Network Optimization Model Research Articles

Analysis and design of optimal deep neural network model for image recognition using hybrid cuckoo search with self-adaptive particle swarm intelligence

Analysis and design of optimal deep neural network model for image recognition using hybrid cuckoo search with self-adaptive particle swarm intelligence

A BP Neural Network Product Design Optimization Model Based on Emotional Design and Sustainable Product Design

The objective of this study is to investigate the impact of emotional design and sustainable product design on user experience and satisfaction. Additionally, the study aims to optimize the combination efficiency and material selection methods of these two design approaches, extracting optimization parameters based on emotional and sustainable product design principles. By analyzing user satisfaction through questionnaires, various product indicators were examined. The study ultimately establishes a BP neural network optimization model by analyzing the design elements of different products and the satisfaction levels of various indicators. This BP neural network design optimization model, integrating emotional and sustainable design, demonstrates an accuracy rate of over 95% in predicting user satisfaction for different design schemes. Upon examining the emotional design and sustainable design parameter tables following data standardization, and analyzing various product models, it was concluded that the rate of change in each design parameter significantly impacts the satisfaction of each product indicator, highlighting the importance of these parameters in product design optimization.

Deep Learning-Based Simultaneous Temperature- and Curvature-Sensitive Scatterplot Recognition.

Since light propagation in a multimode fiber (MMF) exhibits visually random and complex scattering patterns due to external interference, this study numerically models temperature and curvature through the finite element method in order to understand the complex interactions between the inputs and outputs of an optical fiber under conditions of temperature and curvature interference. The systematic analysis of the fiber's refractive index and bending loss characteristics determined its critical bending radius to be 15 mm. The temperature speckle atlas is plotted to reflect varying bending radii. An optimal end-to-end residual neural network model capable of automatically extracting highly similar scattering features is proposed and validated for the purpose of identifying temperature and curvature scattering maps of MMFs. The viability of the proposed scheme is tested through numerical simulations and experiments, the results of which demonstrate the effectiveness and robustness of the optimized network model.

Hybrid Particle Swarm Optimization and Feedforward Neural Network Model for Enhanced Prediction of Gas Turbine Emissions

Hybrid Particle Swarm Optimization and Feedforward Neural Network Model for Enhanced Prediction of Gas Turbine Emissions

Developing a Bi-Level Optimization Model for the Coupled Street Network and Land Subdivision Design Problem with Various Lot Areas in Irregular Blocks

Street design and land subdivision are significant tasks in the development and redevelopment planning process. Optimizing street and land subdivision layouts within a unified framework to achieve solutions that meet a set of objectives and constraints (e.g., minimizing parcel area deviation from standard values, minimizing land consumption for street construction, etc.) is a critical concern for planners, particularly in complex contexts such as blocks with irregular shapes and parcels of varying sizes and requirements. To address this challenge, a mathematical formulation is presented for the bi-level street network and land subdivision optimization problem. Subsequently, the solution procedure is outlined, which utilizes a genetic-based algorithm for street design and a memetic–genetic-based algorithm for land subdivision. Finally, two cases are presented, solved, and discussed to analyze and verify the proposed mathematical model and solution procedures. The results suggest that the formulated problem is suitable for addressing the coupled street network and land subdivision design problem, and it can be adapted and extended to other case studies. Additionally, the introduced ideas and algorithms satisfactorily solved the stated problem.

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums.

This study aims to develop a digital retrieval system for art museums to solve the problems of inaccurate information and low retrieval efficiency in the digital management of cultural heritage. By introducing an improved Genetic Algorithm (GA), digital management and access efficiency are enhanced, to bring substantial optimization and innovation to the digital management of cultural heritage. Based on the collection of art museums, this study first integrates the collection's images, texts, and metadata with multi-source intelligent information to achieve a more accurate and comprehensive description of digital content. Second, a GA is introduced, and a GA 2 Convolutional Neural Network (GA2CNN) optimization model combining domain knowledge is proposed. Moreover, the convergence speed of traditional GA is improved to adapt to the characteristics of cultural heritage data. Lastly, the Convolutional Neural Network (CNN), GA, and GA2CNN are compared to verify the proposed system's superiority. The results show that in all models, the sample output results' actual value is 2.62, which represents the real data observation results. For sample number 5, compared with the actual value of 2.62, the predicted values of the GA2CNN and GA models are 2.6177 and 2.6313, and their errors are 0.0023 and 0.0113. The CNN model's predicted value is 2.6237, with an error of 0.0037. It can be found that the network fitting accuracy after optimization of the GA2CNN model is high, and the predicted value is very close to the actual value. The digital retrieval system integrated with the GA2CNN model has a good performance in enhancing retrieval efficiency and accuracy. This study provides technical support for the digital organization and display of cultural heritage and offers valuable references for innovative exploration of museum information management in the digital era.

