Efficient Optimization Method Research Articles

An Efficient Geometric Parameter Optimization Method for Microstrip Bandpass Filter Based on One‐Dimensional Convolutional Neural Network

Machine learning‐assisted electromagnetic simulation has become an effective acceleration tool for designing microwave components by introducing high‐precision models and optimization algorithms, featuring fast design and high efficiency. However, enormous amount of data generated from the blind preliminary and computationally expensive simulation is required to predict the accuracy response. An efficient geometric parameter optimization method for microstrip bandpass filter (BPF) based on a one‐dimensional convolutional neural network is proposed. Nonlinear convergence factor, adaptive weight, and Gaussian difference mutation strategies are integrated using the whale optimization algorithm to avoid the local optimum and improve optimization accuracy. Computational efficiency is improved significantly with small‐scale training data. The validity and efficiency of the proposed method are confirmed by fifth‐order microstrip BPFs, and the performance of the predicted structure parameters is significantly improved, which shows great promise for application in optimization and performance improvement in microwave electromagnetic simulation.

FINC: An Efficient and Effective Optimization Method for Normalized Cut.

The optimization methods for solving the normalized cut model usually involve three steps, i.e., problem relaxation, problem solving and post-processing. However, these methods are problematic in both performance since they do not directly solve the original problem, and efficiency since they usually depend on the time-consuming eigendecomposition and k-means (or spectral rotation) for post-processing. In this paper, we propose a fast optimization method to speedup the classical normalized cut clustering process, in which an auxiliary variable is introduced and alternatively updated along with the cluster indicator matrix. The new method is faster than the conventional three-step optimization methods since it solves the normalized cut problem in one step. Theoretical analysis reveals that the new method is able to monotonically decrease the normalized cut objective function and converge in finite iterations. Moreover, we have proposed efficient methods for adjust two regularization parameters. Extensive experimental results show the superior performance of the new method. Moreover, it is faster than the existing methods for solving the normalized cut.

A New Gradient-Accelerated Two-Stage Multiobjective Optimization Method for CO2-Alternating-Water Injection in an Oil Reservoir

Summary CO2-water-alternating-gas (CO2-WAG) is a reservoir development method that can simultaneously enhance oil recovery and achieve CO2 storage. However, improperly designed parameters for CO2 injection and oil production may significantly reduce the oil displacement efficiency and CO2 storage. Furthermore, optimizing the injection parameters is computationally expensive due to the high computational cost of the compositional simulation. This work aims to propose an efficient optimization method to obtain a series of well-control schemes that balance maximizing net present value (NPV) and CO2 storage for decision-makers. Given the number of CO2-WAG cycles and the duration of each cycle, we optimize the water injection rate, gas injection rate, and half-cycle for the injection well and the bottomhole pressure (BHP) for the production well. In this paper, a two-stage optimization strategy is proposed to enhance the optimization efficiency. The first stage performs the surrogate-assisted single-objective optimizations of each considered objective. It is designed to find the endpoints of the Pareto front that connect all solutions of the multiobjective optimization; this stage not only provides important search directions for the subsequent multiobjective optimization but also improves the accuracy of the surrogate model near the Pareto front. The second stage is the surrogate-assisted multiobjective optimization, which aims to find all the solutions along the Pareto front based on the Pareto endpoints obtained from the first stage. In addition, this study successfully combines the gradient of the objective functions with the meta-heuristic algorithm during the multiobjective optimization, which ensures a faster convergence to the global optimum. The proposed multiobjective optimization algorithm shows faster convergence than the conventional optimization methods when applied to the three multiobjective optimization test functions. Finally, a comparison with the conventional multiobjective optimization is conducted based on one test function and two benchmark reservoir simulation models to verify the correctness and efficiency of the proposed method. It is confirmed that the proposed method outperforms the conventional ones for the optimization of CO2-WAG injection.

