Multivariable Optimization Algorithm Research Articles

Efficient Local Coherent Structure Learning via Self-Evolution Bipartite Graph.

Dimensionality reduction (DR) targets to learn low-dimensional representations for improving discriminability of data, which is essential for many downstream machine learning tasks, such as image classification, information clustering, etc. Non-Gaussian issue as a long-standing challenge brings many obstacles to the applications of DR methods that established on Gaussian assumption. The mainstream way to address above issue is to explore the local structure of data via graph learning technique, the methods based on which however suffer from a common weakness, that is, exploring locality through pairwise points causes the optimal graph and subspace are difficult to be found, degrades the performance of downstream tasks, and also increases the computation complexity. In this article, we first propose a novel self-evolution bipartite graph (SEBG) that uses anchor points as the landmark of subclasses, and learns anchor-based rather than pairwise relationships for improving the efficiency of locality exploration. In addition, we develop an efficient local coherent structure learning (ELCS) algorithm based on SEBG, which possesses the ability of updating the edges of graph in learned subspace automatically. Finally, we also provide a multivariable iterative optimization algorithm to solve proposed problem with strict theoretical proofs. Extensive experiments have verified the superiorities of the proposed method compared to related SOTA methods in terms of performance and efficiency on several real-world benchmarks and large-scale image datasets with deep features.

A numerical determination of complex solid gun propellant burn rates through closed bomb simulation

AbstractClosed bomb testing is a prominent means of characterizing the combustion behavior of solid gun propellants. This sub‐scale test allows the propellant to burn in a constant volume environment, where the resulting pressure‐time trace can be collected via a pressure transducer. Historically, numerical procedures have been developed to determine the burn rates of the gun propellants from these pressure‐time traces; however, no standardized procedure exists to determine the burn rates of grains with variable surface thermochemistry and ignition criteria. To address this capability gap, a non‐linearly constrained, multivariate optimization algorithm has been developed to decouple propellant grain surfaces and determine surface‐specific burn rates [1]. In this work, the optimization algorithm as well as the legacy Excel‐based Closed Bomb (XLCB) program [2] were used to determine the burn rates of homogeneous, deterred, and layered propellants from experimental data. Closed bomb simulations using these burn rates were then conducted with the two‐phase, multidimensional, interior ballistics solver, iBallistix [3]. The maximum mean error between the simulated and experimental pressure‐time curves was 6.8 % for the optimization algorithm and 23.8 % for XLCB, showing a marked improvement with our new approach. Furthermore, the approach discussed herein improves burn rate predictions of complex solid gun propellants when compared with legacy closed bomb data reduction analysis programs.

Construction of new energy consumption optimization model based on improved pathfinder algorithm

As traditional energy reserves continue to decline, the importance of new energy sources increases. However, the current traditional power system often fails to consider new energy sources, particularly in power supply systems that integrate multiple new energy sources. The cost, efficiency, and environmental factors seriously affect the energy system’s efficiency. Therefore, this proposal presents a multi-objective optimization discrete assignment pathfinder algorithm. The algorithm can handle multi-objective optimization problems and adapt to various constraints, providing a more precise optimization scheme for new energy systems. The experimental results indicated that the proposed research method exhibits better performance compared to other algorithms of the same type. Compared with the multi-objective multivariate universe optimization algorithm and the multi-objective sparrow search algorithm, the research method was ahead in terms of fitness value by 9.54% and 14.67%, respectively. Meanwhile, in the grid simulation, the research method achieved an average efficiency of 96.16%, which is better than the comparative algorithms by 6.57–14.02%. The study not only improves the optimization efficiency of new energy consumption, but also provides a powerful decision support tool for the planning and operation of wind farms. It is of great significance for the improvement of power system efficiency and decarbonization, and helps to promote the large-scale integration and sustainable development of new energy.

Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic.

Vehicle malfunctions have a direct impact on both human and road safety, making vehicle network security an important and critical challenge. Vehicular ad hoc networks (VANETs) have grown to be indispensable in recent years for enabling intelligent transport systems, guaranteeing traffic safety, and averting collisions. However, because of numerous types of assaults, such as Distributed Denial of Service (DDoS) and Denial of Service (DoS), VANETs have significant difficulties. A powerful Network Intrusion Detection System (NIDS) powered by Artificial Intelligence (AI) is required to overcome these security issues. This research presents an innovative method for creating an AI-based NIDS that uses Deep Learning methods. The suggested model specifically incorporates the Self Attention-Based Bidirectional Long Short-Term Memory (SA-BiLSTM) for classification and the Cascaded Convolution Neural Network (CCNN) for learning high-level features. The Multi-variant Gradient-Based Optimization algorithm (MV-GBO) is applied to improve CCNN and SA-BiLSTM further to enhance the model's performance. Additionally, information gained using MV-GBO-based feature extraction is employed to enhance feature learning. The effectiveness of the proposed model is evaluated on reliable datasets such as KDD-CUP99, ToN-IoT, and VeReMi, which are utilized on the MATLAB platform. The proposed model achieved 99% accuracy on all the datasets.