Evaluation on network optimization model of low-carbon multi-level supply chain based on big data algorithm evaluation

Evaluation on network optimization model of low-carbon multi-level supply chain based on big data algorithm evaluation

Network optimization model approach to the volume-to-point heat conduction problem

Network optimization model approach to the volume-to-point heat conduction problem

The Net Zero policy and its impact on the air transport network: Considering the modal competition between airlines and high-speed rail

This study explores the relationship among carriers’ behavior, the increase of operational cost under the Net Zero policy, and the workability of greenhouse gas (GHG) reduction. We adopted the bi-level network optimization model with a modification on the passenger utility function to reflect the effect of carbon dioxide reduction preference. We considered a four-node network in which high-speed Rail is available for short-haul transport and aviation is used for long-haul transport. The results suggest that when the fuel price increases owing to the usage of Sustainable Aviation Fuel (SAF) and environment-friendly aircraft, airlines lose their profits and the passenger flow decreases. However, when passengers become more sensitive to the environmental load of travel, airlines are incentivized to use SAF and environment-friendly aircraft to gain more passengers, provided fuel costs do not rise sharply. Our results suggest that the effectiveness of GHG reduction largely depends on the cost increase and therefore, when imposing more stringent regulations on all airlines, new problems may arise from the decline in international communication.

Robust optimal dispatching of power grid with the participation of virtual power plants

Abstract One of the key technologies for creating intelligent distribution networks is energy management systems, which allow virtual power plants (VPPs) to combine distributed flexible resources and accomplish unified scheduling and coordination. This paper presents a solid optimization model that compromises safety and economics for distribution networks involving virtual power plants. First, a power flow model for the distribution network is created to minimize voltage variation, operating cycle cost, and the safety operation of several AM (active management) methods as constraints. Second, a strong optimization model for the distribution network was built with the participation of virtual power plants, accounting for the unpredictability of the load and fresh energy. Finally, the problem was solved using the relaxation evolution technique, and the precision and potency of the suggested model were shown by doing a simulated comparison study on the upgraded IEEE33 node testing system.

Coordinated operation of PV/EV/Temperature controlled loads in distribution network

Abstract In order to solve the problems of voltage exceeding limits, local use, and reverse power flow due to the high proportion of distributed photovoltaic grid connections, a distributed photovoltaic uptake method considering demand-side resources and energy storage is proposed. Firstly, based on the equivalent thermal parameter (ETP) method, a load scheduling model for temperature-controlled loads is established. Secondly, with the objective function of minimizing the comprehensive operating cost of the distribution system, combined with the regulating effect of energy storage, stability and safety constraints for the distribution network, as well as the adjustable characteristics of thermal storage electric heating, air conditioning load, and electric vehicles, a demand side optimization model for the distribution network is established, which takes into account voltage safety and distributed photovoltaic uptake. Finally, based on historical data in Fengning County, Chengde City, and a certain park in Tangshan City, simulation analysis was conducted to obtain scheduling schemes and various scheduling indicators for the joint participation of demand-side resources and energy storage in the distributed photovoltaic local use of the distribution network under different permeability. The effectiveness and rationality of the proposed distributed photovoltaic consumption model were verified.