An adaptive dual‐controller on‐line efficiency optimization method of four‐switch buck‐boost converter for wide range application

SummaryIn order to achieve efficiency extremum of power conversion independent of parameter values and operational mode, an adaptive dual‐controller on‐line efficiency optimization method is presented in this paper. Firstly, based on tri‐modal analysis for four‐switch buck‐boost (FSBB) converter, efficiency extremum analysis is given and existed for such this converter. Then, adaptive dual controller on‐line efficiency seeking algorithm is shown to adaptive two variables for efficiency optimization and output voltage regulation. Furthermore, perturb and observe detector is described to realize control variables decoupling of dual‐controller and to prevent the instability of the whole system during the algorithm tuning process and to improve the algorithm speed of the dual controller. The proposed method is model independent and does not depend on the circuit parameter values. The detail implement of algorithm and performance analysis is given, and the experimental platform and experimental results are put forward to verify the effectiveness of the algorithm.

An Efficient Optimal Solution Method for Neutrosophic Transport Models: Analysis, Improvements, and Examples


 Transport issues aim to determine the number of units that will be transferred from the production centers to consumption areas so that the cost of transportation is as low as possible, taking into account the conditions of supply and demand. Due to the great importance of these issues and to obtain more accurate results that take into account all circumstances, we conducted two research studies. In the first research, we presented a formulation of neutrosophic transport issues, and in the second research, we presented some ways to find a preliminary solution to these issues, but we do not know whether the preliminary solution is optimal or not, so we will present in this research a study whose purpose is to shed light on some important methods used to improve the optimal solution to transportation issues and then reformulating them using the concepts of neutrosophic science, a science that leaves nothing to chance or circumstances but rather provides solutions with neutrosophic values. Unspecified values take into account the best and worst conditions.

A multi-objective energy efficiency optimization method of ship under different sea conditions

With the increasing need for environmental protection and the related advancement of relevant laws and regulations, ship energy efficiency optimization has become an important research topic. In this study, a multi-objective optimization model of ship energy efficiency based on the multi-objective particle swarm optimization algorithm is designed. The model uses the main engine speed as the optimization variable, and the ship's fuel consumption and sailing time as optimization objectives. Additionally, considering that the ship's sailing state can be affected by different sea conditions, the sea conditions are clustered by using the K-means clustering algorithm. Then, the route is divided, and the main engine speed is optimized for each segment. The results show that the solutions can reduce the fuel consumption by up to 25.28 % or the sailing time by up to 17.08 %. In the Ideal Solutions can reduce the fuel consumption by up to 3.98% or reduce the sailing time by up to 2.37%. Thus, the proposed model could effectively optimize the ship's energy efficiency. And it is necessary to consider the relationship between fuel consumption and sailing time when identifying the most suitable solution to meet real-world needs.

On the Rod Heating Problem by Sources on the Class of Zonal Controls Using the Current and Past State at Measurement Points

We study an optimal feedback control problem for the rod heating process by means of lumped sources. The control actions are the powers of the sources, the values of which are defined on the class of zonal controls. The values of the parameters of zonal control actions are determined by subsets of the state space, to which belong the values of the process state at the measurement points at the current and past time moments. The posed problem is reduced to a parametric optimal control problem on determining a finite-dimensional vector of values of the parameters of zonal control actions. We have obtained optimality conditions for the values of the parameters of zonal control actions. These conditions contain formulas for the gradient of the objective functional with respect to the optimizable parameters. They make it possible to solve the reduced problem numerically with the application of efficient first-order optimization methods.

Surrogate-based optimization design for surface texture of helical pair in helical hydraulic rotary actuator

A good surface texture design can effectively improve the tribological performance of the helical pair within a helical hydraulic rotary actuator(HHRA). However, the optimization design process can be time-consuming due to the multiple design variables involved and the complexity of the mathematical model. This paper proposes a modified efficient global optimization (MEGO) method for solving such demanding surface texture design challenges. The MEGO utilizes a Kriging model with the optimized Latin hypercube sampling (OLHS) for initial sampling and the proposed modified expected improvement (MEI) function for sequential sampling. A comparative study of several global optimization algorithms with the MEGO on the surface texture design is performed. Subsequently, surrogate-based optimization and parameter analysis are carried out, resulting in the identification of an optimal set of texture parameters. The findings reveal the superiority of the MEGO in both model prediction accuracy and refinement of minima. Moreover, compared to the base design, the friction coefficient can be reduced by up to 45.2%.