A novel methodology to assist manufacturers in the heat exchanger sizing for variable-speed heat pumps based on part load conditions and economic assessment

Matts Bäckstöm assessed the selection criteria of heat exchangers for single-speed heat pumps in 1940, and his methodology and conclusions have been kept practically unchanged for almost a century. However, heat pump systems have evolved, especially with the introduction of variable-speed heat pumps. These new systems introduced a new degree of freedom to the heat pump design – the compressor speed – which is not considered by traditional design criteria.This study proposes novel design criteria to optimize the heat exchanger size when the heat pump unit can work under part-load conditions. The proposed method models how heat exchangers and variable-speed compressors perform under different loads and sizes. It considers part-load requirements based on climatic data (which are typically available in the standards) and considers economic factors such as initial investment and operation costs (which are dependent on the components's size). Given the mentioned information, a multivariable optimization algorithm is implemented to find the optimum heat exchanger size that minimizes the total cost. To validate the proposed approach's effectiveness, it was exemplified by determining the optimum size of a coil and a plate heat exchanger for a domestic variable-speed heat pump. The results demonstrate that using traditional criteria could lead to over-dimensioning heat exchangers in variable-speed heat pumps which could incur in an increase of costs of 5% for the studied case.

Experimental study on kinetic oxidation of graphite IG-110 by steam

Graphite is proposed for use in High-temperature Gas-cooled Reactors (HTGRs) as the fuel matrix, neutron moderator/reflector, and core structural material. One important property of nuclear grade graphite is their resistance to oxidation in high-temperature environment. Extensive investigation has been performed in the literature for graphite oxidation by air. However, available experimental data are still limited for graphite oxidation by steam under conditions comparable to a postulated steam ingress accident in HTGRs. In this study, the oxidation rate of graphite IG-110 by steam in helium flow was measured at temperatures from 850 to 1100 °C with the steam partial pressure varying from 0.5 to 20.0 kPa and the hydrogen partial pressure varying from 0 to 2.0 kPa. Further analysis confirms the oxidation process in this present study is dominated by the chemical kinetics, which lends credit to the data for being used to develop numerical models. It was observed that the increase of the kinetic oxidation rate with the steam partial pressure tends to become less apparent if the steam partial pressure keeps increasing. In addition, it was found that the partitioning of hydrogen inhibits the graphite-steam reaction process even with the steam partial pressure up to 20.0 kPa. However, this inhibiting effect starts to become saturated when the hydrogen partial pressure exceeds 1.0 kPa. The oxidation rates were fitted to the conventional Langmuir-Hinshelwood (LH) and Boltzmann-enhanced Langmuir-Hinshelwood (BLH) models by a multivariable optimization algorithm. The BLH model exhibits a better accuracy than the LH model within the specified experimental conditions. The predicted oxidation rate using the BLH model shows a mean relative difference of about 24% with the maximum difference of about 55% when compared with our experimental data.

A Review on the Preliminary Design of Axial and Radial Turbines for Small-Scale Organic Rankine Cycle

Organic Rankine cycle (ORC) is an effective technology to harness low-grade energy. Turbine, as a key component of ORC, takes advantages of its high efficiency and compact size compared with other expanders. Currently, developing suitable turbines with a high performance and a low cost is one of the bottlenecks for wide applications of various ORCs. In this context, technical progress on radial inflow turbines (RITs), axial turbines (ATs), and radial outflow turbines (ROTs) is introduced, and loss models used in the preliminary design are compared, especially for small-scale ORCs. RIT is recommended for medium and small ORCs with an expansion pressure ratio of <10. The power outs and rotational speeds of the designed RITs spanned the ranges of 9.3–684 kW and 3000–114,000 r/min with an efficiency of 56.1–91.75%. In comparison, the power outputs and speeds of ATs were 3–2446 kW and 3000–91,800 r/min with an efficiency of 63–89.1%. AT is suitable for large-scale ORCs with a power output of greater than hundreds of kW. However, AT with impulse stages is feasible for small-scale ORCs when the pressure ratio is high, and the mass flow rate is small. The power outputs of the designed ROTs were relatively small, at 10–400 kW with a speed of 7200–42,700 r/min and an efficiency of 68.7–85%. For organic working fluids with a large expansion pressure ratio, ROT might be employed. Conventional mean-line models may neglect the effects of supersonic flow, which will be encountered in many ORC turbines. Therefore, adequate models for supersonic expansion loss and shock loss need to be added. Meanwhile, a proper multivariable optimization algorithm such as a gradient-based or stochastic search method should be selected. Finally, the challenges and potential research directions are discussed. The outcomes can provide some insights for the development of ORC turbines and the optimization of ORC systems.

