Guided Parallel Hybrid Local Search Algorithm for Solving Time-Dependent Agile Satellite Scheduling Problems

As satellite capabilities have evolved and new observation requirements have emerged, satellites have become essential tools in disaster relief, emergency monitoring, and other fields. However, the efficiency of satellite scheduling still needs to be enhanced. Learning and optimization are symmetrical processes of solving problems. Learning problem knowledge could provide efficient optimization strategies for solving problems. A knowledge-guided parallel hybrid local search algorithm (KG-PHLS) is proposed in this paper to solve time-dependent agile Earth observation satellite (AEOS) scheduling problems more efficiently. Firstly, the algorithm uses heuristic algorithms to generate initial solutions. Secondly, a knowledge-based parallel hybrid local search algorithm is employed to solve the problem in parallel. Meanwhile, data mining techniques are used to extract knowledge to guide the construction of new solutions. Finally, the proposed algorithm has demonstrated superior efficiency and computation time through simulations across multiple scenarios. Notably, compared to benchmark algorithms, the algorithm improves overall efficiency by approximately 7.4% and 8.9% in large-scale data scenarios while requiring only about 60.66% and 31.89% of the computation time of classic algorithms. Moreover, the proposed algorithm exhibits scalability to larger problem sizes.

Polymer-Tethered Quenched Fluorescent Probes for Enhanced Imaging of Tumor-Associated Proteases.

Fluorescence-based contrast agents enable real-time detection of solid tumors and their neovasculature, making them ideal for use in image-guided surgery. Several agents have entered late-stage clinical trials or secured FDA approval, suggesting they are likely to become the standard of care in cancer surgeries. One of the key parameters to optimize in contrast agents is molecular size, which dictates much of the pharmacokinetic and pharmacodynamic properties of the agent. Here, we describe the development of a class of protease-activated quenched fluorescent probes in which a N-(2-hydroxypropyl)methacrylamide copolymer is used as the primary scaffold. This copolymer core provides a high degree of probe modularity to generate structures that cannot be achieved with small molecules and peptide probes. We used a previously validated cathepsin substrate and evaluated the effects of length and type of linker, as well as the positioning of the fluorophore/quencher pair on the polymer core. We found that the polymeric probes could be optimized to achieve increased overall signal and tumor-to-background ratios compared to the reference small molecule probe. Our results also revealed multiple structure-activity relationship trends that can be used to design and optimize future optical imaging probes. Furthermore, they confirm that a hydrophilic polymer is an ideal scaffold for use in optical imaging contrast probes, allowing a highly modular design that enables efficient optimization to maximize probe accumulation and overall biodistribution properties.

Similitude laws for dynamic response of beam-plate coupled structure

The beam-plate coupled structure is a typical structure in spacecraft solar wings, and the prediction of its mechanical environment is crucial for overall design, structure and mechanism subsystem design. However, it is usually difficult to perform ground simulation tests on spacecraft solar wings with full-size structure. Therefore, a scaled model is established based on similarity theory to forecast the dynamic response of the full-size structure. In this article, finite element (FE) and hybrid finite element-statistical energy analysis (FE-SEA) models of the beam-plate coupled structure are established according to the band division principle. Finite element model allows for detailed analysis of structural behaviour, while hybrid models incorporate statistical energy analysis to account for high-frequency dynamics. By integrating these approaches, a comprehensive understanding of the coupled structure’s response can be achieved, facilitating efficient design and optimisation processes. The scaling laws of the dynamic response are obtained by combining the dimensional analysis and equation analysis methods, respectively. The scaled models are designed for verification, and the natural characteristics and vibration responses predicted by the scaled models are basically consistent with those of the prototype. The findings indicate that the prediction accuracy derived from the FE-SEA model is higher, which provides a novel approach for the dynamic similarity of complicated structures such as the solar wings of spacecraft.

Efficient Design of Broadband and Low-Profile Multilayer Absorbing Materials on Cobalt-Iron Magnetic Alloy Doped with Rare Earth Element.