Energy efficiency optimisation in massive multiple‐input, multiple‐output network for 5G applications using new quantum genetic algorithm

AbstractDevising efficient optimisation methods has been a subject of great research attention since current evolving trends in communication networks, machine learning, and other cutting‐edge systems that need a fast and accurate optimised computational model. Classical computers became incapable of handling new optimisation problems posed by newly emerging trends. Quantum optimisation algorithms appear as alternative solutions. The existing bottleneck that restricts the use of the newly developed quantum strategies is the limited qubit size of the available quantum computers (the size of the most recent universal quantum computer is 433 qubits). A new quantum genetic algorithm (QGA) is proposed that handles the presented problem. A quantum extreme value searching algorithm and quantum blind computing framework are utilised to extend the search capabilities of the GA. The quantum genetic strategy is exploited to maximise energy efficiency at full spectral efficiency of massive multiple‐input, multiple‐output (M‐MIMO) technology as a toy example for pointing out the efficiency of the presented quantum strategy. The authors run extensive simulations and prove how the presented quantum method outperforms the existing classical genetic algorithm.

BREAST CANCER DETECTION AND CLASSIFICATION USING HISTOPATHOLOGICAL IMAGES BASED ON OPTIMIZATION-ENABLED DEEP LEARNING

Cancer is the second cause of mortality worldwide and it has been identified as a perilous disease. Among all types of cancers, Breast Cancer (BC) is a substantial research subject in the medical imaging area, because it is a serious disease and primary reason for death in women. Proper diagnosis helps patients to get adequate treatment, enhancing the probability of surviving. Because of the poor contrast and unclear structure of tumor cells in the images, automatic segmenting of breast tumors remains a difficult task. However, the identification and interpretation of breast lesions is challenging even for expert radiologists. To address these limitations, an efficient mechanism for BC detection and classification using histopathological images is proposed, which employs a DenseNet-based Chronological Circle Inspired Optimization Algorithm (CCIOA). Deep Learning (DL) approaches are used in the suggested BC classification scheme to precisely segment and identify the BC. The segmentation is done using ResuNet++, and an efficient optimization method called Invasive Water Ebola Optimization (IWEO) is used to fine-tune the DL network’s parameters. Furthermore, DenseNet is utilized for BC detection, while CCIOA is used for DenseNet training. The CCIOA-DenseNet is evaluated using the metrics of accuracy, True Positive Rate (TPR), and True Negative Rate (TNR). Experiment results show that the CCIOA-DenseNet attained better accuracy of 0.971, TPR of 0.966, and TNR of 0.954.

Mechanical and tribological properties of basalt fiber fabric reinforced polyamide 6 composite laminates with interfacial enhancement by electrostatic self-assembly of graphene oxide

Exploring simple, efficient and applicable interfacial optimisation methods is important to enhance the overall performance of basalt fibre (BF) fabric-reinforced thermoplastic composite laminates. Graphene oxide (GO) was introduced on the surface of BF fabric through electrostatic self-assembly to improve the interfacial bonding of BF fabric-reinforced polyamide 6 (PA6) composites laminates (BF/PA6) for enhancing the mechanical and frictional wear properties of the composites. It was concluded that surface electrostatic assembly of GO resulted in a significant increase in surface roughness, wettability, surface energy and surface chemical activity of BF fabrics without damaging the fibre backbone structure. With the GO concentration of 0.8 g/L, the interlaminar shear strength, impact strength, flexural strength and flexural modulus of the BF/PA6 composites were increased by 85.1 %, 43.2 %, 53.2 % and 63.4 %, and the average friction coefficient and wear rate was reduced by 15.2 % and 40.5 %, respectively. The introduced homogeneous GO strengthens the interfacial bonding of such fibre-fabric reinforced thermoplastic composites by creating mechanical engagement, similar compatibility and chemical cross-linking between the BF fabrics and the PA6 matrix. With enhanced interfacial bonding, the BF fabric reinforced thermoplastic composites possess enhanced stress damage resistance. Under frictional reciprocating stress, the introduced GO inhibited the generation and expansion of microcracks, formed frictional transfer film to play solid lubrication and anti-wear role, and enhanced the thermal stability of the composites to mitigate the frictional thermal damage. The interfacial enhancement method of BF fabric-reinforced thermoplastic composites provided in this study is simple, green, efficient and has good potential for application.

