期刊,仿生工程学报（英文版） 2024年卷1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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Flow Field Simulation and Parameter Analysis of Hydraulic Unbalanced Bionic Self-recovery Actuator for Rotary Equipment

Wei LiXin PanDehong GeJinji Gao...

325-343页

查看更多>>摘要：The rotor is the most important component of rotating machinery,and the vibration produced by its mass unbalance has a serious influence on the secure and steady operation of the machine,so an effective online suppression technology is urgently needed.A new hydraulic unbalanced bionic self-recovery system is introduced,imitating the way of manually repairing faulty equipment.To accomplish the effect of actuator mass redistribution,the technology employs pressurized air to drive the quantitative transfer of liquid in the reservoir cavity at opposite positions.It can complete the online adjustment of the equipment's balancing state and suppress the unbalanced vibration of equipment in real time,which gives the equipment the ability to maintain an autonomous health state and improve equipment performance.The composition and working principle of the system are introduced in detail,and the key performance parameters,such as the minimum running speed and the balancing liquid transfer speed,are analyzed theoretically.The fluid-solid coupling model of the actuator was established,and the two-phase flow from inside the hydraulic unbalanced bionic self-recovery actuator was simulated under multiple working conditions and the performance parameters were quantitatively analyzed.A balancing simulation test bed was built,and its effectiveness was verified by performance parameter tests and unbalanced bionic self-recovery experiments.The experimental results show that the mass distribution adjustment of the balancing disk can be achieved using different viscosity balancing liquid,and the response of liquid viscosity 10 cSt is faster than that of liquid viscosity 100 cSt in the process of balancing liquid transfer,and the time is reduced by more than 75％;the system can reduce the simulated rotor amplitude from 18.3 μm to 10.6 μm online in real time,which provides technical support for the subsequent development of a new generation of bionic intelligent equipment.

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An Innovation of Evaluation and Design of Vehicle Acceleration Sound Based on EEG Signals

Liping XieXinYou LinWan ChenZhien Liu...

344-361页

查看更多>>摘要：There is a bottleneck in the design of vehicle sound that the subjective perception of sound quality that combines multiple psychological factors fails to be accurately and objectively quantified.Therefore,EEG signals are introduced in this paper to investigate the evaluation and design method of vehicle acceleration sound with powerful sound quality.Firstly,the experiment of EEG acquisition and subjective evaluation under the stimulation of powerful vehicle sounds is conducted,respectively,then three physiological EEG features of PSD_p,PSD_y and DE are constructed to evaluate the vehicle sounds based on the correlation analysis algorithms.Subsequently,the Adaptive Genetic Algorithm(AGA)is proposed to optimize the Elman model,where an intelligent model(AGA-Elman)is constructed to objectively predicate the perception of subjects for the vehicle sounds with powerful sound quality.The results demonstrate that the error of the constructed AGA-Elman model is only 2.88％,which outperforms than the traditional BP and Elman model;Finally,two vehicle acceleration sounds(Design 1 and Design2)are designed based on the constructed AGA-Elman model from the perspective of order modulation and frequency modulation,which provide the acoustic theoretical guidance for the design of vehicle sound incorporating the EEG signals.

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Experimental Study on Impingement Processes of Fuel Sprays on Biomimetic Structured Surfaces

Yanling ChenLiang GuoWanchen SunYuying Yan...

362-373页

查看更多>>摘要：To improve the controllability of the wall-wetting process after the fuel spray-wall impingement in internal combustion engines,the methods of laser etching,chemical etching and surface free energy modification are used to prepare biomimetic structured surfaces with different wettability.The impingement processes of diesel and n-butanol sprays on the walls under different conditions are experimentally investigated.As the surface oleophilicity increases,the spreading radius of wall-impinging sprays decreases.At about 5 s after the fuel injections,the fuel spray droplets hit the walls for the first time,and the secondary breakup and rebound occur.The mixture concentrations of different fuels hitting the various walls reach the peak value.Under a higher surface temperature,the peak value of the mixture concentration is mainly related to the heat flux to the fuel droplets in different boiling regimes from the metal surfaces.The concentration of the air-fuel mixture in the near wall region increases with increasing surface oleophilicity,increasing wall temperature and decreasing ambient pressure.Compared with diesel,n-butanol presents a higher air-fuel mixture concentration in the near wall region.