Magnetic metal absorbing materials have exhibited excellent absorptance performance. However, their applications are still limited in terms of light weight, low thickness and wide absorption bandwidth. To address this challenge, we design a broadband and low-profile multilayer absorber using cobalt-iron (CoFe) alloys doped with rare earth elements (REEs) lanthanum (La) and Neodymium (Nd). An improved estimation of distribution algorithm (IEDA) is employed in conjunction with a mathematical model of multilayer absorbing materials (MAMs) to optimize both the relative bandwidth with reflection loss (RL) below -10 dB and the thickness. Firstly, the absorption performance of CoFe alloys doped with La/Nd with different contents is analysed. Subsequently, IEDA is introduced based on a mathematical model to achieve an optimal MAM design that obtains a balance between absorption bandwidth and thickness. To validate the feasibility of our proposed method, a triple-layer MAM is designed and optimized to exhibit wide absorption bandwidth covering C, X, and Ku bands (6.16-12.82 GHz) and a total thickness of 2.39 mm. Then, the electromagnetic (EM) absorption mechanisms of the triple-layer MAMs are systematically investigated. Finally, the triple-layer sample is further fabricated and measured. The experimental result is in good agreement with the simulated result. This paper presents a rapid and efficient optimization method for designing MAMs, offering promising prospects in microwave applications, such as radar-stealth technology, EM shielding, and reduced EM pollution for electronic devices.

Comparative evaluation between Taguchi method and response surface method for optimization of electrocoagulation process in the context of treatment of dairy industry wastewater.

The presence of a large amount of organic and inorganic pollutants in dairy effluent is a substantial environmental issue. This study investigated electrocoagulation (EC) as a potential treatment method for dairy wastewater under different operating conditions, such as applied voltage (5-25V), electrolysis time (30-90min), and inter-electrode distance (1-2cm) by using aluminum electrodes. This study focuses on achieving the maximum removal of BOD, COD, and nitrate in dairy effluents with the aforementioned operating conditions. The process was optimized using the response surface methodology (RSM) and Taguchi method. RSM method optimized the electrocoagulation operating conditions such as the voltage at 23.75V, time of 90min, and inter-electrode distance at 1.07cm. This optimization achieved the maximum removal percentage of BOD, COD, and nitrate at 79.06%, 84.35%, and 79.64%, respectively, in dairy effluent. Taguchi method optimized the electrocoagulation parameters such as the voltage at 25V, time duration of 90min, and inter-electrode distance of 1.00cm, showcasing improved removal percentages of BOD, COD, and nitrate as 90.54%, 89.28%, and 82.74% respectively. The current study attempts to understand the optimization efficiencies between Taguchi method and response surface method for diary wastewater treatment.

AN INTEGRATED APPROACH TO DESIGNING ROBUST GAS-BEARING SUPPORTED TURBOCOMPRESSORS THROUGH SURROGATE MODELING AND CONSTRAINED ALL-AT-ONCE MULTI-OBJECTIVE OPTIMIZATION

Abstract This research introduces an innovative framework to engineering design to tackle the challenges of robustness against manufacturing deviations and holistic optimization simultaneously in a multi-disciplinary, multi-subsystems context. The methodology is based on an application of ensemble artificial neural networks, which significantly accelerates computational processes. Coupled with the Non-dominated Sorting Genetic Algorithm III, this approach facilitates efficient multi-objective optimization, yielding a comprehensive Pareto front and high-quality design solutions. Here, the framework is applied to the design of gas-bearing-supported turbocompressors. These systems are challenging due to their sensitivity to manufacturing variations, particularly in the gas-bearing geometry, which can lead to rotordynamic instability. Additionally, the interdependencies between the subsystems, such as axial and journal bearings, rotor, compressor impellers, and magnets, necessitate a multidisciplinary approach that spans aerodynamics, structural dynamics, rotordynamics, mechanics, loss analyses, and more. A clear tradeoff between system efficiency, mass-flow range, and robustness has been identified for the compressor design. Higher nominal compressor mass-flows, i.e. increased nominal power, is suggested to decrease the hypervolume of feasible manufacturing deviations. Hence, there is a sweet power spot for gas-bearing supported turbomachinery. Further, the framework's computational efficiency is on par with that of a university cluster, while only employing a desktop computer equipped with a consumer-grade graphics card. This work demonstrates a significant advancement in the design of complex engineering systems and sets a new standard for speed and efficiency in computational engineering design.