A Feature Selection Method Based on Hybrid Dung Beetle Optimization Algorithm and Slap Swarm Algorithm

Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In recent years,meta-heuristic algorithms have been widely used in FS problems,so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization(HBCSSDBO)algorithm is proposed in this paper to improve the effect of FS.In this hybrid algorithm,the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem.By combining the K nearest neighbor(KNN)classifier,the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI(University of California,Irvine)datasets.Seven evaluation metrics such as average adaptation,average prediction accuracy,and average running time are chosen to judge and compare the algorithms.The selected dataset is also discussed by categorizing it into three dimensions:high,medium,and low dimensions.Experimental results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy,shows better optimization performance.In addition,the results of statistical tests confirm the significant validity of the method.

Optimal and robust control of population transfer in asymmetric quantum-dot molecules

We present an optimal and robust quantum control method for efficient population transfer in asymmetric double quantum-dot molecules.We derive a long-duration control scheme that allows for highly efficient population transfer by accurately controlling the amplitude of a narrow-bandwidth pulse.To overcome fluctuations in control field parameters,we employ a frequency-domain quantum optimal control theory method to optimize the spectral phase of a single pulse with broad bandwidth while preserving the spectral amplitude.It is shown that this spectral-phase-only optimization approach can successfully identify robust and optimal control fields,leading to efficient population transfer to the target state while concurrently suppressing population transfer to undesired states.The method demonstrates resilience to fluctuations in control field parameters,making it a promising approach for reliable and efficient population transfer in practical applications.

Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems

Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for solvers.Among all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto front.However,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single constraints.Therefore,this paper attempts to identify promising single constraints and utilize them to help solve CMOPs.To be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively.Besides,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships.Moreover,an improved tentative method is designed to find and transfer useful knowledge among tasks.Experimental results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.

Intelligent Reflecting Surface Assisted Transmission Optimization Strategies in Wireless Networks

Wireless Power Transfer(WPT)technology can provide real-time power for many terminal devices in Internet of Things(IoT)through millimeterWave(mmWave)to support applications with large capacity and low latency.Although the intelligent reflecting surface(IRS)can be adopted to create effective virtual links to address the mmWave blockage problem,the conventional solutions only adopt IRS in the downlink from the Base Station(BS)to the users to enhance the received signal strength.In practice,the reflection of IRS is also applicable to the uplink to improve the spectral efficiency.It is a challenging to jointly optimize IRS beamforming and system resource allocation for wireless energy acquisition and information transmission.In this paper,we first design a Low-Energy Adaptive Clustering Hierarchy(LEACH)clustering protocol for clustering and data collection.Then,the problem of maximizing the minimum system spectral efficiency is constructed by jointly optimizing the transmit power of sensor devices,the uplink and downlink transmission times,the active beamforming at the BS,and the IRS dynamic beamforming.To solve this non-convex optimization problem,we propose an alternating optimization(AO)-based joint solution algorithm.Simulation results show that the use of IRS dynamic beamforming can significantly improve the spectral efficiency of the system,and ensure the reliability of equipment communication and the sustainability of energy supply under NLOS link.

Enhanced Tunicate Swarm Optimization with Transfer Learning Enabled Medical Image Analysis System

Medical image analysis is an active research topic,with thousands of studies published in the past few years.Transfer learning(TL)including convolutional neural networks(CNNs)focused to enhance efficiency on an innovative task using the knowledge of the same tasks learnt in advance.It has played a major role in medical image analysis since it solves the data scarcity issue along with that it saves hardware resources and time.This study develops an EnhancedTunicate SwarmOptimization withTransfer Learning EnabledMedical Image Analysis System(ETSOTL-MIAS).The goal of the ETSOTL-MIAS technique lies in the identification and classification of diseases through medical imaging.The ETSOTL-MIAS technique involves the Chan Vese segmentation technique to identify the affected regions in the medical image.For feature extraction purposes,the ETSOTL-MIAS technique designs a modified DarkNet-53 model.To avoid the manual hyperparameter adjustment process,the ETSOTLMIAS technique exploits the ETSO algorithm,showing the novelty of the work.Finally,the classification of medical images takes place by random forest(RF)classifier.The performance validation of the ETSOTL-MIAS technique is tested on a benchmark medical image database.The extensive experimental analysis showed the promising performance of the ETSOTL-MIAS technique under different measures.

Evolutionary Multitasking With Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization

Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-objective evolutionary algorithms(CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking(EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front(PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.

Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screening and triage.At the same time,CXR interpretation is a time-consuming and subjective process.Furthermore,high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis.Therefore,computer-aided diagnosis(CAD)models using machine learning(ML)and deep learning(DL)can be designed for screening TB accurately.With this motivation,this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification(WSODTL-TBC)model on Chest X-rays(CXR).The presented WSODTL-TBC model aims to detect and classify TB on CXR images.Primarily,the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation.Besides,a pre-trained residual network with a two-dimensional convolutional neural network(2D-CNN)model is applied to extract feature vectors.In addition,the WSO algorithm with long short-term memory(LSTM)model was employed for identifying and classifying TB,where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology,showing the novelty of the work.The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset,and the outcomes were investigated in many aspects.The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.

Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.

Parameter optimization for tungsten carbide/Ni-based composite coating deposited by plasma transferred arc hardfacing

Ni-based composite coatings with a high content of tungsten carbides(Stelcar65composite coatings)were synthesized by plasma transferred arc(PTA)hardfacing.The welding parameters of Stelcar65composite coatings were optimized by orthogonal tests.The PTA welding parameters including welding current,powder feed rate and welding speed have significant influence on the tungsten carbide degradation.The values for the optimum welding current,powder feed rate and welding speed were determined to be100A,25g/min and40mm/min,respectively.The produced WC/Ni-based composite coatings were crack-and degradation-free.The microstructure of deposited layers,as well as the microstructure and microhardness of the optimal coating were further analyzed.

The heat transfer optimization of conical fin by shape modification

In present work, a stepping optimization algorithm is proposed for the geometric optimization of conical fin, and the heat transfer rate of the fin is treated as the objective function in the optimization algorithm. The conical fin is divided into finite elements which have different generatrix slopes, and the geometry of the conical fin is finally determined by ensuring that every divided element can maintain the maximum heat transfer rate. Based on the actual condition of every element of the fin, the heat conduction equation is solved step by step. The present result shows that the optimized conical fin has more heat transfer quantity and higher fin efficiency compared with those of some typical fins. Furthermore, the theoretical feasibility and the error analysis of present optimization algorithm have been performed as well.

Time Optimization of Multiple Knowledge Transfers in the Big Data Environment

In the big data environment, enterprises must constantly assimilate big dataknowledge and private knowledge by multiple knowledge transfers to maintain theircompetitive advantage. The optimal time of knowledge transfer is one of the mostimportant aspects to improve knowledge transfer efficiency. Based on the analysis of thecomplex characteristics of knowledge transfer in the big data environment, multipleknowledge transfers can be divided into two categories. One is the simultaneous transferof various types of knowledge, and the other one is multiple knowledge transfers atdifferent time points. Taking into consideration the influential factors, such as theknowledge type, knowledge structure, knowledge absorptive capacity, knowledge updaterate, discount rate, market share, profit contributions of each type of knowledge, transfercosts, product life cycle and so on, time optimization models of multiple knowledgetransfers in the big data environment are presented by maximizing the total discountedexpected profits (DEPs) of an enterprise. Some simulation experiments have beenperformed to verify the validity of the models, and the models can help enterprisesdetermine the optimal time of multiple knowledge transfer in the big data environment.

Uniformity principle of temperature difference field in heat transfer optimization

The uniformity principle of temperature difference field is very useful in heat exchanger analyses and optimizations.In this paper, we analyze some other heat transfer optimization problems in the thermal management system of spacecrafts,including the cooling of thermal components, the one-stream series-wound heat exchanger network, the volume-to-point heat conduction problem, and the radiative heat transfer optimization problem, and have found that the uniformity principle of temperature difference field also holds. When the design objectives under the given constraints are achieved, the distributions of the temperature difference fields are uniform. The principle reflects the characteristic of the distribution of potential in the heat transfer optimization problems. It is also shown that the principle is consistent with the entransy theory. Therefore, although the principle is intuitive and phenomenological, the entransy theory can be the physical basis of the principle.