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Geyser Inspired Algorithm:A New Geological-inspired Meta-heuristic for Real-parameter and Constrained Engineering Optimization

Mojtaba GhasemiMohsen ZareAmir ZahediMohammad-Amin Akbari...

374-408页

查看更多>>摘要：Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using sta-tistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evalu-ate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

Adel GotDjaafar ZouacheAbdelouahab MoussaouiLaith Abualigah...

409-425页

查看更多>>摘要：Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in predic-tion and classification tasks.However,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good performance.On the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets.In this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously.The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets.Additionally,it is applied to a disease Covid-19 dataset.The experimental results show the high ability of the proposed algorithm to find the appropriate SVM's parameters,and its acceptable performance to deal with feature selection problem.

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An Improved Binary Quantum-based Avian Navigation Optimizer Algorithm to Select Effective Feature Subset from Medical Data:A COVID-19 Case Study

Ali FatahiMohammad H.Nadimi-ShahrakiHoda Zamani

426-446页

查看更多>>摘要：Feature Subset Selection(FSS)is an NP-hard problem to remove redundant and irrelevant features particularly from medical data,and it can be effectively addressed by metaheuristic algorithms.However,existing binary versions of metaheuristic algorithms have issues with convergence and lack an effective binarization method,resulting in suboptimal solutions that hinder diagnosis and prediction accuracy.This paper aims to propose an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm(IBQANA)for FSS in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic algorithms.The proposed IBQANA's contributions include the Hybrid Binary Operator(HBO)and the Distance-based Binary Search Strategy(DBSS).HBO is designed to convert continuous values into binary solutions,even for values outside the[0,1]range,ensuring accurate binary mapping.On the other hand,DBSS is a two-phase search strategy that enhances the performance of inferior search agents and accelerates convergence.By combining exploration and exploitation phases based on an adaptive probability function,DBSS effectively avoids local optima.The effectiveness of applying HBO is compared with five transfer function families and thresholding on 12 medical datasets,with feature numbers ranging from 8 to 10,509.IBQANA's effectiveness is evaluated regarding the accuracy,fitness,and selected features and compared with seven binary metaheuristic algorithms.Furthermore,IBQANA is utilized to detect COVID-19.The results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical datasets.The proposed method presents a promising solution to the FSS problem in medical data preprocessing.

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Dragonfly Interaction Algorithm for Optimization of Queuing Delay in Industrial Wireless Networks

Sanjay BhardwajDa-Hye KimDong-Seong Kim

447-485页

查看更多>>摘要：In industrial wireless networks,data transmitted from source to destination are highly repetitive.This often leads to the queuing of the data,and poor management of the queued data results in excessive delays,increased energy consumption,and packet loss.Therefore,a nature-inspired-based Dragonfly Interaction Optimization Algorithm(DMOA)is proposed for optimization of the queue delay in industrial wireless networks.The term"interaction"herein used is the characterization of the"flying movement"of the dragonfly towards damselflies(female dragonflies)for mating.As a result,interaction is represented as the flow of transmitted data packets,or traffic,from the source to the base station.This includes each and every feature of dragonfly movement as well as awareness of the rival dragonflies,predators,and damselflies for the desired optimization of the queue delay.These features are juxtaposed as noise and interference,which are further used in the calculation of industrial wireless metrics:latency,error rate(reliability),throughput,energy efficiency,and fairness for the optimization of the queue delay.Statistical analysis,convergence analysis,the Wilcoxon test,the Friedman test,and the classical as well as the 2014 IEEE Congress of Evolutionary Computation(CEC)on the benchmark functions are also used for the evaluation of DMOA in terms of its robustness and efficiency.The results demonstrate the robustness of the proposed algorithm for both classical and benchmarking functions of the IEEE CEC 2014.Furthermore,the accuracy and efficacy of DMOA were demonstrated by means of the convergence rate,Wilcoxon testing,and ANOVA.Moreover,fairness using Jain's index in queue delay optimization in terms of throughput and latency,along with computational complexity,is also evaluated and compared with other algorithms.Simulation results show that DMOA exceeds other bio-inspired optimization algorithms in terms of fairness in queue delay management and average packet loss.The proposed algorithm is also evaluated for the conflicting objectives at Pareto Front,and its analysis reveals that DMOA finds a compromising solution between the objectives,thereby optimizing queue delay.In addition,DMOA on the Pareto front delivers much greater performance when it comes to optimizing the queuing delay for industry wireless networks.

