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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

Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection

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Journal of Bionic Engineering 2023年第3期20卷 1153-1174页

作者： Xin Wang Xiaogang Dong Yanan Zhang huiling chen School of Mathematics and Statistics Changchun University of TechnologyChangchunJilin130012China School of Management Xi’an Jiaotong UniversityXi’anShaanxi710049China Information Construction Office Changchun University of TechnologyChangchunJilin130012China College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China

Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross Optimization Algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum;the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend;and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems’ dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization;for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features.

关键词： Harris hawks optimization Bioinspired algorithm Global optimization Engineering optimization Feature selection

Double Mutational Salp Swarm Algorithm:From Optimal Performance Design to Analysis

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Journal of Bionic Engineering 2023年第1期20卷 184-211页

作者： Chao Lin Pengjun Wang Xuehua Zhao huiling chen College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China College of Electrical and Electronic Engineering Wenzhou UniversityWenzhou325035China School of Digital Media Shenzhen Institute of Information TechnologyShenzhen518172China

The Salp Swarm Algorithm(SSA)is a population-based Meta-heuristic Algorithm(MA)that simulates the behavior of a group of salps foraging in the *** the basic SSA has stable exploration capability and convergence speed,it still can fall into local optimum when solving complex optimization problems,which may be due to low utilization of population information and unbalanced exploration-to-exploitation ***,this study proposes a Double Mutation Salp Swarm Algorithm(DMSSA).In this study,a Cuckoo Mutation Strategy(CMS)and an Adaptive DE Mutation Strategy(ADMS)are introduced into the structure of the original *** former mutation strategy is summarized as three basic operations:judgment,shuffling,and *** purpose is to fully consider the information among search agents and use the differences between different search agents to participate in the update of positions,making the optimization process both diverse in exploration and minor in *** latter strategy employs three basic operations:selection,mutation,and *** the follower part,some individuals do not blindly adopt the original follow ***,the global optimal position and differences are considered,and the variation factor is adjusted adaptively,allowing the new algorithm to balance exploration,exploitation,and convergence *** evaluate the performance of DMSSA,comparisons are made with numerous algorithms on 30 IEEE CEC2014 benchmark *** statistical results confirm the better performance and significant difference of DMSSA in solving benchmark function ***,the applicability and scalability of DMSSA to optimization problems with constraints are further confirmed in three experiments on classical engineering design optimization *** source code of the proposed algorithm will be available at:https://***/ncjsq/Double-Mutational-Salp-Swarm-Algorithm.

关键词： Salp swarm algorithm Meta-heuristic algorithm Global optimization-Exploration Exploitation Bionic

Balancing Exploration–Exploitation of Multi-verse Optimizer for Parameter Extraction on Photovoltaic Models

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Journal of Bionic Engineering 2024年第2期21卷 1022-1054页

作者： Yan Han Weibin chen Ali Asghar Heidari huiling chen Xin Zhang College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran School of Biomedical Engineering National Engineering Research Center of Ophthalmology and OptometryEye HospitalWenzhou Medical UniversityWenzhou325027China Key Lab of Biohealth Materials and Chemistry of Wenzhou Wenzhou UniversityWenzhouChina Key Laboratory of Intelligent Informatics for Safety&Emergency of Zhejiang Province Wenzhou UniversityWenzhouChina

Extracting photovoltaic(PV)model parameters based on the measured voltage and current information is crucial in the simulation and management of PV *** accurately and reliably extract the unknown parameters of different PV models,this paper proposes an improved multi-verse optimizer that integrates an iterative chaos map and the Nelder–Mead simplex method,*** experiments verified that the proposed INMVO fueled by both mechanisms has more affluent populations and a more reasonable balance between exploration and ***,to verify the feasibility and competitiveness of the proposal,this paper employed INMVO to extract the unknown parameters on single-diode,double-diode,three-diode,and PV module four well-known PV models,and the high-performance techniques are selected for *** addition,the Wilcoxon signed-rank and Friedman tests were employed to test the experimental results *** evaluation metrics,such as root means square error,relative error,absolute error,and statistical test,demonstrate that the proposed INMVO works effectively and accurately to extract the unknown parameters on different PV models compared to other *** addition,the capability of INMVO to stably and accurately extract unknown parameters was also verified on three commercial PV modules under different irradiance and *** conclusion,the proposal in this paper can be implemented as an advanced and reliable tool for extracting the unknown parameters of different PV *** that the source code of INMVO is available at https://***/woniuzuioupao/INMVO.