Autonomous Maneuver Decisions via Transfer Learning Pigeon-Inspired Optimization for UCAVs in Dogfight Engagements

This paper proposes an autonomous maneuver decision method using transfer learning pigeon-inspired optimization(TLPIO)for unmanned combat aerial vehicles(UCAVs)in dogfight engagements.Firstly,a nonlinear F-16 aircraft model and automatic control system are constructed by a MATLAB/Simulink platform.Secondly,a 3-degrees-of-freedom(3-DOF)aircraft model is used as a maneuvering command generator,and the expanded elemental maneuver library is designed,so that the aircraft state reachable set can be obtained.Then,the game matrix is composed with the air combat situation evaluation function calculated according to the angle and range threats.Finally,a key point is that the objective function to be optimized is designed using the game mixed strategy,and the optimal mixed strategy is obtained by TLPIO.Significantly,the proposed TLPIO does not initialize the population randomly,but adopts the transfer learning method based on Kullback-Leibler(KL)divergence to initialize the population,which improves the search accuracy of the optimization algorithm.Besides,the convergence and time complexity of TLPIO are discussed.Comparison analysis with other classical optimization algorithms highlights the advantage of TLPIO.In the simulation of air combat,three initial scenarios are set,namely,opposite,offensive and defensive conditions.The effectiveness performance of the proposed autonomous maneuver decision method is verified by simulation results.

Optimal Deep Transfer Learning Based Colorectal Cancer Detection and Classification Model

Colorectal carcinoma(CRC)is one such dispersed cancer globally and also prominent one in causing cancer-based death.Conventionally,pathologists execute CRC diagnosis through visible scrutinizing under the microscope the resected tissue samples,stained and fixed through Haematoxylin and Eosin(H&E).The advancement of graphical processing systems has resulted in high potentiality for deep learning(DL)techniques in interpretating visual anatomy from high resolution medical images.This study develops a slime mould algorithm with deep transfer learning enabled colorectal cancer detection and classification(SMADTL-CCDC)algorithm.The presented SMADTL-CCDC technique intends to appropriately recognize the occurrence of colorectal cancer.To accomplish this,the SMADTLCCDC model initially undergoes pre-processing to improve the input image quality.In addition,a dense-EfficientNet technique was employed to extract feature vectors from the pre-processed images.Moreover,SMA with Discrete Hopfield neural network(DHNN)method was applied for the recognition and classification of colorectal cancer.The utilization of SMA assists in appropriately selecting the parameters involved in the DHNN approach.A wide range of experiments was implemented on benchmark datasets to assess the classification performance.A comprehensive comparative study highlighted the better performance of the SMADTL-CDC model over the recent approaches.

A Multimodel Transfer-Learning-Based Car Price Prediction Model with an Automatic Fuzzy Logic Parameter Optimizer

Cars are regarded as an indispensable means of transportation in Taiwan.Several studies have indicated that the automotive industry has witnessed remarkable advances and that the market of used cars has rapidly expanded.In this study,a price prediction system for used BMW cars was developed.Nine parameters of used cars,including their model,registration year,and transmission style,were analyzed.The data obtained were then divided into three subsets.The first subset was used to compare the results of each algorithm.The predicted values produced by the two algorithms with the most satisfactory results were used as the input of a fully connected neural network.The second subset was used with an optimization algorithm to modify the number of hidden layers in a fully connected neural network and modify the low,medium,and high parameters of the membership function(MF)to achieve model optimization.Finally,the third subset was used for the validation set during the prediction process.These three subsets were divided using k-fold cross-validation to avoid overfitting and selection bias.In conclusion,in this study,a model combining two optimal algorithms(i.e.,random forest and k-nearest neighbors)with several optimization algorithms(i.e.,gray wolf optimizer,multilayer perceptron,and MF)was successfully established.The prediction results obtained indicated a mean square error of 0.0978,a root-mean-square error of 0.3128,a mean absolute error of 0.1903,and a coefficient of determination of 0.9249.

State Accurate Representation and Performance Prediction Algorithm Optimization for Industrial Equipment Based on Digital Twin

The combination of the Industrial Internet of Things(IIoT)and digital twin(DT)technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance.However,conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information.The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model.In this paper,a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state.Firstly,a global real-time feedback and the dynamic adjustment mechanism is established by combining DT-IIoT with algorithm optimization.Secondly,a strong screening dual-model optimization(SSDO)prediction method based on Stacking integration and fusion is proposed in the dynamic regulation mechanism.Lightweight screening and multi-round optimization are used to improve the prediction accuracy of the evolution model.Finally,tak-ing the boiler performance of a power plant in Shanxi as an example,the accurate representation and evolution prediction of boiler steam quantity is realized.The results show that the real-time state representation and life cycle performance prediction of large key equipment is optimized through these methods.The self-lifting ability of the Stacking integration and fusion-based SSDO prediction method is 15.85%on average,and the optimal self-lifting ability is 18.16%.The optimization model reduces the MSE loss from the initial 0.318 to the optimal 0.1074,and increases R2 from the initial 0.731 to the optimal 0.9092.The adaptability and reliability of the model are comprehensively improved,and better prediction and analysis results are achieved.This ensures the stable operation of core equipment,and is of great significance to comprehensively understanding the equipment status and performance.