Development of rutting forecasting models for distinct asphalt pavement structures in RIOH testing track using different approaches

Rutting is one of the main exacerbation phenomena of the asphalt pavements, which seriously affects road safety and the quality of service. The predicted models are indispensable for preventing and controlling the damage caused by the exacerbation of pavement management systems. This paper is centered on developing more appropriate and exact rutting forecasting models for estimating the rutting performance of distinct asphalt pavement structures on the basis of the field data set of the Research Institute of Highway (RIOH) testing track. First of all, the critical influential factors giving rise to the rutting evolution are identified through analyzing the field data set of the RIOH testing track. After that, the framework of the rutting forecasting models based on the artificial neural networks (ANNs) coupled with genetic algorithm (GA) (GA-ANNs-based rutting forecasting models), the frameworks of the experiential and mechanics-based experiential rutting forecasting models are established according to the implicit relationship between the rutting and the key influential factors. Then, the unknown parameters of the experiential and mechanics-based experiential rutting forecasting models for different asphalt pavement structures are designed by the multivariate nonlinear optimization algorithm. Finally, the applicability of the GA-ANNs-based rutting forecasting models, the experiential and mechanics-based experiential rutting forecasting models are discussed and illustrated through analyzing their forecasting effects. The result illustrates that the proposed rutting forecasting models have better applicability for different asphalt pavement structures (i.e., the semi-rigid base pavement structure, the rigid base pavement structure, the flexible base pavement structure, and the full depth AC pavement structure).

Gradient Approximation and Multivariable Derivative-Free Optimization Based on Noncommutative Maps

In this article, multivariable derivative-free optimization algorithms for unconstrained optimization problems are developed. A novel procedure for approximating the gradient of multivariable objective functions based on noncommutative maps is introduced. The procedure is based on the construction of an exploration sequence to specify where the objective function is evaluated and the definition of so-called gradient generating functions which are composed with the objective function, such that the procedure mimics a gradient descent algorithm. Various theoretical properties of the proposed class of algorithms are investigated and numerical examples are presented.

Simulation and Optimization of Ultrasonic Transducer

The ultrasonic transducers have numerous applications in industries, including medical probes for performing ultrasound scans. One of the significant drawbacks of the ultrasonic transducer is the wastage of a large portion of energy, due to high acoustic impedance, while transmitting ultrasonic waves to the target object. The present study is aimed to investigate the material design of the piezo-composite transducer and improve its performance. Different piezo-composite transducers were simulated in the COMSOL environment by varying input parameters, and three key performance indicators (KPI) were calculated. Many constraint-based multivariable optimization algorithms have been used to maximize the KPIs. A set of parameters, such as Sensitivity and Fractional Bandwidth, have been found to increase the performance of piezo-composite transducer model and its overall efficiency. This study is intended to impinge unidirectional property to the transducer which is found to be beneficial in more accurate medical as well as structural reports and cost savings.

Design and Development of a Solar Electric Delivery Pod

The ultrasonic transducers have numerous applications in industries, including medical probes for performing ultrasound scans. One of the significant drawbacks of the ultrasonic transducer is the wastage of a large portion of energy, due to high acoustic impedance, while transmitting ultrasonic waves to the target object. The present study is aimed to investigate the material design of the piezo-composite transducer and improve its performance. Different piezo-composite transducers were simulated in the COMSOL environment by varying input parameters, and three key performance indicators (KPI) were calculated. Many constraint-based multivariable optimization algorithms have been used to maximize the KPIs. A set of parameters, such as Sensitivity and Fractional Bandwidth, have been found to increase the performance of piezo-composite transducer model and its overall efficiency. This study is intended to impinge unidirectional property to the transducer which is found to be beneficial in more accurate medical as well as structural reports and cost savings.

