期刊,仿生工程学报（英文版） 2024年卷5期_国家学术搜索

期刊信息/Journal information

仿生工程学报（英文版）

主　　编：任露泉

出版周期：季刊

国际刊号：1672-6529

电子邮箱：fsxb@jlu.edu.cn

电　　话：0431-85095180，85094074

邮政编码：130022

地　　址：吉林省长春市人民大街5988号

仿生工程学报（英文版）/Journal Journal of Bionic EngineeringCSCDCSTPCDEISCI

查看更多>>本刊办刊宗旨是为仿生科学与工程领域中的新思想、新发现、新理论和新技术提供交流的平台。主要报道涉及仿生科学与工程所有方面的原始论文和综述，包括动植物仿生工程方面的基础研究，以及这些基础研究在工程技术和设计方面的应用。

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Improved Runge Kutta Optimization Using Compound Mutation Strategy in Reinforcement Learning Decision Making for Feature Selection

Jinpeng HuangYi ChenAli Asghar HeidariLei Liu...

2460-2496页

查看更多>>摘要：Runge Kutta Optimization(RUN)is a widely utilized metaheuristic algorithm.However,it suffers from these issues:the imbalance between exploration and exploitation and the tendency to fall into local optima when it solves real-world opti-mization problems.To address these challenges,this study aims to endow each individual in the population with a certain level of intelligence,allowing them to make autonomous decisions about their next optimization behavior.By incorporating Reinforcement Learning(RL)and the Composite Mutation Strategy(CMS),each individual in the population goes through additional self-improvement steps after completing the original algorithmic phases,referred to as RLRUN.That is,each individual in the RUN population is trained intelligently using RL to independently choose three different differentiation strategies in CMS when solving different problems.To validate the competitiveness of RLRUN,comprehensive empirical tests were conducted using the IEEE CEC 2017 benchmark suite.Extensive comparative experiments with 13 conventional algorithms and 10 advanced algorithms were conducted.The experimental results demonstrated that RLRUN excels in convergence accuracy and speed,surpassing even some champion algorithms.Additionally,this study introduced a binary version of RLRUN,named bRLRUN,which was employed for the feature selection problem.Across 24 high-dimensional datasets encompassing UCI datasets and SBCB machine learning library microarray datasets,bRLRUN occupies the top position in classification accuracy and the number of selected feature subsets compared to some algorithms.In conclusion,the proposed algorithm demonstrated that it exhibits a strong competitive advantage in high-dimensional feature selection for complex datasets.

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Improving PID Controller Performance in Nonlinear Oscillatory Automatic Generation Control Systems Using a Multi-objective Marine Predator Algorithm with Enhanced Diversity

Yang YangYuchao GaoJinran WuZhe Ding...

2497-2514页

查看更多>>摘要：Power systems are pivotal in providing sustainable energy across various sectors.However,optimizing their performance to meet modern demands remains a significant challenge.This paper introduces an innovative strategy to improve the opti-mization of PID controllers within nonlinear oscillatory Automatic Generation Control(AGC)systems,essential for the stability of power systems.Our approach aims to reduce the integrated time squared error,the integrated time absolute error,and the rate of change in deviation,facilitating faster convergence,diminished overshoot,and decreased oscillations.By incorporating the spiral model from the Whale Optimization Algorithm(WOA)into the Multi-Objective Marine Predator Algorithm(MOMPA),our method effectively broadens the diversity of solution sets and finely tunes the balance between exploration and exploitation strategies.Furthermore,the QQSMOMPA framework integrates quasi-oppositional learning and Q-learning to overcome local optima,thereby generating optimal Pareto solutions.When applied to nonlinear AGC systems featuring governor dead zones,the PID controllers optimized by QQSMOMPA not only achieve 14％reduction in the frequency settling time but also exhibit robustness against uncertainties in load disturbance inputs.

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MAPFUNet:Multi-attention Perception-Fusion U-Netfor Liver Tumor Segmentation

Junding SunBiao WangXiaosheng WuChaosheng Tang...

