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Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images

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Journal of bionic Engineering 2023年第3期20卷 1198-1262页

作者： Yan Han Weibin Chen Ali Asghar Heidari Huiling Chen Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran

Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the *** quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the ***,it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray *** we all know,image segmentation is a critical stage in image processing and *** achieve better image segmentation results,this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named *** utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevel threshold image *** image segmentation scheme is called *** ran two sets of experiments to test the performance of RDMVO and ***,RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark ***,the image segmentation experiment was carried out using RDMVO-MIS,and some meta-heuristic algorithms were selected as *** test image dataset includes Berkeley images and COVID-19 Chest X-ray *** experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.

关键词： COVID-19 Multilevel threshold image segmentation Kapur’s entropy Multi-verse optimizer Meta-heuristic algorithm bionic algorithm

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An Improved Harris Hawks Optimization algorithm with Multi-strategy for Community Detection in Social Network

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Journal of bionic Engineering 2023年第3期20卷 1175-1197页

作者： Farhad Soleimanian Gharehchopogh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia969Iran

The purpose of community detection in complex networks is to identify the structural location of nodes. Complex network methods are usually graphical, with graph nodes representing objects and edges representing connections between things. Communities are node clusters with many internal links but minimal intergroup connections. Although community detection has attracted much attention in social media research, most face functional weaknesses because the structure of society is unclear or the characteristics of nodes in society are not the same. Also, many existing algorithms have complex and costly calculations. This paper proposes different Harris Hawk Optimization (HHO) algorithm methods (such as Improved HHO Opposition-Based Learning(OBL) (IHHOOBL), Improved HHO Lévy Flight (IHHOLF), and Improved HHO Chaotic Map (IHHOCM)) were designed to balance exploitation and exploration in this algorithm for community detection in the social network. The proposed methods are evaluated on 12 different datasets based on NMI and modularity criteria. The findings reveal that the IHHOOBL method has better detection accuracy than IHHOLF and IHHOCM. Also, to offer the efficiency of the , state-of-the-art algorithms have been used as comparisons. The improvement percentage of IHHOOBL compared to the state-of-the-art algorithm is about 7.18%.

关键词： bionic algorithm Complex network Community detection Harris hawk optimization algorithm Opposition-based learning Levy flight Chaotic maps

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A Novel Approach for Mitigating Power Quality Issues in a PV Integrated Microgrid System Using an Improved Jelly Fish algorithm

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Journal of bionic Engineering 2023年第1期20卷 30-46页

作者： Swati Suman Debashis Chatterjee Rupali Mohanty Department of Electrical Engineering Jadavpur UniversityKolkataWest Bengal700032India

A two-step methodology was used to address and improve the power quality concerns for the PV-integrated microgrid system. First, partial shading was included to deal with the real-time issues. The Improved Jelly Fish algorithm integrated Perturb and Obserb (IJFA-PO) has been proposed to track the Global Maximum Power Point (GMPP). Second, the main unit-powered via DC–AC converter is synchronised with the grid. To cope with the wide voltage variation and harmonic mitigation, an auxiliary unit undergoes a novel series compensation technique. Out of various switching approaches, IJFA-based Selective Harmonic Elimination (SHE) in 120° conduction gives the optimal solution. Three switching angles were obtained using IJFA, whose performance was equivalent to that of nine switching angles. Thus, the system is efficient with minimised higher-order harmonics and lower switching losses. The proposed system outperformed in terms of efficiency, metaheuristics, and convergence. The Total Harmonic Distortion (THD) obtained was 1.32%, which is within the IEEE 1547 and IEC tolerable limits. The model was developed in MATLAB/Simulink 2016b and verified with an experimental prototype of grid-synchronised PV capacity of 260 W tested under various loading conditions. The present model is reliable and features a simple controller that provides more convenient and adequate performance.

关键词： Harmonic mitigation Selective harmonic elimination pulse width modulation inverters Search-based optimization techniques bionic algorithm Total harmonic distortion Modulation indices

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Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing

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Journal of bionic Engineering 2023年第5期20卷 2240-2275页

作者： Jiaochen Chen Zhennao Cai Huiling Chen Xiaowei Chen José Escorcia-Gutierrez Romany F.Mansour Mahmoud Ragab College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China Department of Rheumatology and Immunology The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China Department of Computational Science and Electronics Universidad de la CostaCUC080002BarranquillaColombia Department of Mathematics Faculty of ScienceNew Valley University72511El-KhargaEgypt Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing *** assist pathologists in evaluating histopathological images of LN,a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN *** method is based on an improved Cuckoo Search(CS)algorithm that introduces a Diffusion Mechanism(DM)and an Adaptiveβ-Hill Climbing(AβHC)strategy called the DMCS *** DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 *** addition,the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological *** results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal *** to the three image quality evaluation metrics:PSNR,FSIM,and SSIM,the proposed image segmentation method performs well in image segmentation *** research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.