Integration of physical information and reaction mechanism data for surrogate prediction model and multi-objective optimization of glycolic acid production

With the continuous development of the chemical industry, the concept of advocating green development has become increasingly popular. Glycolic acid (GA), serving as the monomer for biodegradable plastic polyglycolic acid, plays a crucial role in combating plastic pollution and fostering an eco-friendly society. The selective oxidation of ethylene glycol (EG) to produce GA represents a novel green production technology. Controlling reaction parameters to achieve multi-objective optimization of product distribution and direct CO2 emissions is crucial for scaling up the process. With the advent of the big data era, the integration of the chemical industry with artificial intelligence to achieve engineering scale-up is an important trend. This study proposes a neural network model for production prediction and optimization. The model is trained using experimental data, reaction mechanism data, and physical information, enabling rapid prediction of GA production. After validating with 40% of experimental data and 16% of reaction mechanism data, the model's prediction error was within ±5%, and the linear correlation coefficient R2 between the predicted values and actual values was 0.998. Furthermore, this study integrated a multi-objective optimization algorithm based on the model, enabling surrogate optimization of reaction parameters during production. After optimization, the direct CO2 emissions were reduced by over 99% and overall greenhouse gas emissions were reduced by 4.6%. The research paradigm proposed in this research can offer guidance and technical support for the optimized operation of EG selective oxidation to GA.

Benders Decomposition-based Coordinated Optimization Method for Medium and Low Voltage Distribution Network with Photovoltaics Access

aims: From the perspective of improving the economic benefit of the distribution network and ensuring the safe operation of the distribution network, the medium and low voltage optimization model with the goal of reducing the operation cost is established. background: Double carbon background and new energy power system policy, a large number of distributed photovoltaic access distribution network caused a great influence, not only on the low voltage distribution network user side power quality impact, photovoltaic full hair also can happen when tide back phenomenon, for the stable operation of the medium voltage distribution network and scheduling, therefore, this paper puts forward a scheduling of the voltage distribution network and low voltage distribution network optimization method, to solve the problems of large-scale distributed photovoltaic access. objective: Reducing operating costs and ensuring the stable and safe operation of the distributed network. method: The optimization problem of medium and low voltage distribution network is decomposed into the main optimization problems of medium voltage distribution network and the sub-problems of optimizing the low-voltage distribution network. An optimal solution is obtained by coordinating the boundary coupling variables between the two problems and solving them iteratively. Finally, the effectiveness of the coordination algorithm is verified by simulating the medium and low voltage distribution network. result: A coordinated optimization method for medium and low voltage distribution network with photovoltaics access. conclusion: It reduces the operation cost, ensures the stable and safe operation of the power grid, thus improving the economic benefits. other: None

Optimizing Micro Friction Stir Spot Welding (mFSSW) of Aluminum Alloy AA1100 Using Neural Network Model

This study explores the optimization of Micro Friction Stir Spot Welding (mFSSW) by investigating the influence of tool profiles on welding outcomes, using aluminum alloy AA1100 with a 0.42 mm thickness as the specimen material. Monitoring temperature and RPM during welding with thermocouples and tachometers, mechanical properties are assessed through tensile shear tests, microhardness measurements, and macrostructural observations. The findings serve as the basis for developing Neural Network models using Rapidminer software, marking a transformative development that positions Neural Networks as potent tools for optimizing welding processes, potentially leading to achieving optimal weld quality. The investigation also delves into three welding tool configurations – the two-stage pin, one-stage pin, and pinless mFSSW probes – highlighting their distinct impacts on tensile shear test values and overall welding quality. Notably, the two-stage pin configuration emphasizes the significance of larger pin diameters and controlled heat generation for enhanced weld strength, while the one-stage pin configuration underscores the pivotal role of pin diameter and elevated temperatures in improving weld quality. The pinless mFSSW probe configuration, on the other hand, emphasizes the importance of shoulder diameter and temperature control for superior tensile shear test results. Leveraging Neural Network modeling for optimization, this study advances our understanding of parameter interactions and underscores the efficacy of Neural Networks in achieving superior tensile shear test values and welding quality in mFSSW, offering valuable insights for future endeavors in the field..