2515-2539页

查看更多>>摘要：The second-leading cause of cancer-related deaths globally is liver cancer.The treatment of liver cancers depends heavily on the accurate segmentation of liver tumors from CT scans.The improved method based on U-Net has achieved good perfor-mance for liver tumor segmentation,but these methods can still be improved.To deal with the problems of poor performance from the original U-Net framework in the segmentation of small-sized liver tumors and the position information of tumors that is seriously lost in the down-sampling process,we propose the Multi-attention Perception-fusion U-Net(MAPFU-Net).We propose the Position ResBlock(PResBlock)in the encoder stage to promote the feature extraction capability of MAPFUNet while retaining the position information regarding liver tumors.A Dual-branch Attention Module(DWAM)is proposed in the skip connections,which narrows the semantic gap between the encoder's and decoder's features and enables the network to utilize the encoder's multi-stage and multi-scale features.We propose the Channel-wise ASPP with Atten-tion(CAA)module at the bottleneck,which can be combined with multi-scale features and contributes to the recovery of micro-tumor feature information.Finally,we evaluated MAPFUNet on the LITS2017 dataset and the 3DIRCADB-01 dataset,with Dice values of 85.81 and 83.84％for liver tumor segmentation,which were 2.89 and 7.89％higher than the baseline model,respectively.The experiment results show that MAPFUNet is superior to other networks with better tumor feature representation and higher accuracy of liver tumor segmentation.We also extended MAPFUNet to brain tumor segmentation on the BraTS2019 dataset.The results indicate that MAPFUNet performs well on the brain tumor segmentation task,and its Dice values on the three tumor regions are 83.27％(WT),84.77％(TC),and 76.98％(ET),respectively.

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Multi-strategy Hybrid Coati Optimizer:A Case Study of Prediction of Average Daily Electricity Consumption in China

Gang HuSa WangEssam H.Houssein

2540-2568页

查看更多>>摘要：The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption.In this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity consumption.Firstly,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning strategy.Effectively improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the population.Secondly,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test sets.Finally,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting models.The experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.

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A Modified Genetic Algorithm for Combined Heat and Power Economic Dispatch

Deliang LiChunyu Yang

2569-2586页

查看更多>>摘要：Combined Heat and Power Economic Dispatch(CHPED)is an important problem in the energy field,and it is beneficial for improving the utilization efficiency of power and heat energies.This paper proposes a Modified Genetic Algorithm(MGA)to determine the power and heat outputs of three kinds of units for CHPED.First,MGA replaces the simulated binary crossover by a new one based on the uniform and guassian distributions,and its convergence can be enhanced.Second,MGA modi-fies the mutation operator by introducing a disturbance coefficient based on guassian distribution,which can decrease the risk of being trapped into local optima.Eight instances with or without prohibited operating zones are used to investigate the efficiencies of MGA and other four genetic algorithms for CHPED.In comparison with the other algorithms,MGA has reduced generation costs by at least 562.73$,1068.7$,522.68$ and 1016.24$,respectively,for instances 3,4,7 and 8,and it has reduced generation costs by at most 848.22$,3642.85$,897.63$ and 3812.65$,respectively,for instances 3,4,7 and 8.Therefore,MGA has desirable convergence and stability for CHPED in comparison with the other four genetic algorithms.

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Reconstructing 3D Biomedical Architectural Order at Multiple Spatial Scales with Multimodal Stack Input

Chaojing ShiGuocheng SunKaitai HanMengyuan Huang...

2587-2601页

查看更多>>摘要：Microscopy,crucial for exploring biological structures,often uses polarizing microscopes to observe tissue anisotropy and reconstruct label-free images.However,these images typically show low contrast,and while fluorescence imaging offers higher contrast,it is phototoxic and can disrupt natural assembly dynamics.This study focuses on reconstructing fluores-cence images from label-free ones using a complex nonlinear transformation,specifically aiming to identify organelles within diverse optical properties of tissues.A multimodal deep learning model,3DTransMDL,was developed,employing the Stokes vector to analyze the sample's retardance,phase,and orientation.This model incorporates isotropy and anisot-ropy to differentiate organelles,enhancing the input with structures'varied optical properties.Additionally,techniques like background distortion normalization and covariate shift methods were applied to reduce noise and overfitting,improving model generalization.The approach was tested on mouse kidney and human brain tissues,successfully identifying specific organelles and demonstrating superior performance in reconstructing 3D images,significantly reducing artifacts compared to 2D predictions.Evaluation metrics such as SSIM,PCC,and R2 score confirm the model's efficacy,with improvements observed in multi-modality input setups.This advancement suggests potential applications in molecular dynamics,aiming for further enhancements in future studies.

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The Chaos Sparrow Search Algorithm:Multi-layer and Multi-pass Welding Robot Trajectory Optimization for Medium and Thick Plates