A topology optimization framework for 3D phononic crystals via the method of successive iteration of analysis and design

Topology optimization has become a useful tool for designing Phononic Crystals (PnCs) to achieve desired elastic wave propagation properties. However, topological design of three-dimensional (3D) PnCs remains a significant challenge due to extremely high computational cost required for the repeated solution of the nested double-loop problem of design optimization and eigenvalue analysis to determine the band structure during the optimization iteration process. This paper presents a more efficient topology optimization method based on the concept of successive iteration of analysis and design. The method employs a sequential approximation of the eigenpairs through an inverse iteration-like procedure to avoid solving the expensive eigenvalue problems in each design iteration. To further improve the efficiency of the design iteration, the eigenmodes of the intermediate PnCs designs are first computed on a relatively coarse finite element mesh. Then they are mapped onto the fine mesh and used as the initial trial eigenmodes in the eigenpair analysis and design sensitivity analysis. The high efficiency of the proposed method, as compared with conventional nested double-loop optimization approaches, is demonstrated by 3D numerical examples with over one million degrees of freedom. It is shown that this approach can achieve simultaneous convergence of the eigenvectors along with the design evolution of the unit cell, therefore substantially reduces the computational burden for high-resolution topological design of 3D PnCs.

EMsFEM based concurrent topology optimization method for hierarchical structure with multiple substructures

An efficient concurrent topology optimization method based on the Extended Multiscale Finite Element Method (EMsFEM) is proposed to design the hierarchical structure with multiple substructures in this paper. Firstly, the mathematical description model for the topology of hierarchical structures and the interpolation model for material are established at both the macro and substructural levels. Then the concurrent topology optimization formulation for hierarchical structures is proposed based on EMsFEM. After that, the sensitivity analysis scheme of the objective function and constraint functions is given, based on which the Method of Moving Asymptotes (MMA) is employed to update design variables. The assumption of separation of length scales is not required in the proposed method, which avoids the disconnection between adjacent substructures. Besides, without limiting the topology and spatial distribution of substructures in advance, the concurrent topology optimization for hierarchical structures with multiple substructures can be realized using this method. The provided numerical examples verify the correctness and effectiveness of the proposed sensitivity analysis method as well as the feasibility, effectiveness and scalability of the proposed concurrent topology optimization method for hierarchical structures. Finally, the influence of the initial substructure configurations and the number of substructure types on the final configuration and mechanical performance of hierarchical structures are explored with a series of numerical examples.

An efficient simulation optimization method for the redundancy allocation problem with a chance constraint

We explore the Redundancy Allocation Problem (RAP) under the objective of minimizing the cost of a production system of general topology in which system reliability is treated as a chance constraint. A novel simulation optimization-based solution method grounded in the concepts of the trust region and response surface methodology is proposed to efficiently solve the generalized RAP (GRAP) under random system survival times. The generalizability of the RAP model and efficiency of the solution method allows for our approach to be utilized in a wide variety of real-world applications. We demonstrate in a series of numerical experiments based on production systems of varying complexity that the finite convergence of the proposed method is much more efficient than the commonly-used genetic algorithm. It is shown that on a simple bridge network, only the proposed algorithm can find the true optimal solution to the GRAP under an allotted computational budget. On a complex network which includes series, parallel, and logical relationships, the proposed algorithm is also shown to find solutions to the GRAP which have substantially lower total system cost than those found by GA under a wide variety of scenarios.

Consumer Purchasing Power Prediction of Interest E-Commerce Based on Cost-Sensitive Support Vector Machine