关键词： Multi-threshold image segmentation 2D Rényi entropy Renal pathology Cuckoo search algorithm Swarm intelligence algorithms bionic algorithm

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An Efficient Multilevel Threshold Image Segmentation Method for COVID-19 Imaging Using Q-Learning Based Golden Jackal Optimization

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Journal of bionic Engineering 2023年第5期20卷 2276-2316页

作者： Zihao Wang Yuanbin Mo Mingyue Cui School of Artificial Intelligence Guangxi Minzu UniversityNanningChina Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi Minzu UniversityNanning530006China

From the end of 2019 until now,the Coronavirus Disease 2019(COVID-19)has been rampaging around the world,posing a great threat to people's lives and health,as well as a serious impact on economic *** the severely infectious nature of COVID-19,the diagnosis of COVID-19 has become *** through the use of Computed Tomography(CT)images is an efficient and quick ***,scientific researchers have proposed numerous segmentation methods to improve the diagnosis of CT *** this paper,we propose a reinforcement learning-based golden jackal optimization algorithm,which is named QLGJO,to segment CT images in furtherance of the diagnosis of *** learning is combined for the first time with meta-heuristics in segmentation *** strategy can effectively overcome the disadvantage that the original algorithm tends to fall into local *** addition,one hybrid model and three different mutation strategies were applied to the update part of the algorithm in order to enrich the diversity of the *** experiments were carried out to test the performance of the proposed ***,compare QLGJO with other advanced meta-heuristics using the IEEE CEC2022 benchmark ***,QLGJO was experimentally evaluated on CT images of COVID-19 using the Otsu method and compared with several well-known *** is shown that QLGJO is very competitive in benchmark function and image segmentation experiments compared with other advanced ***,the source code of the QLGJO is publicly available at https://***/Vang-z/QLGJO.

关键词： COVID-19 bionic algorithm Golden jackal optimization Image segmentation Otsu and Kapur method

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Coronavirus Mask Protection algorithm:A New Bio-inspired Optimization algorithm and Its Applications

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Journal of bionic Engineering 2023年第4期20卷 1747-1765页

作者： Yongliang Yuan Qianlong Shen Shuo Wang Jianji Ren Donghao Yang Qingkang Yang Junkai Fan Xiaokai Mu School of Mechanical and Power Engineering Henan Polytechnic UniversityJiaozuo454003China School of Mechanical Engineering Dalian University of TechnologyDalian116024China School of Software Henan Polytechnic UniversityJiaozuo454003China

Nowadays,meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization *** this paper,a COVID-19 prevention-inspired bionic optimization algorithm,named Coronavirus Mask Protection algorithm(CMPA),is proposed based on the virus transmission of *** main inspiration for the CMPA originated from human self-protection behavior against *** CMPA,the process of infection and immunity consists of three phases,including the infection stage,diffusion stage,and immune ***,wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves,which are similar to the exploration and exploitation in optimization *** study simulates the self-protection behavior mathematically and offers an optimization *** performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions,CEC2020 suite problems,and three truss design *** statistical results demonstrate that the CMPA is more competitive among these state-of-the-art ***,the CMPA is performed to identify the parameters of the main girder of a gantry *** show that the mass and deflection of the main girder can be improved by 16.44%and 7.49%,respectively.

关键词： Coronavirus Mask Protection algorithm bionic algorithm Metaheuristic algorithm Optimization algorithm Truss optimization Parameter identification

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Multi-strategies Boosted Mutative Crow Search algorithm for Global Tasks:Cases of Continuous and Discrete Optimization

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Journal of bionic Engineering 2022年第6期19卷 1830-1849页

作者： Weifeng Shan Hanyu Hu Zhennao Cai Huiling Chen Haijun Liu Maofa Wang Yuntian Teng School of Emergency Management Institute of Disaster PreventionLangfang065201China Institute of Geophysics China Earthquake AdministrationBeijing100081China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China

Crow Search algorithm(CSA)is a swarm-based single-objective optimizer proposed in recent years,which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when *** original version of the CSA has simple parameters and moderate ***,it often tends to converge slowly or get stuck in a locally optimal region due to a missed harmonizing strategy during the exploitation and exploration ***,strategies of mutation and crisscross are combined into CSA(CCMSCSA)in this paper to improve the performance and provide an efficient optimizer for various optimization *** verify the superiority of CCMSCSA,a set of comparisons has been performed reasonably with some well-established metaheuristics and advanced metaheuristics on 15 benchmark *** experimental results expose and verify that the proposed CCMSCSA has meaningfully improved the convergence speed and the ability to jump out of the local *** addition,the scalability of CCMSCSA is analyzed,and the algorithm is applied to several engineering problems in a constrained space and feature selection *** results show that the scalability of CCMSCSA has been significantly improved and can find better solutions than its competitors when dealing with combinatorial optimization *** proposed CCMSCSA performs well in almost all experimental ***,we hope the researchers can see it as an effective method for solving constrained and unconstrained optimization problems.