Song MuJianyong WangChunyang Mu

2602-2618页

查看更多>>摘要：The welding of medium and thick plates has a wide range of applications in the engineering field.Industrial welding robots are gradually replacing traditional welding operations due to their significant advantages,such as high welding quality,high work efficiency,and effective reduction of labor intensity.Ensuring the accuracy of the welding trajectory for the welding robot is crucial for guaranteeing welding quality.In this paper,the author uses the chaos sparrow search algorithm to optimize the trajectory of a multi-layer and multi-pass welding robot for medium and thick plates.Firstly,the Sparrow Search Algorithm(SSA)is improved by introducing tent chaotic mapping and Gaussian mutation of the inertia weight factor.Secondly,in order to prevent the welding robot arm from colliding with obstacles in the welding environment during the welding process,maintain the stability of the welding robot,and ensure the continuous stability of the changes in each joint angle,joint angular velocity,and angular velocity of the joint angle,a welding robot model is established by improving the Denavit-Hartenberg parameter method.A multi-objective optimization fitness function is used to optimize the trajectory of the welding robot,minimizing time and energy consumption.Thirdly,the optimi-zation and convergence performance of SSA and Chaos Sparrow Search Algorithm(CSSA)are compared through 10 benchmark test functions.Based on the six sets of test functions,the CSSA algorithm consistently maintains superior optimization performance and has excellent stability,with a faster decline in the convergence curve compared to the SSA algorithm.Finally,the accuracy of welding is tested through V-shaped multi-layer and multi-pass welding experiments.The experimental results show that the CSSA algorithm has a strong superiority in trajectory optimization of multi-layer and multi-pass welding for medium and thick plates,with an accuracy rate of 99.5％.It is an effective optimization method that can meet the actual needs of production.

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Single Solution Optimization Mechanism of Teaching-Learning-Based Optimization with Weighted Probability Exploration for Parameter Estimation of Photovoltaic Models

Jinge ShiYi ChenZhennao CaiAli Asghar Heidari...

2619-2645页

查看更多>>摘要：This article presents a novel optimization approach called RSWTLBO for accurately identifying unknown parameters in photovoltaic(PV)models.The objective is to address challenges related to the detection and maintenance of PV systems and the improvement of conversion efficiency.RSWTLBO combines adaptive parameter w,Single Solution Optimization Mechanism(SSOM),and Weight Probability Exploration Strategy(WPES)to enhance the optimization ability of TLBO.The algorithm achieves a balance between exploitation and exploration throughout the iteration process.The SSOM allows for local exploration around a single solution,improving solution quality and eliminating inferior solutions.The WPES enables comprehensive exploration of the solution space,avoiding the problem of getting trapped in local optima.The algo-rithm is evaluated by comparing it with 10 other competitive algorithms on various PV models.The results demonstrate that RSWTLBO consistently achieves the lowest Root Mean Square Errors on single diode models,double diode models,and PV module models.It also exhibits robust performance under varying irradiation and temperature conditions.The study concludes that RSWTLBO is a practical and effective algorithm for identifying unknown parameters in PV models.

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Fast and Accurate Pupil Localization in Natural Scenes

Zhuohao GuoManjia SuYihui LiTianyu Liu...

2646-2657页

查看更多>>摘要：The interferences,such as the background,eyebrows,eyelashes,eyeglass frames,illumination variations,and specular lens reflection pose challenges for pupil localization in natural scenes.In this paper,we propose a novel method comprising improved YOLOv8 and Illumination Adaptive Algorithm(IAA),for fast and accurate pupil localization in natural scenes.We introduced deformable convolution into the backbone of YOLOv8 to enable the model to extract the eye regions more accurately,thus avoiding the interference of background outside the eye on subsequent pupil localization.The IAA can reduce the interference of illumination variations and lens reflection by adjusting automatically the grayscale of the image according to the exposure.Experimental results verified that the improved YOLOv8 exhibited an eye detection accuracy(IOU≥0.5)of 90.2％,while the IAA leads to a 9.15％improvement on 5-pixels error ratio e5 with processing times in the tens of microseconds on GPU.Experimental results on the benchmark database CelebA show that the proposed method for pupil localization achieves an accuracy of 83.05％on e5 and achieves real-time performance of 210 FPS on GPU,outperforming other advanced methods.

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An Intrusion Detection System on The Internet of Things Using Deep Learning and Multi-objective Enhanced Gorilla Troops Optimizer

Hossein AsgharzadehAli GhaffariMohammad MasdariFarhad Soleimanian Gharehchopogh...

2658-2684页

查看更多>>摘要：In recent years,developed Intrusion Detection Systems(IDSs)perform a vital function in improving security and anomaly detection.The effectiveness of deep learning-based methods has been proven in extracting better features and more accurate classification than other methods.In this paper,a feature extraction with convolutional neural network on Internet of Things(IoT)called FECNNIoT is designed and implemented to better detect anomalies on the IoT.Also,a binary multi-objective enhance of the Gorilla troops optimizer called BMEGTO is developed for effective feature selection.Finally,the combination of FECNNIoT and BMEGTO and KNN algorithm-based classification technique has led to the presentation of a hybrid method called CNN-BMEGTO-KNN.In the next step,the proposed model is implemented on two benchmark data sets,NSL-KDD and TON-IoT and tested regarding the accuracy,precision,recall,and Fl-score criteria.The proposed CNN-BMEGTO-KNN model has reached 99.99％and 99.86％accuracy on TON-IoT and NSL-KDD datasets,respectively.In addition,the proposed BMEGTO method can identify about 27％and 25％of the effective features of the NSL-KDD and TON-IoT datasets,respectively.

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