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仿生工程学报（英文版）
仿生工程学报（英文版）

任露泉

季刊

1672-6529

fsxb@jlu.edu.cn

0431-85095180，85094074

130022

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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    A Method Based on Plants Light Absorption Spectrum and Its Use for Data Clustering

    Behnam FarnadKambiz MajidzadehMohammad MasdariAmin Babazadeh Sangar...
    3004-3040页
    查看更多>>摘要：Nature-inspired optimization algorithms refer to techniques that simulate the behavior and ecosystem of living organisms or natural phenomena.One such technique is the"Photosynthesis Spectrum Algorithm,"which was developed by mimicking the process by which photons behave as a population in plants.This optimization technique has three stages that mimic the structure of leaves and the fluorescence phenomenon.Each stage updates the fitness of the solution by using a mathematical equation to direct the photon to the reaction center.Three stages of testing have been conducted to test the efficacy of this approach.In the first stage,functions from the CEC 2019 and CEC 2021 competitions are used to evaluate the performance and convergence of the proposed method.The statistical results from non-parametric Friedman and Kendall's W tests show that the proposed method is superior to other methods in terms of obtaining the best average of solutions and achieving stabil-ity in finding solutions.In other sections,the experiment is designed for data clustering.The proposed method is compared with recent data clustering and classification metaheuristic algorithms,indicating that this method can achieve significant performance for clustering in less than 10 s of CPU time and with an accuracy of over 90％.
      原文链接:
    • 万方数据

    Learner Phase of Partial Reinforcement Optimizer with Nelder-Mead Simplex for Parameter Extraction of Photovoltaic Models

    Jinpeng HuangZhennao CaiAli Asghar HeidariLei Liu...
    3041-3075页
    查看更多>>摘要：This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed LNPRO.The LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,changing the state of the PRO in terms of self-strengthening.Furthermore,the Nelder-Mead simplex is used to optimize the best agent in the population,accelerating the convergence speed and improving the accuracy of the PRO population.By com-paring LNPRO with nine advanced algorithms in the IEEE CEC 2022 benchmark function,the convergence accuracy of the LNPRO has been verified.The accuracy and stability of simulated data and real data in the parameter extraction of PV systems are crucial.Compared to the PRO,the precision and stability of LNPRO have indeed been enhanced in four types of photovoltaic components,and it is also superior to other excellent algorithms.To further verify the parameter extraction problem of LNPRO in complex environments,LNPRO has been applied to three types of manufacturer data,demonstrating excellent results under varying irradiation and temperatures.In summary,LNPRO holds immense potential in solving the parameter extraction problems in PV systems.
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    An Opposition-Based Learning Adaptive Chaotic Particle Swarm Optimization Algorithm

    Chongyang JiaoKunjie YuQinglei Zhou
    3076-3097页
    查看更多>>摘要：To solve the shortcomings of Particle Swarm Optimization(PSO)algorithm,local optimization and slow convergence,an Opposition-based Learning Adaptive Chaotic PSO(LCPSO)algorithm was presented.The chaotic elite opposition-based learning process was applied to initialize the entire population,which enhanced the quality of the initial individuals and the population diversity,made the initial individuals distribute in the better quality areas,and accelerated the search efficiency of the algorithm.The inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm,and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local optimum.The LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics,and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability,search accuracy and convergence speed.In addition,the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.
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    Solving Fuel-Based Unit Commitment Problem Using Improved Binary Bald Eagle Search

    Sharaz AliMohammed Azmi Al-BetarMohamed NasorMohammed A.Awadallah...
    3098-3122页
    查看更多>>摘要：The Unit Commitment Problem(UCP)corresponds to the planning of power generation schedules.The objective of the fuel-based unit commitment problem is to determine the optimal schedule of power generators needed to meet the power demand,which also minimizes the total operating cost while adhering to different constraints such as power generation limits,unit startup,and shutdown times.In this paper,four different binary variants of the Bald Eagle Search(BES)algo-rithm,were introduced,which used two variants using S-shape,U-shape,and V-shape transfer functions.In addition,the best-performing variant(using an S-shape transfer function)was selected and improved further by incorporating two binary operators:swap-window and window-mutation.This variation is labeled Improved Binary Bald Eagle Search(IBBESS2).All five variants of the proposed algorithm were successfully adopted to solve the fuel-based unit commitment problem using seven test cases of 4-,10-,20-,40-,60-,80-,and 100-unit.For comparative evaluation,34 comparative methods from existing literature were compared,in which IBBESS2 achieved competitive scores against other optimization techniques.In other words,the proposed IBBESS2 performs better than all other competitors by achieving the best average scores in 20-,40-,60-,80-,and 100-unit problems.Furthermore,IBBESS2 demonstrated quicker convergence to an optimal solution than other algorithms,especially in large-scale unit commitment problems.The Friedman statistical test further validates the results,where the proposed IBBESS2 is ranked the best.In conclusion,the proposed IBBESS2 can be considered a powerful method for solving large-scale UCP and other related problems.
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    Feature Selection Based on Improved White Shark Optimizer