Performance Analysis and Optimization of a Cooling System for Hybrid Solar Panels Based on Climatic Conditions of Islamabad, Pakistan

The unconvertible portion of incident radiation on solar panels causes an increase in their temperature and a decrease in efficiency due to the negative temperature coefficient of the maximum power. This problem is dealt with through the use of cooling systems to lower the temperature of photovoltaic (PV) panels. However, the developments are focused on the loss of efficiency or extract the heat out of the solar panel, rather than optimizing the solution to produce a net gain in the electric power output. Therefore, this study proposes the analytical model for the cell temperature, irradiance and design of absorbers. Furthermore, the cooling systems for the hybrid solar panels were developed through analytical modeling of the solar cell temperature behavior and heat exchange between the fluid and back surface of the PV module in MATLAB. The design parameters such as mass flow rate, input power, solar cell temperature, velocity, height, number of passes and maximum power output were optimized through a multi-objective, multivariable optimization algorithm to produce a net gain in the electrical power. Three layouts of heat absorbers were considered—i.e., single-pass ducts, multi-pass ducts, and tube-type heat absorbers. Water was selected as a cooling medium in the three layouts. The optimized results were achieved for the multi-pass duct with 31 passes that delivered a maximum power output of 186.713 W at a mass flow rate of 0.14 kg/s. The maximum cell temperature achieved for this configuration was 38.810 °C at a velocity of 0.092 m/s. The results from the analytical modeling were validated through two-way fluid-solid interaction simulations using ANSYS fluent and thermal modules. Analyses revealed that the multi-pass heat absorber reduces the cell temperature with the least input power and lowest fluid mass flow rate to produce the highest power output in the hybrid PV system.

Performance Enhancement of Inclined Core-Shell Nanowire Solar Cells Using Multivariable Optimization

This paper proposes an optimized high-performance nanowire array solar cell. Three approaches have contributed to achieving the highest possible performance of nanowire solar cells. Inclining the nanowires, adding cores to the nanowires, and finding the optimized design parameters such as inclination angle, core diameter, nanowire length, and the fill factor. Si and GaAs have been used as the core–shell material respectively. A multivariable particle swarm optimization algorithm has been used with the objective of maximizing the efficiency and the nanowire solar cell current density. We could achieve a current density of 58.59 mA/cm2 and a power conversion efficiency (PCE) of 33.52%. The performance parameters of the optimized design such as the absorption, the distribution of the electric field, current density, and efficiency have been calculated and compared with non-optimized structure and other works. According to the obtained results, the superiority of the proposed optimized nanowire solar cell over other designs has been verified.

Dynamic multivariate threshold optimization and alarming for nonstationary processes subject to varying conditions

For the alarm system of nonstationary industrial processes, the conventional alarm thresholds configured for one single operational zone often result in frequent false and missed alarms. Besides, these univariate thresholds neglect interactions among process variables. To address these issues, this paper proposes a novel dynamic multivariate alarm threshold optimization algorithm for nonstationary processes subject to varying conditions. Firstly, the process correlation variations can be identified by Toeplitz inverse covariance-based clustering method, pointing to the changes of operating conditions. Each condition can be structurally interpreted by an inverse covariance matrix of the multivariate Gaussian distribution, revealing similar within-time and cross-time variable interactions. Therefore, it provides a promising foundation to capture the conditional Gaussian distribution and design the corresponding thresholds of each variable, which finely covers the current normal operational zone. Then, offline threshold optimization and online alarming strategy are developed and discussed in detail, which can timely adapt the model to varying conditions, promoting accurate and sensitive alarming performance. Finally, the validity of the proposed threshold is demonstrated on both continuous and batch processes with typical nonstationary characteristics. Results show that the proposed threshold can effectively adapt to varying conditions and aggregate multivariate information, thus reducing nuisances and ensuring the reliability of the alarm system as well.

Multivariate Adaptive Step Fruit Fly Optimization Algorithm Optimized Generalized Regression Neural Network for Short-Term Power Load Forecasting

Short-term load forecasting plays a significant role in the management of power plants. In this paper, we propose a multivariate adaptive step fruit fly optimization algorithm (MAFOA) to optimize the smoothing parameter of the generalized regression neural network (GRNN) in the short-term power load forecasting. In addition, due to the substantial impact of some external factors including temperature, weather types, and date types on the short-term power load, we take these factors into account and propose an efficient interval partition technique to handle the unstructured data. To verify the performance of MAFOA-GRNN, the power load data are used for empirical analysis in Wuhan City, China. The empirical results demonstrate that the forecasting accuracy of the MAFOA applied to the GRNN outperforms the benchmark methods.

