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

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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An Efficient optimization algorithm to Study the Stochastic Responses of Vehicle-Bridge Coupling Systems

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COMPUTING IN SCIENCE & ENGINEERING 2019年第3期21卷 6-17页

作者： Zhu, Siyu Li, Yongle Togbenou, Koffi Xiang, Tianyu Chengdu Univ Technol Coll Environm & Civil Engn Chengdu Sichuan Peoples R China Southwest Jiaotong Univ Dept Bridge Engn Chengdu Sichuan Peoples R China Xihua Univ Dept Civil & Struct Engn Chengdu Sichuan Peoples R China

An efficient algorithm is proposed for the stochastic responses of vehicle-bridge systems. The pseudo-excitation method is combined with the self-adaptive Gauss integration to handle the stochastic responses of vehicle-bridge systems, for which the statistic characteristics are calculated. The applicability and accuracy of the proposed hybrid numerical method are demonstrated by comparing the results with those from Monte Carlo simulation. In addition, nondimensional time is introduced to evaluate the stochastic responses of vehicles.

关键词： Bridges Structures Gaussian Processes Integral Equations Integration Monte Carlo Methods Time Domain Analysis Vibrations Stochastic Responses Vehicle Bridge Coupling System Pseudoexcitation Method optimization algorithm Self Adaptive Gauss Integration Monte Carlo Simulation Intelligent Vehicles Couplings Vibrations Mathematical Model Stochastic Processes Vehicle Dynamics Monte Carlo Methods Stochastic Responses Vehicle Bridge System Pseudo Excitation Method Self Adaptive Gauss Integration

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Efficient slope reliability analysis using a surrogate-assisted normal search particle swarm optimization algorithm

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第1期11卷 173-194页

作者： Yuan, Yi-li Hu, Chang-ming Li, Liang Xu, Jian Hou, Xu-hui Xian Univ Sci & Technol Sch Architecture & Civil Engn Xian 710055 Shaanxi Peoples R China Xian Univ Architecture & Technol Coll Civil Engn Xian 710055 Shaanxi Peoples R China Qinghai Univ Sch Civil Engn Xining 810016 Qinghai Peoples R China Qinghai Prov Key Lab Energy Saving Bldg Mat & Engn Xining 810016 Qinghai Peoples R China

Sampling of training data is the most important step in active learning slope reliability analysis, which controls the analysis accuracy. In this study, a novel surrogate-assisted normal search particle swarm optimization (SANSPSO) was proposed to enhance the accuracy and robustness of existing methodologies. In SANSPSO, the sampling process was considered a minimum problem with an objective function defined as the absolute value of the performance function. Initiated with a normal search paradigm and supplemented by three algorithm strategies, this approach seeks to preserve the continuity of the solution while refining the algorithm's efficacy and efficiency. To reduce computation cost, surrogate-assistance was used, in which a surrogate model substitutes the objective function in most iterations. This surrogate model evolves during the iteration process and ultimately replaces the actual performance function within Monte Carlo simulation. Finally, this study presents a comparative study with five state-of-the-art methods across four explicit problems and three engineering cases, where test data suggest that the SANSPSO methodology yields a 20% improvement in accuracy and a 30% rise in stability under different dimensional problems relative to the most efficacious of the alternate methods assessed because of the improved and more consistent prediction of limit state function. These findings substantiate the validity and robustness of the SANSPSO approach. Graphical Abstract

关键词： reliability analysis swarm intelligence limit state function sequential design of experiments probability of failure optimization algorithm

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A hybrid GA-PSO optimization algorithm for conformal antenna array pattern synthesis

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2018年第13期32卷 1601-1615页

作者： Liang, Zhipeng Ouyang, Jun Yang, Feng Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu Sichuan Peoples R China

This paper introduces a hybrid genetic algorithm and particle swarm optimization algorithm (GA-PSO) dedicated to pattern synthesis of conformal antenna arrays. Taking advantages of both methods, the proposed hybrid GA-PSO optimization algorithm has fast convergence speed and high convergence accuracy when applied to antenna array pattern synthesis. To show the performance of the hybrid optimization algorithm, several typical test functions and optimization examples of a linear array pattern synthesis are illustrated. Finally, a 4x2 cylindrical conformal microstrip antenna array as a practical synthesis example is studied to demonstrate the proposed algorithm. The simulated and measured results have shown the proposed method is effective and reliable for conformal antenna array pattern synthesis.

