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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 problems.In this paper,a COVID-19 prevention-inspired bionic optimization algorithm,named Coronavirus Mask Protection algorithm(CMPA),is proposed based on the virus transmission of COVID-19.The main inspiration for the CMPA originated from human self-protection behavior against COVID-19.In CMPA,the process of infection and immunity consists of three phases,including the infection stage,diffusion stage,and immune stage.Notably,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 algorithms.This study simulates the self-protection behavior mathematically and offers an optimization algorithm.The 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 problems.The statistical results demonstrate that the CMPA is more competitive among these state-of-the-art algorithms.Further,the CMPA is performed to identify the parameters of the main girder of a gantry crane.Results 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

QoS-Aware Cloud Service optimization algorithm in Cloud Manufacturing Environment

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Intelligent Automation & Soft Computing 2023年第8期37卷 1499-1512页

作者： Wenlong Ma Youhong Xu Jianwei Zheng Sadaqat ur Rehman School of Information Engineering Quzhou College of TechnologyQuzhou324000China School of Computer Science and Technology Zhejiang University of TechnologyHangzhou310023China Department of Natural and Computing Science University of AberdeenScotlandAberdeenAB243FXUK

In a cloud manufacturing environment with abundant functionally equivalent cloud services,users naturally desire the highest-quality service(s).Thus,a comprehensive measurement of quality of service(QoS)is needed.Opti-mizing the plethora of cloud services has thus become a top priority.Cloud ser-vice optimization is negatively affected by untrusted QoS data,which are inevitably provided by some users.To resolve these problems,this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantiﬁcation mechanisms.Untrusted data are assessed by an information correction method.The weights discovered by the variable precision Rough Set,which mined the evaluation indicators from historical data,providing a comprehensive performance ranking of service quality.The manufacturing cloud service optimization algorithm thus provides a quantitative reference for service selection.In experimental simulations,this method recommended the optimal services that met users’needs,and effectively reduced the impact of dis-honest users on the selection results.

关键词： Cloud manufacturing quality of service optimization algorithm rough set

DLMNN Based Heart Disease Prediction with PD-SS optimization algorithm

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Intelligent Automation & Soft Computing 2023年第2期35卷 1353-1368页

作者： S.Raghavendra Vasudev Parvati R.Manjula Ashok Kumar Nanda Ruby Singh D.Lakshmi S.Velmurugan Department of Information and Communication Technology Manipal Institute of TechnologyManipal Academy of Higher EducationManipal576104India Department of Information Science and Engineering SDM College of Engineering and TechnologyDharwad580002India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering B.V Raju Institute of TechnologyTelangana502313India Faculty of Engineering and Technology SRM Institute of Science and TechnologyNCR CampusUttar Prasesh201204India School of Computing Science and Engineering VIT Bhopal UniversityMadhya Pradesh466114India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

In contemporary medicine,cardiovascular disease is a major public health concern.Cardiovascular diseases are one of the leading causes of death worldwide.They are classiﬁed as vascular,ischemic,or hypertensive.Clinical information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart illness.Heart failure rates have risen drama-tically in recent years as a result of changes in modern lifestyles.Heart diseases are becoming more prevalent in today’s medical setting.Each year,a substantial number of people die as a result of cardiac pain.The primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of diseases.To improve the efﬁciency of the classiﬁcation algo-rithms,we construct a data pre-processing stage using feature selection.Experi-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modiﬁed Neural Network(DLMNN)model combined with the Pet Dog-Smell Sensing(PD-SS)algorithm predicted the highest classiﬁcation performance on the UCI Machine Learning Heart Disease dataset.The DLMNN-based PDSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease dataset.We demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease dataset.Our proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.

关键词： Machine learning random forest coronary heart disease cardiovascular optimization algorithm

LOA-RPL:Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion optimization algorithm to Maximize Network Lifetime

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Computers, Materials & Continua 2021年第10期69卷 351-371页

作者： Sankar Sennan Somula Ramasubbareddy Anand Nayyar Yunyoung Nam Mohamed Abouhawwash Department of Computer Science Engineering Sona College of TechnologySalem636005India VNR Vignana Jyothi Institute of Engineering&Technology Hyderabad500090India Graduate School Duy Tan UniversityDa NangVietnam550000Vietnam Faculty of Information Technology Duy Tan UniversityDa NangVietnam550000Vietnam Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMI48824USA

Energy conservation is a significant task in the Internet of Things(IoT)because IoT involves highly resource-constrained devices.Clustering is an effective technique for saving energy by reducing duplicate data.In a clustering protocol,the selection of a cluster head(CH)plays a key role in prolonging the lifetime of a network.However,most cluster-based protocols,including routing protocols for low-power and lossy networks(RPLs),have used fuzzy logic and probabilistic approaches to select the CH node.Consequently,early battery depletion is produced near the sink.To overcome this issue,a lion optimization algorithm(LOA)for selecting CH in RPL is proposed in this study.LOA-RPL comprises three processes:cluster formation,CH selection,and route establishment.A cluster is formed using the Euclidean distance.CH selection is performed using LOA.Route establishment is implemented using residual energy information.An extensive simulation is conducted in the network simulator ns-3 on various parameters,such as network lifetime,power consumption,packet delivery ratio(PDR),and throughput.The performance of LOA-RPL is also compared with those of RPL,fuzzy rule-based energyefficient clustering and immune-inspired routing(FEEC-IIR),and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm(RISARPL).The performance evaluation metrics used in this study are network lifetime,power consumption,PDR,and throughput.The proposed LOARPL increases network lifetime by 20%and PDR by 5%–10%compared with RPL,FEEC-IIR,and RISA-RPL.LOA-RPL is also highly energy-efficient compared with other similar routing protocols.