A Global Optimization Algorithm for Unconstrained Non-linear Optimization Problems Using Chaotic Neuron Map and Golden Number

This paper presents aninnovative global multi-variable optimization algorithm using one of the best chaotic sequences, the neuron map, a description of which is also provided in the paper. The algorithm uses neuron map in the first stage to move near the global minimum point, as well as in each iteration of the second stage of local search that is done using the N-dimensional golden section search algorithm. The generation and mapping of the neuron variables to the optimization variables along with the stagewise search for the global minimum is explained conscientiously in the work. Numerical results on some benchmark functions and the comparison with a latest state-of-the-art algorithm ispresented in order to demonstrate the efficiency of the proposed algorithm.

Multi-Variable Multi-Objective Optimization Algorithm for Optimal Design of PMa-SynRM for Electric Bicycle Traction Motor

In this paper, internal division point genetic algorithm (IDP-GA) was proposed to lessen the computational burden of multi-variable multi-objective optimization problem using finite element analysis such as optimal design of electric bicycles. The IDP-GA could consider various objectives with normalized weighted sum method and could reduce the number of function calls with novel crossover strategy and vector-based pattern search method. The superiority of the proposed algorithm was verified by comparing performances with conventional optimization method at two mathematical test functions. Finally, the applicability of the IDP-GA in practical electric machine design was verified by successfully deriving an improved design of electric bicycle propulsion motor.

Peak load minimization of an e-bus depot: impacts of user-set conditions in optimization algorithms

The transport sector is responsible for 25% of global CO2 emissions. To reduce emissions in the EU, a shift from the currently 745,000 operating public buses to electric buses (EBs) is expected in the coming years. Large-scale deployments of EBs and the electrification of bus depots will have a considerable impact on the local electric grid, potentially creating network congestion problems and spikes in the local energy load. In this work, we implement an exact, offline, modular multi-variable mixed-integer linear optimization algorithm to minimize the daily power load profile peak and optimally plan an electric bus depot. The algorithm accepts a bus depot schedule as input, and depending on the user input on optimization conditions, accounts for varying time granularity, preemption of the charging phase, vehicle-to-grid (V2G) charging capabilities and varying fleet size. The primary objective of this work is the analysis of the impact of each of these input conditions on the resulting minimized peak load. The results show that our optimization algorithm can reduce peak load by 83% on average. Time granularity and V2G have the greatest impact on peak reduction, whereas preemption and fleet splitting have the greatest impact on the computational time but an insignificant impact on peak reduction. The results bear relevance for mobility planners to account for innovative fleet management options. Depot infrastructure costs can be minimized by optimally sizing the infrastructure needs, by relying on split-fleet management or V2G options.

Modal decomposition of fiber modes based on direct far-field measurements at two different distances with a multi-variable optimization algorithm.

We present a novel method for modal decomposition of a composite beam guided by a large-mode-area fiber by means of direct far-field pattern measurements with a multi-variable optimization algorithm. For reconstructing far-field patterns, we use finite-number bases of Hermite Gaussian modes that can be converted from all the guided modes in the given fiber and exploit a stochastic parallel gradient descent (SPGD)-based multi-variable optimization algorithm equipped with the D4σ technique in order for completing the modal decomposition with compensating the centroid mismatch between the measured and reconstructed beams. We measure the beam intensity profiles at two different distances, which justifies the uniqueness of the solution obtained by the SPGD algorithm. We verify the feasibility and effectiveness of the proposed method both numerically and experimentally. We have found that the fractional error tolerance in terms of the beam intensity overlap could be maintained below 1 × 10-7 and 3.5 × 10-3 in the numerical and experimental demonstrations, respectively. As the modal decomposition is made uniquely and reliably, such a level of the error tolerance could be maintained even for a beam intensity profile measured at a farther distance.

Methodology of developing mathematical-ANN hybrid model based mill setup model for hot strip rolling

Hot strip mill, one of the most important units of an integrated steel plant, is operated by mill setup model. The conventional mill setup models calculate thermal, reduction and speed schedules of the material being rolled using mathematical models derived from fundamental principles of heat transfer and plastic deformation. However, such mill setup models often compute inaccurate schedules leading to quality issues and operational problems. This paper describes a novel technique of developing a hybrid model by integrating mathematical models with artificial neural network (ANN) model. The trained hybrid models use a multivariable optimization algorithm to calculate the thermal, reduction and speed schedules during hot strip rolling. More than six hundred coils were successfully rolled in an industrial hot strip mill using the mill setup model developed under the present work. It is found that the mill setup model developed using the hybrid models is more accurate and faster than the mill setup models that use conventional mathematical models.