Optimization of DC Resistance Divider Up to 1200 kV Using Thermal and Electric Field Analysis

Self-heating and electric field distribution are the primary factors affecting the accuracy of the Ultra High Voltage Direct Current(UHVDC)resistive divider.Reducing the internal temperature rise of the voltage divider caused by self-heating,reducing the maximum electric field strength of the voltage divider,and uniform electric field distribution can effectively improve the UHVDC resistive divider’s accuracy.In this paper,thermal analysis and electric field distribution optimization design of 1200 kV UHVDC resistive divider are carried out:(1)Using the proposed iterative algorithm,the heat dissipation and temperature distribution of the high voltage DC resistive divider are studied,and the influence of the ambient temperature and the power of the divider on the temperature of the insulating medium of the divider is analyzed;(2)Established the finite element models of 1200 kV and 2×600 kV DC resistive dividers,analyzed the influence of the size of the grading ring and the installation position on the maximum electric field strength of the voltage divider,and calculated the impact of the shielding resistor layer on the vicinity of the measuring resistor layer.The research indicates that:(1)The temperature of the insulating medium is linearly related to the horsepower of the voltage divider and the ambient temperature;(2)After the optimized design of the electric field,the maximum electric field strength of the 1200 kV DC resistive divider is reduced to 1471 V/mm,which is about 24% lower than that of the unoptimized design;(3)Installing the shielding resistor layer can significantly improve the electric field near the measuring resistor layer.This paper has an important reference function for improving the accuracy of the UHVDC resistive divider.

Weed Classification Using Particle Swarm Optimization and Deep Learning Models

Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and quality.Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity.To address this issue,an efficient weed classification model is proposed with the Deep Convolutional Neural Network(CNN)that implements automatic feature extraction and performs complex feature learning for image classification.Throughout this work,weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds based on the two publicly available weed datasets.The Tamil Nadu Agricultural University(TNAU)dataset used as afirst dataset that consists of 40 classes of weed images and the other dataset is from Indian Council of Agriculture Research–Directorate of Weed Research(ICAR-DWR)which contains 50 classes of weed images.An effective Particle Swarm Optimization(PSO)technique is applied in the proposed CNN to automa-tically evolve and improve its classification accuracy.The proposed model was evaluated and compared with pre-trained transfer learning models such as GoogLeNet,AlexNet,Residual neural Network(ResNet)and Visual Geometry Group Network(VGGNet)for weed classification.This work shows that the performance of the PSO assisted proposed CNN model is significantly improved the success rate by 98.58%for TNAU and 97.79%for ICAR-DWR weed datasets.

Structural Optimization and Analysis of Magnetic Coupled Inductive Power Transfer System

Along with the prosperous of magnetic coupled inductive power transfer( MCIPT) technology which is widely used in industrial applications such as electric vehicle charging,the topology of double D coils( DD coils) with a spatial quadrature Q coil arises with great research interest. The Q coil, however, has been thoroughly studied by adding to the receiving side but seldom to the transmitting side. By using finite element simulation, this paper presents a preliminary study on the effectiveness of Q coil in the transmitting side,and its inner dimension is optimized for optimal compensating the misalignment between the transmitting and receiver sides. Simulation results show that the windings of the Q coil should be placed in the center of the aperture of the DD coils,and these results render a useful guidance for mechanical structural design and circuit controller design of MCIPT.

Optimization of multi-revolution low-thrust transfer based on modified direct method

A modified direct optimization method is proposed to solve the optimal multi-revolution transfer with low-thrust between Earth-orbits. First, through parameterizing the control steering angles by costate variables, the search space of free parameters has been decreased. Then, in order to obtain the global optimal solution effectively and robustly, the simulated annealing and penalty function strategies were used to handle the constraints, and a GA/SQP hybrid optimization algorithm was utilized to solve the parameter optimization problem, in which, a feasible suboptimal solution obtained by GA was submitted as an initial parameter set to SQP for refinement. Comparing to the classical direct method, this novel method has fewer free parameters, needs not initial guesses, and has higher computation precision. An optimal-fuel transfer problem from LEO to GEO was taken as an example to validate the proposed approach. The results of simulation indicate that our approach is available to solve the problem of optimal muhi-revolution transfer between Earth-orbits.