The traditional e-commerce business chain is being reconstructed around the content of short videos and live streams, and the interest e-commerce is thriving as a new trend in the e-commerce industry. Diversified content promotes the rapid development of interest e-commerce. For consumers, their preferences for different content reflect their consumption level to a certain extent. The purpose of this study is to accurately predict the purchasing power level with the consumer content preference, and provide new ideas for interest e-commerce business. In this paper, the new swarm intelligence algorithm is used to find the optimal misclassification cost, and three cost-sensitive models are established. On this basis, the content preference of interest e-commerce consumers is used to predict the level of purchasing power. The results show that the content preference of interest e-commerce consumers, such as “fashion”, “photography” and “interpretation”, have a significant effect on the prediction of purchasing power at the 95% confidence level. The accuracies of the optimized cost-sensitive support vector machine in predicting consumer purchasing power are all above 0.9, and the highest is 0.9792. This study effectively alleviates the problem that the classification results tend to be biased towards negative samples, especially when the imbalanced rate of the sample is high. It not only provides researchers with an efficient parameter optimization method, but also reflects the relationship between consumer content preference and purchasing power, providing data support for interest e-commerce operations.

Stable Improved Dynamic Programming Method: An Efficient and Accurate Method for Optimization of Reservoir Flood Control Operation

Stable Improved Dynamic Programming Method: An Efficient and Accurate Method for Optimization of Reservoir Flood Control Operation

Data-driven surrogate modeling for global sensitivity analysis and the design optimization of medical waste shredding systems

Excessive medical waste is generated in various medical facilities, especially post-Covid. Recently, sterilization-based shredding systems are being widely used to treat medical waste; however, such systems are commonly over-designed, due to load variation among various treatment facilities. To overcome this challenge, a data-driven surrogate model framework is proposed to perform sensitivity analysis and design optimization based on different loading environments and capacity requirements. The stress estimation surrogate model was generated using the Latin hypercube sampling (LHS), which can represent the overall information of the design area with a limited sample. Furthermore, the data-driven model significantly reduced the computational time as increased numbers of samples were generated from the data-driven surrogate model, instead of finite element analysis (FEA). Two distinct design capacities of the shredding system were used for assessing the effectiveness of the present framework. The results demonstrated that surrogate model-based sensitivity analysis is an efficient approach to developing system designs based on various input and output conditions. The proposed approach mitigates the tremendous potential of the surrogate model, significantly reduces computational costs associated with sensitivity analysis, and yields promising accuracy for the optimization process. This method suggests a computationally efficient optimization method for different shredding capacities. Additionally, the proposed method does not remain at simply optimizing the existing system but provides optimization values for various capacity systems using the data of the existing design, therefore it can be applied to any mechanical system for designing an optimized and compact system.

A Derivative-free Trust-region Method for Optimization on the Ellipsoid

Optimization methods play a crucial role in various fields and applications. In some optimization problems, the derivative information of the objective function is unavailable. Such black-box optimization problems need to be solved by derivative-free optimization methods. At the same time, optimization problems with ellipsoidal constraints are important and have widespread applications in various fields as well. Following the development of the late professor M. J. D. Powell’s efficient derivative-free trust-region optimization methods, this paper considers solving derivative-free optimization problems on the ellipsoid. Our new optimization solver EC-NEWUOA for problems on the ellipsoid in ℜ n is designed based on Powell’s derivative-free software NEWUOA for unconstrained optimization problems. The proposed techniques for our new method mainly include using the Courant penalty function, the augmented Lagrangian method, and the projection technique. Details about the method and theoretical analysis are included in this paper. We also compare our new method with other algorithms by solving test problems and then show the numerical advantages of our new method.

An Efficient Optimization Method for Slow Wave Structures Based on Surrogate Model for TWT Applications

An Efficient Optimization Method for Slow Wave Structures Based on Surrogate Model for TWT Applications

Https://www.bakumathj.org/archive/Vol2No2/j.bmj.033.pdf

In the paper, we consider an approach for numerical solution to the optimal feedback control problem for an object with distributed parameters on the basis of observation of the object’s phase state at its specific locations by the example of the rod heating process. The control actions are the power of the heat source, the values of which are defined on the class of “zonal” controls. The values of the parameters of zonal control actions are determined by subsets of the state space, to which belong the values of the process state at the measurement points at the current moment of time. The problem of determining zonal controls is reduced to a parametric optimal control problem on determining a finite-dimensional vector of values of the parameters of zonal control actions. We derive optimality conditions for the values of the parameters of zonal control actions. These conditions contain formulas for the gradient of the objective functional with respect to the optimizable parameters of zonal controls. They make it possible to solve the stated problem numerically with the application of efficient first-order optimization methods.