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Chaotic Aquila Optimization Algorithm for Solving Phase Equilibrium Problems and Parameter Estimation of Semi-empirical Models

Oguz Emrah TurgutMert Sinan TurgutErhan Kirtepe

486-526页

查看更多>>摘要：This research study aims to enhance the optimization performance of a newly emerged Aquila Optimization algorithm by incorporating chaotic sequences rather than using uniformly generated Gaussian random numbers.This work employs 25 different chaotic maps under the framework of Aquila Optimizer.It considers the ten best chaotic variants for performance evaluation on multidimensional test functions composed of unimodal and multimodal problems,which have yet to be stud-ied in past literature works.It was found that Ikeda chaotic map enhanced Aquila Optimization algorithm yields the best predictions and becomes the leading method in most of the cases.To test the effectivity of this chaotic variant on real-world optimization problems,it is employed on two constrained engineering design problems,and its effectiveness has been verified.Finally,phase equilibrium and semi-empirical parameter estimation problems have been solved by the proposed method,and respective solutions have been compared with those obtained from state-of-art optimizers.It is observed that CH01 can successfully cope with the restrictive nonlinearities and nonconvexities of parameter estimation and phase equi-librium problems,showing the capabilities of yielding minimum prediction error values of no more than 0.05 compared to the remaining algorithms utilized in the performance benchmarking process.

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Feedback Mechanism-driven Mutation Reptile Search Algorithm for Optimizing Interpolation Developable Surfaces

Gang HuJiao WangXiaoni ZhuMuhammad Abbas...

527-571页

查看更多>>摘要：Curvature lines are special and important curves on surfaces.It is of great significance to construct developable surface interpolated on curvature lines in engineering applications.In this paper,the shape optimization of generalized cubic ball developable surface interpolated on the curvature line is studied by using the improved reptile search algorithm.Firstly,based on the curvature line of generalized cubic ball curve with shape adjustable,this paper gives the construction method of SGC-Ball developable surface interpolated on the curve.Secondly,the feedback mechanism,adaptive parameters and mutation strategy are introduced into the reptile search algorithm,and the Feedback mechanism-driven improved reptile search algorithm effectively improves the solving precision.On IEEE congress on evolutionary computation 2014,2017,2019 and four engineering design problems,the feedback mechanism-driven improved reptile search algorithm is compared with other representative methods,and the result indicates that the solution performance of the feedback mechanism-driven improved reptile search algorithm is competitive.At last,taking the minimum energy as the evaluation index,the shape optimization model of SGC-Ball interpolation developable surface is established.The developable surface with the minimum energy is achieved with the help of the feedback mechanism-driven improved reptile search algorithm,and the comparison experiment verifies the superiority of the feedback mechanism-driven improved reptile search algorithm for the shape opti-mization problem.

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Bald Eagle Search Optimization Algorithm Combined with Spherical Random Shrinkage Mechanism and Its Application

Wenyan GuoZhuolin HouFang DaiXiaoxia Wang...

572-605页

查看更多>>摘要：Over the last two decades,stochastic optimization algorithms have proved to be a very promising approach to solving a variety of complex optimization problems.Bald eagle search optimization(BES)as a new stochastic optimization algorithm with fast convergence speed has the ability of prominent optimization and the defect of collapsing in the local best.To avoid BES collapse at local optima,inspired by the fact that the volume of the sphere is the largest when the surface area is certain,an improved bald eagle search optimization algorithm(INMBES)integrating the random shrinkage mechanism of the sphere is proposed.Firstly,the INMBES embeds spherical coordinates to design a more accurate parameter update method to modify the coverage and dispersion of the population.Secondly,the population splits into elite and non-elite groups and the Ber-noulli chaos is applied to elite group to tap around potential solutions of the INMBES.The non-elite group is redistributed again and the Nelder-Mead simplex strategy is applied to each group to accelerate the evolution of the worst individual and the convergence process of the INMBES.The results of Friedman and Wilcoxon rank sum tests of CEC2017 in 10,30,50,and 100 dimensions numerical optimization confirm that the INMBES has superior performance in convergence accuracy and avoiding falling into local optimization compared with other potential improved algorithms but inferior to the champion algorithm and ranking third.The three engineering constraint optimization problems and 26 real world problems and the problem of extracting the best feature subset by encapsulated feature selection method verify that the INMBES's performance ranks first and has achieved satisfactory accuracy in solving practical problems.

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