Estimating soil moisture content in citrus orchards using multi-temporal sentinel-1A data-based LSTM and PSO-LSTM models

Soil moisture content is a vital variable in agricultural, hydrological, ecological and climatological processes. However, susceptible to soil type, soil structure, topography, vegetation and human activities, soil moisture content exhibits strong spatial heterogeneity in spatial distribution, which makes it difficult to accurately estimate the soil moisture content distribution information at a large scale using conventional methods. To solve the problem, this study proposed a novel hybrid model (PSO-LSTM) based on the particle swarm optimization (PSO) and long short-term memory (LSTM) network model to accurately predict soil moisture content at a large scale. Five different input combinations were constructed based on the vertical polarization (VV) and cross-polarization (VH) of multi-phase Sentinel-1A data, and the soil moisture content at depths of 5 cm, 10 cm, 20 cm and 40 cm in citrus orchards were estimated using the standalone LSTM and hybrid PSO-LSTM models. The results showed that the estimation accuracy of the hybrid PSO-LSTM model was greater than that of the standalone LSTM model at different depths, with the normalized root mean square error (NRMSE) of 4.568–11.023 % and 18.056–30.156 %, respectively. With the VV polarization as the only inputs, the PSO-LSTM model obtained high prediction accuracy, with the normalized root mean square error (NRMSE) of 5.458–10.125 %, respectively. Therefore, the PSO-LSTM model with VV polarization input was recommended to estimate the soil moisture content at different depths in citrus orchards, which provides important data for decision-making on distributed precision irrigation at a large scale.

Multi-Objective Optimization for Seismic Structural Investigation and Maintenance Strategies of Bridges in Service

With the continuous updating of seismic design codes for Bridges, the new regulations have higher requirements for the seismic structure and mechanical properties of old Bridges. In order to make the decision of seismic structure strengthening more scientifically, with the goal of optimal overall performance, minimum economic cost and minimum environmental impact, the optimal model of seismic strengthening sustainable strategy is established. Firstly, according to the results of the seismic measures of the existing Bridges, the corresponding reinforcement measures are proposed for the structural problems that do not meet the seismic measures. Then the utility evaluation of the three objectives of the existing bridge was carried out. At last, NSGA-Ⅱ algorithm was used to build a multi-objective maintenance decision optimization model of the bridge network. Combined with the weight of the decision-maker's preference, TOPSIS algorithm was used to obtain the optimal solution of the target, and decision-making suggestions were provided for the relevant departments. The results show that there are many structural problems in the new evaluation of the old bridge, and the maintenance of the structural problems are more feasible and effective than the improvement of the traditional mechanical properties. Combined with the comprehensive evaluation utility and reinforcement objective function, the maintenance decision can be better under different objectives. The reinforcement maintenance decision model has the ability to improve the comprehensive performance and sustainability of the bridge to the maximum extent within the limited budget.

Static Equivalence Method of Power Grid Based on Genetic Algorithm

When the electromagnetic transient simulation of a large-scale power grid is carried out, because the simulation is limited by the scale of simulation software, it is necessary to divide the grid into internal and external grids, and the external grid is modeled with equivalent simplification. Aiming at resolving the difficulties of the traditional grid equivalence method, such as a cumbersome calculation process and harsh calculation conditions, a static grid equivalence method based on the genetic algorithm is proposed in this paper. The method first constructs an external grid equivalence network, which includes coupling branches between boundary nodes, and each boundary node is connected to the external grid equivalence power supply through the branches. Then, the external grid equivalence model is used to establish an optimization model for solving the equivalence network parameters based on the information of the internal network, the difference of the power input from the external grid to the boundary nodes before and after equivalence is used as the objective function, and the genetic algorithm is used to realize the iterative optimization of the objective function to obtain a set of optimal post-equivalence network model parameters so that the state variables of the internal power grid before and after the equivalence are matched. The accuracy and effectiveness of the proposed method are verified through simulation with the CERPI36V7 node system.