关键词： Photovoltaic models Multi-verse optimizer Nelder-Mead simplex Iterative chaos map

Bio-Inspired Optimization in Engineering and Sciences

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Computer Modeling in Engineering & Sciences 2023年第11期137卷 1065-1067页

作者： Yudong Zhang huiling chen School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China

Bio-inspired optimization algorithms[1,2]are a set of optimization algorithms inspired by natural phenomena,such as evolutionary processes,social behaviours,and swarm intelligence[3].These algorithms attempt to simulate these processes to solve optimization problems[4,5].Classical bio-inspired algorithms include genetic algorithm,ant colony optimization,artificial bee colony,particle swarm optimization,firefly algorithm,Japanese tree frog algorithm,Harris hawks optimization[6],slime mould algorithm[7],grey wolf optimization,sparrow search algorithm,whale optimization algorithm,***.1 shows the taxonomy of common bio-inspired optimization *** are some recent newly proposed bio-inspired algorithms,such as Siberian tiger optimization[8],jellyfish search algorithm[9],etc.

关键词： artificial optimization algorithm

Study on circle detection algorithm based on data dispersion

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High Technology Letters 2017年第4期23卷 399-403页

作者： Shuai Liguo Wei Youying chen huiling School of Mechanical and Electrical Engineering Henan Institute of Science and Technology Xinxiang 453003 P. R. China School of Mechanical Engineering Southeast University Nanjing 211189 P. R. China

To reduce time-consuming,a new algorithm is proposed for circle detection based on the theory of data dispersion. The center coordinates and radius can be detected with the following steps in this algorithm precisely and quickly. Firstly,image processing is needed to extract the boundary of the primary image,which is almost like a circle in shape,and after that,the original circle is reduced to a single-pixel width circle by image processing. Secondly,the center coordinates are calculated by three selected points on the circle. There might be a deviation between the calculated center and real center. Thirdly,a square area is determined for the center coordinates computing with an experimental range and each pixel inside the square is a potential center. Fourthly,the center is computed with distance criterion and the center coordinate is determined when the variance reaches the minimum. Lastly,the radius is equal to the means of the distance vector with minimum *** are conducted and the results show that the proposed algorithm gets the same accuracy and better real-time performance in comparison with traditional Hough transform.

关键词： circle detection center coordinates dispersion

The Colony Predation Algorithm

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Journal of Bionic Engineering 2021年第3期18卷 674-710页

作者： Jiaze Tu huiling chen Mingjing Wang Amir H.Gandomi College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Faculty of Engineering and Information Technology University of Technology SydneyUltimoNSW 2007Australia

This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in *** utilizes a mathematical mapping following the strategies used by animal hunting groups,such as dispersing prey,encircling prey,supporting the most likely successful hunter,and seeking another ***,the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals'selective abandonment *** paper also presents a new way to deal with cross-border situations,whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm's exploitation *** proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm's efficacy in optimizing engineering *** results show that the proposed algorithm exhibits competitive,superior performance in different search landscapes over the other ***,the source code of the CPA will be publicly available after publication.

关键词： Colony Predation Algorithm optimization nature-inspired computing meta-heuristic engineering problems

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

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Journal of Bionic Engineering 2023年第2期20卷 797-818页

作者： Jie Xing Hanli Zhao huiling chen Ruoxi Deng Lei Xiao Key Laboratory of Intelligent Informatics for Safety&Emergency of Zhejiang Province Wenzhou UniversityWenzhou325035China

Whale optimization algorithm(WOA)tends to fall into the local optimum and fails to converge quickly in solving complex *** address the shortcomings,an improved WOA(QGBWOA)is proposed in this ***,quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal ***,a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in *** verify the advantages of QGBWOA,comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10,30,50,and 100 and on CEC 2020 test with dimension ***,the performance results were tested using Wilcoxon signed-rank(WS),Friedman test,and post hoc statistical tests for statistical *** accuracy and speed are remarkably improved,as shown by experimental ***,feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world *** proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics.

关键词： Whale optimization algorithm Quasi-opposition-based learning Gaussian barebone Image segmentation Feature selection Bionic algorithm

Wearable gas/strain sensors based on reduced graphene oxide/linen fabrics

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Frontiers of Materials Science 2019年第3期13卷 305-313页

作者： Xia HE Qingchun LIU Jiajun WANG huiling chen School of Art and Design Zhejiang Sci-Tech UniversityHangzhou 310018China School of Art Design Zhejiang A&F UniversityHangzhou 311300China

Multifunctional wearable e-textiles have been a focus of much attention due to their great potential for healthcare,sportswear,fitness,space,and military *** them,electroconductive textile yarn shows great promise for use as the next-generation flexible sensors without compromising properties and comfort of usual ***,a myriad of efforts have been devoted to improving performance and functionality of wearable ***,the current manufacturing process of metal-based electroconductive textile yarn is expensive,unscalable,and environmentally *** this work,we report the preparation of multifunctional reduced graphene oxide/linen(RGO/LN)fabrics through the reduction and the followed suction *** RGO/LN fabric could serve as the methane gas sensor,which exhibited high sensitivity,remarkable reliability and ***,the RGO/LN fabric sensor exhibited good moisture permeability and air *** present work reveals that RGO/LN fabric has great potential as wearable smart devices in personal healthcare applications.

关键词： wearable sensor reduced graphene oxide linen fabric methane