关键词： Crow search algorithm Feature selection Global optimization Metaheuristic algorithms Engineering problems bionic algorithm

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An Efficient Hybrid Model Based on Modified Whale Optimization algorithm and Multilayer Perceptron Neural Network for Medical Classification Problems

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Journal of bionic Engineering 2022年第5期19卷 1504-1521页

作者： Saeid Raziani Sajad Ahmadian Seyed Mohammad Jafar Jalali Abdolah Chalechale Department of Computer Engineering Razi UniversityKermanshah ***Iran Faculty of Information Technology Kermanshah University of TechnologyKermanshah ***Iran Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsMelbourne 3217Australia

Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex *** models such as stochastic gradient descent have been developed to train ***,they mainly suffer from falling into local optima leading to reduce the accuracy of ***,the convergence speed of training process depends on the initial values of weights and biases in ***,these values are randomly determined by most of the training *** deal with these issues,in this paper,we develop a novel evolutionary algorithm by modifying the original version of Whale Optimization algorithm(WOA).To this end,a nonlinear function is introduced to improve the exploration and exploitation phases in the search process of ***,the modified WOA is applied to automatically obtain the initial values of weights and biases in FNN leading to reduce the probability of falling into local *** addition,the FNN model trained by the modified WOA is used to develop a classification approach for medical diagnosis *** medical diagnosis datasets are utilized to evaluate the efficiency of the proposed ***,four evaluation metrics including accuracy,AUC,specificity,and sensitivity are used in the experiments to compare the performance of classification *** experimental results demonstrate that the proposed method is better than other competing classification models due to achieving higher values of accuracy,AUC,specificity,and sensitivity metrics for the used datasets.

关键词： Feed forward neural network Meta-heuristic algorithm Whale optimization algorithm Optimization Classification bionic algorithm

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Optimizing Financial Engineering Time Indicator Using bionics Computation algorithm and Neural Network Deep Learning

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COMPUTATIONAL ECONOMICS 2022年第4期59卷 1755-1772页

作者： Wang, Zeyu Deng, Yue Wuhan Univ Inst Qual Dev Strategy Macro Qual Management Collaborat Innovat Ctr Hube Wuhan 430072 Peoples R China

The present work aims to optimize the time index of financial engineering to improve the efficiency of financial decision-making. A Back Propagation Neural Network (BPNN) model is designed and optimized by the Ant Colony algorithm (ACA) based on the bionic algorithm and Deep Learning (DL). After introducing the basic knowledge of neural networks and bionic algorithms, the advantages and disadvantages of the algorithms are integrated for maximal effects. Besides, ACA optimizes the weights and thresholds in the neural network in complex problems to reduce the relative error, enhance the stability and accuracy, and improve the classification speed of the BPNN model. The experimental results indicate that the classification accuracy of the ACA model is 91.3%, and the area under the receiver operating characteristic curve is 0.867. Moreover, the running time of BPNN based on ACA is 2.5 s, the error is 0.2, and the required number of iteration steps is 36 times, better than the test results of similar algorithms. These results demonstrate that the improved BPNN based on ACA has higher classification efficiency, better efficiency and smaller errors than the traditional BPNN. In terms of financial engineering decision-making, the time index of decision-making has been significantly improved, which is conducive to reducing the decision-making risk of financial institutions and has a positive effect on improving the overall operational efficiency of enterprises.

关键词： bionic algorithm Deep learning Back propagation neural network Ant colony algorithm Financial engineering

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An Improved Whale algorithm and Its Application in Truss Optimization

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Journal of bionic Engineering 2021年第3期18卷 721-732页

作者： Fengguo Jiang Lutong Wang Lili Bai School of Architecture and Civil Engineering Heilongjiang University of Science and TechnologyHarbin 150022China College of Aerospace and Civil Engineering Harbin Engineering UniversityHarbin 150001China

The current Whale Optimization algorithm(WOA)has several drawbacks,such as slow convergence,low solution accuracy and easy to fall into the local optimal *** overcome these drawbacks,an improved Whale Optimization algorithm(IWOA)is proposed in this *** can enhance the global search capability by two ***,the crossover and mutation operations in Differential Evolutionary algorithm(DE)are combined with the whale optimization ***,the cloud adaptive inertia weight is introduced in the position update phase of WOA to divide the population into two subgroups,so as to balance the global search ability and local development *** and Matlab are used to establish the structure *** demonstrate the application of the IWOA,truss structural optimizations on 52-bar plane truss and 25-bar space truss were performed,and the results were are compared with that obtained by other optimization *** is verified that,compared with WOA,the IWOA has higher efficiency,fast convergence speed,better solution accuracy and *** IWOA can be used in the optimization design of large truss structures.

关键词： improve whale optimization algorithm differential evolutionary algorithm cloud theory simulating optimization bionic algorithm

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