A robust optimization model for multi-objective blood supply chain network considering scenario analysis under uncertainty: a multi-objective approach

Annually, different regions of the world are affected by natural disasters such as floods and earthquakes, resulting in significant loss of lives and financial resources. These events necessitate rescue operations, including the provision and distribution of relief items like food and clothing. One of the most critical challenges in such crises is meeting the blood requirement, as an efficient and reliable blood supply chain is indispensable. The perishable nature of blood precludes the establishment of a reserve stock, making it essential to minimize shortages through effective approaches and designs. In this study, we develop a mathematical programming model to optimize supply chains in post-crisis scenarios using multiple objectives. Presented model allocates blood to various demand facilities based on their quantity and location, considering potential situations. We employ real data from a case study in Iran and a robust optimization approach to address the issue. The study identifies blood donation centers and medical facilities, as well as the number and locations of new facilities needed. We also conduct scenario analysis to enhance the realism of presented approach. Presented research demonstrates that with proper management, crises of this nature can be handled with minimal expense and deficiency.

Optimization Model of Emergency Evacuation Network Based on Genetic Algorithm

Aggregation and dispersion are the normal states. In economic production and social life, the evacuation of people and materials deserves extensive attention. From the perspective of mathematics and economics, arranging a thoughtful evacuation is essential for the best balance between personal satisfaction and cost. In this paper, evacuation is deeply studied by a genetic algorithm; the balance, cost, and willingness (satisfaction) priority models, which consider both satisfaction and cost, are established. In this paper, based on the field investigation and questionnaire survey conducted by Oriental Sports Center, overall satisfaction is inferred by the satisfaction of each person in the tested sample, and the time cost from the evacuation point to different reception points is inferred through actual investigation. Based on these data, a balance model is established, and the calculation is performed. The ratio of the total willingness value of the evacuated individuals to the total cost is taken as the function of the optimization objective. When the optimization objective function is complex, the genetic algorithm is used to search for the optimal solution by simulating the process of natural evolution. At the same time, the cost priority and willingness priority models are established to compare the advantages and disadvantages of different evacuation schemes and their applicable situations. The evacuation of Oriental Sports Center is studied using a genetic algorithm. The objective function value of equilibrium model optimization is 0.2155 (optimal solution), significantly different from willingness priority model optimization (0.0300). After calculating the optimization objective function value and cost value under the conditions of cost priority and willingness priority, a “People-oriented and cost-balanced” suggestion is put forward for a large crowd evacuation scheme.

A constrained constructive optimization model of branching arteriolar networks in rat skeletal muscle.

Blood flow regulation within the microvasculature reflects a complex interaction of regulatory mechanisms and varies spatially and temporally according to conditions such as metabolism, growth, injury, and disease. Understanding the role of microvascular flow distributions across conditions is of interest to investigators spanning multiple disciplines; however, data collection within networks can be labor-intensive and challenging due to limited resolution. To overcome these experimental challenges, computational network models that can accurately simulate vascular behavior are highly beneficial. Constrained constructive optimization (CCO) is a commonly used algorithm for vascular simulation, particularly well known for its adaptability toward vascular modeling across tissues. The present work demonstrates an implementation of CCO aimed to simulate a branching arteriolar microvasculature in healthy skeletal muscle, validated against literature including comprehensive rat gluteus maximus vasculature datasets, and reviews a list of user-specified adjustable model parameters to understand how their variability affects the simulated networks. Network geometric properties, including mean element diameters, lengths, and numbers of bifurcations per order, Horton's law ratios, and fractal dimension, demonstrate good validation once model parameters are adjusted to experimental data. This model successfully demonstrates hemodynamic properties such as Murray's law and the network Fahraeus effect. Application of centrifugal and Strahler ordering schemes results in divergent descriptions of identical simulated networks. This work introduces a novel CCO-based model focused on generating branching skeletal muscle microvascular arteriolar networks based on adjustable model parameters, thus making it a valuable tool for investigations into skeletal muscle microvascular structure and tissue perfusion.NEW & NOTEWORTHY The present work introduces a CCO-based algorithm for generating branching arteriolar networks, with adjustable model parameters to enable modeling in varying skeletal muscle tissues. The geometric and hemodynamic parameters of the generated networks have been comprehensively validated using experimental data collected previously in-house and from literature. This is one of few validated CCO-based models to specialize in skeletal muscle microvasculature and acts as a beneficial tool for investigating the microvasculature for hypothesis testing and validation.