关键词： Internet of things cluster head clustering protocol optimization algorithm lion optimization algorithm network lifetime routing protocol wireless sensor networks energy consumption low-power and lossy networks

Parameters of new three-water model based on nuclear magnetic experiment and optimization algorithm

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Global Geology 2023年第1期26卷 57-62页

作者： KANG Nan HONG Xin ZHANG Lihua PAN Baozhi TANG Lei ZHANG Pengji Engineering Technology Branch of CNOOC Energy Development Co. Ltd.Tianjin 300452China College of Geo-Exploration Science and Technology Jilin UniversityChangchun 130026China

Clastic rock reservoir is the main reservoir type in the oil and gas field.Archie formula or various conductive models developed on the basis of Archie’s formula are usually used to interpret this kind of reservoir,and the three-water model is widely used as well.However,there are many parameters in the threewater model,and some of them are difficult to determine.Most of the determination methods are based on the statistics of large amount of experimental data.In this study,the authors determine the value of the parameters of the new three-water model based on the nuclear magnetic data and the genetic optimization algorithm.The relative error between the resistivity calculated based on these parameters and the resistivity measured experimentally at 100%water content is 0.9024.The method studied in this paper can be easily applied without much experimental data.It can provide reference for other regions to determine the parameters of the new three-water model.

关键词： new three-water model optimization algorithm NMR water saturation rock electric parameters

Synergistic Swarm optimization algorithm

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2557-2604页

作者： Sharaf Alzoubi Laith Abualigah Mohamed Sharaf Mohammad Sh.Daoud Nima Khodadadi Heming Jia Faculty of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Al Al-Bayt UniversityMafraq25113Jordan Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Department of Industrial Engineering College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia College of Engineering Al Ain UniversityAbu Dhabi112612United Arab Emirates Department of Civil and Architectural Engineering University of MiamiCoral Gables1251USA School of Information Engineering Sanming UniversitySanming365004China

This research paper presents a novel optimization method called the Synergistic Swarm optimization algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://***/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.

关键词： Synergistic swarm optimization algorithm optimization algorithm metaheuristic engineering problems benchmark functions

The Bedbug Meta-heuristic algorithm to Solve optimization Problems

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Journal of Bionic Engineering 2023年第5期20卷 2465-2485页

作者： Kouroush Rezvani Ali Gaffari Mohammad Reza Ebrahimi Dishabi Department of Computer Engineering Miyaneh BranchIslamic Azad UniversityMiyanehIran Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabrizIran

Small parasitic Hemipteran insects known as bedbugs(Cimicidae)feed on warm-blooded mammal’s blood.The most famous member of this family is the Cimex lectularius or common bedbug.The current paper proposes a novel swarm intelligence optimization algorithm called the Bedbug Meta-Heuristic algorithm(BMHA).The primary inspiration for the bedbug algorithm comes from the static and dynamic swarming behaviors of bedbugs in nature.The two main stages of optimization algorithms,exploration,and exploitation,are designed by modeling bedbug social interaction to search for food.The proposed algorithm is benchmarked qualitatively and quantitatively using many test functions including CEC2019.The results of evaluating BMHA prove that this algorithm can improve the initial random population for a given optimization problem to converge towards global optimization and provide highly competitive results compared to other well-known optimization algorithms.The results also prove the new algorithm's performance in solving real optimization problems in unknown search spaces.To achieve this,the proposed algorithm has been used to select the features of fake news in a semi-supervised manner,the results of which show the good performance of the proposed algorithm in solving problems.

关键词： Bedbug Meta-Heuristic algorithm optimization algorithm BMHA

A Finite Difference Method and Effective Modification of Gradient Descent optimization algorithm for MHD Fluid Flow Over a Linearly Stretching Surface

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Computers, Materials & Continua 2020年第2期62卷 657-677页

作者： Yasir Nawaz Muhammad Shoaib Arif Mairaj Bibi Javeria Nawaz Abbasi Umer Javed Amna Nazeer Stochastic Analysis&optimization Research Group Department of MathematicsAir UniversityPAF Complex E-9Islamabad44000Pakistan Department of Mathematics Comsats UniversityChak Shahzad CampusIslamabad44000Pakistan Department of Electrical and Computer Engineering Comsats UniversityIslamabadWah CampusWah Cantt47040Pakistan

Present contribution is concerned with the construction and application of a numerical method for the fluid flow problem over a linearly stretching surface with the modification of standard Gradient descent algorithm to solve the resulted difference equation.The flow problem is constructed using continuity,and Navier Stoke equations and these PDEs are further converted into boundary value problem by applying suitable similarity transformations.A central finite difference method is proposed that gives third-order accuracy using three grid points.The stability conditions of the present proposed method using a Gauss-Seidel iterative procedure is found using Von-Neumann stability criteria and order of the finite difference method is proved by applying the Taylor series on the discretised equation.The comparison of the presently modified optimisation algorithm with the Gauss-Seidel iterative method and standard Newton’s method in optimisation is also made.It can be concluded that the presently modified optimisation algorithm takes a few iterations to converge with a small value of the parameter contained in it compared with the standard descent algorithm that may take millions of iterations to converge.The present modification of the steepest descent method converges faster than Gauss-Seidel method and standard steepest descent method,and it may also overcome the deficiency of singular hessian arise in Newton’s method for some of the cases that may arise in optimisation problem(s).

关键词： Magnetohydrodynamic exact finite difference scheme optimization algorithm stability&convergence

optimization Model and algorithm for Multi-Label Learning

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Optimization Model and Algorithm for Multi-Label Learning