关键词： optimization algorithm pattern synthesis conformal antenna array

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Incomprehensible but Intelligible-in-time logics: Theory and optimization algorithm

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KNOWLEDGE-BASED SYSTEMS 2023年第1期264卷

作者： Mirrashid, Masoomeh Naderpour, Hosein Semnan Univ Semnan Iran

The human mind is a complex biological member with unique abilities. Today, even the most advanced computers cannot do all the brain's calculations in one second. The mind is teachable, and its logic will change based on what it has learned over time. This behavior will be the base of a theory presented in this article, named Incomprehensible but Intelligible-in-time (IbI) Logics. From a mental point of view and according to human knowledge, what it has not learned is a non-logic. However, this may change through time. Meanwhile, the IbI logic is a non-logic that may become an obvious logic in the future. This article has been formed to introduce a side of science to identify IbI logic and organize the mind's scientific idioms. Based on the introduced theory, a new optimization algorithm called IbI Logics algorithm (ILA) was also presented and compared with some other algorithms. The performance of the proposed algorithm in several constrained and unconstrained examples were evaluated. The results showed the acceptable performance and potential of the ILA for optimization goals.(c) 2023 Elsevier B.V. All rights reserved.

关键词： optimization algorithm IbI logic ILA Biological inspiration Computational intelligence

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Coverage enhancing of 3D underwater sensor networks based on improved fruit fly optimization algorithm

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SOFT COMPUTING 2017年第20期21卷 6019-6029页

作者： Zhang, Ying Wang, Mingxing Liang, Jixing Zhang, Haiyang Chen, Wei Jiang, Shengming Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Tennessee State Univ Dept Comp Sci Nashville TN 37209 USA

The coverage rate of the underwater sensor networks directly influences on the monitoring efficiency in underwater environment, and it can be effectively improved by adjusting the positions of the mobile nodes reasonably for a 3D underwater sensor network which consists of mobile nodes as underwater robots like Autonomous Underwater Vehicles. An optimal deployment method can quickly set up a reasonable topology of the sensor networks and achieve a higher efficiency for detecting or investigating. An optimal algorithm of coverage enhancing for 3D Underwater sensor networks based on improved Fruit Fly optimization algorithm (UFOA) is proposed in this paper. This method realizes the global optimal coverage based on foraging behavior of fruit flies, and it has the features of higher speed of convergence, few parameters to set up and stronger global searching ability. Simulation result indicates that the proposed UFOA method can significantly improve the effective coverage rate of the sensor networks compared with some widely studied PSO and IPSO algorithms.

关键词： 3D underwater sensor networks Deployment optimization algorithm Coverage rate Fruit fly

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Bistatic RCS Pattern Synthesis With a Receiver-Transmitter Metasurface and a Phase Distribution optimization algorithm

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2024年第12期66卷

作者： Sima, Boyu Xu, Yibang Li, Huangyan Wang, Xiang Zong, Zhiyuan Kang, Wei Wu, Wen Nanjing Univ Sci & Technol Sch Elect & Opt Engn Key Lab Near Range RF Sensing ICs & Microsyst NJUS Minist Educ Nanjing Peoples R China

This paper proposes a metasurface design method to achieve bistatic radar cross-section (RCS) pattern synthesis in space, based on a receiver-transmitter metasurface (RTMS) and a genetic algorithm for phase distribution optimization. Due to the slots on the ground between the adjacent metasurface elements, the unwanted interunit coupling effect is significantly reduced. Based on the theoretical analysis model of the phased array, the corresponding phase distribution of the required RCS pattern is optimized by a genetic algorithm. According to the required phase distribution, the corresponding RTMS units are designed, and the overall RTMS structure is finally constructed. The simulated results demonstrate that the electromagnetic (EM) waves scattered by the proposed RTMS are concentrated in the desired directions of 0 degrees and +/- 49 degrees which is consistent with the design intention. Both simulated and measured results verify that the proposed RTMS design methodology can realize the bistatic RCS control in space with a customized shape.