    Qianqian CuiShijie ZhaoMiao ChenQiuli Zhao...
    3123-3150页
    查看更多>>摘要：Feature Selection(FS)is an optimization problem that aims to downscale and improve the quality of a dataset by retaining relevant features while excluding redundant ones.It enhances the classification accuracy of a dataset and holds a crucial position in the field of data mining.Utilizing metaheuristic algorithms for selecting feature subsets contributes to optimiz-ing the FS problem.The White Shark Optimizer(WSO),as a metaheuristic algorithm,primarily simulates the behavior of great white sharks'sense of hearing and smelling during swimming and hunting.However,it fails to consider their other randomly occurring behaviors,for example,Tail Slapping and Clustered Together behaviors.The Tail Slapping behavior can increase population diversity and improve the global search performance of the algorithm.The Clustered Together behavior includes access to food and mating,which can change the direction of local search and enhance local utiliza-tion.It incorporates Tail Slapping and Clustered Together behavior into the original algorithm to propose an Improved White Shark Optimizer(IWSO).The two behaviors and the presented IWSO are tested separately using the CEC2017 benchmark functions,and the test results of IWSO are compared with other metaheuristic algorithms,which proves that IWSO combining the two behaviors has a stronger search capability.Feature selection can be mathematically described as a weighted combination of feature subset size and classification error rate as an optimization model,which is iteratively optimized using discretized IWSO which combines with K-Nearest Neighbor(KNN)on 16 benchmark datasets and the results are compared with 7 metaheuristics.Experimental results show that the IWSO is more capable in selecting feature subsets and improving classification accuracy.
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    Double Enhanced Solution Quality Boosted RIME Algorithm with Crisscross Operations for Breast Cancer Image Segmentation

    Mengjun SunYi ChenAli Asghar HeidariLei Liu...
    3151-3178页
    查看更多>>摘要：The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis.Computer-aided medical systems are designed to provide accurate information and reduce human errors,in which accurate and effective seg-mentation of medical images plays a pivotal role in improving clinical outcomes.Multilevel Threshold Image Segmentation(MTIS)is widely favored due to its stability and straightforward implementation.Especially when dealing with sophisticated anatomical structures,high-level thresholding is a crucial technique in identifying fine details.To enhance the accuracy of complex breast cancer image segmentation,this paper proposes an improved version of RIME optimizer EECRIME,denoted as the double Enhanced solution quality Crisscross RIME algorithm.The original RIME initially conducts an efficient opti-mization to target promising solutions.The double-enhanced solution quality(EESQ)mechanism is proposed for thorough exploitation without falling into local optimum.In contrast,the crisscross operations perform a further local exploration of the generated feasible solutions.The performance of EECRIME is verified with basic and advanced algorithms on IEEE CEC2017 benchmark functions.Furthermore,an EECRIME-based MTIS method in combination with Kapur's entropy is applied to segment breast Infiltrating Ductal Carcinoma(IDC)histology images.The results demonstrate that the developed model significantly surpasses its competitors,establishing it as a practical approach for complex medical image processing.
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    Multi-graph Networks with Graph Pooling for COVID-19 Diagnosis

    Chaosheng TangWenle XuJunding SunShuihua Wang...
    3179-3200页
    查看更多>>摘要：Convolutional Neural Networks(CNNs)have shown remarkable capabilities in extracting local features from images,yet they often overlook the underlying relationships between pixels.To address this limitation,previous approaches have attempted to combine CNNs with Graph Convolutional Networks(GCNs)to capture global features.However,these approaches typically neglect the topological structure information of the graph during the global feature extraction stage.This paper proposes a novel end-to-end hybrid architecture called the Multi-Graph Pooling Network(MGPN),which is designed explicitly for chest X-ray image classification.Our approach sequentially combines CNNs and GCNs,enabling the learning of both local and global features from individual images.Recognizing that different nodes contribute dif-ferently to the final graph representation,we introduce an NI-GTP module to enhance the extraction of ultimate global features.Additionally,we introduce a G-LFF module to fuse the local and global features effectively.
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    Journal of Bionic Engineering

    后插1-后插9页
      原文链接:
    • 万方数据