关键词： bistatic RCS synthesis metasurface optimization algorithm phase distribution receiver-transmitter

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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 *** is an effective technique for saving energy by reducing duplicate *** a clustering protocol,the selection of a cluster head(CH)plays a key role in prolonging the lifetime of a ***,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 ***,early battery depletion is produced near the *** overcome this issue,a lion optimization algorithm(LOA)for selecting CH in RPL is proposed in this ***-RPL comprises three processes:cluster formation,CH selection,and route establishment.A cluster is formed using the Euclidean *** selection is performed using *** establishment is implemented using residual energy *** extensive simulation is conducted in the network simulator ns-3 on various parameters,such as network lifetime,power consumption,packet delivery ratio(PDR),and *** 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 *** proposed LOARPL increases network lifetime by 20%and PDR by 5%–10%compared with RPL,FEEC-IIR,and ***-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

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Effective Neural Network Training With a New Weighting Mechanism-Based optimization algorithm

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IEEE ACCESS 2019年 7卷 72403-72410页

作者： Yu, Yunlong Liu, Fuxian Air Force Engn Univ Dept Air Def & Antimissile Xian 710051 Shaanxi Peoples R China

First-order gradient-based optimization algorithms have been of core practical importance in the field of deep learning. In this paper, we propose a new weighting mechanism-based first-order gradient descent optimization algorithm, namely NWM-Adam, to resolve the undesirable convergence behavior of some optimization algorithms which employ fixed sized window of past gradients to scale the gradient updates and improve the performance of Adam and AMSGrad. The NWM-Adam is developed on the basis of the idea, i.e., placing more memory of the past gradients than the recent gradients. Furthermore, it can easily adjust the degree to which how much the past gradients weigh in the estimation. In order to empirically test the performance of our proposed NWM-Adam optimization algorithm, we compare it with other popular optimization algorithms in three well-known machine learning models, i.e., logistic regression, multi-layer fully connected neural networks, and deep convolutional neural networks. The experimental results show that the NWM-Adam can outperform other optimization algorithms.

关键词： Deep learning optimization algorithm learning rate neural network training

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Dynamic Machine Learning Global optimization algorithm and Its Application to Aerodynamics

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JOURNAL OF PROPULSION AND POWER 2023年第4期39卷 524-539页

作者： Zhang, Zi-Qing Li, Pei-Jing Li, Qing-Kuo Dong, Xu Lu, Xin-Gen Zhang, Yan-Feng Chinese Acad Sci Inst Engn Thermophys Key Lab Light Duty Gas Turbine Beijing 100080 Peoples R China Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Engn Thermophys Distributed Generat Renewable Energy Lab Beijing 100080 Peoples R China Chinese Acad Sci Inst Engn Thermophys Key Lab Light Duty Gas Turbine Beijing 100080 Peoples R China

A dynamic extreme gradient boosting (XGBoost) and MaxLIPO trust region parallel global optimization algorithm is proposed in this paper, and it is applied to the turbomachinery blade aerodynamic optimization coupled with an in-house graphics processing unit (GPU) heterogeneous accelerated compressible flow solver, AeroWhale. The algorithm combines an accurate machine learning regression model, an efficient nongradient optimization method with no hyperparameters, a dynamic update regression strategy, and double convergence criteria to achieve high optimization accuracy and efficiency. The optimization results on the test function indicate that the number of objective function calls is less than 2% of that required by a traditional genetic algorithm, which greatly reduces the optimization time. The dynamic XGBoost model ensures that the regression model accuracy near the optimum is relatively high, which is attributed to the update strategy. The error between the optimal value identified by the proposed algorithm and the theoretical value is only 0.52% after several objective function calls. Finally, the aerodynamic optimization algorithm is applied to the LS89 high-pressure turbine, and the total pressure loss is reduced by 13.16%. The sensitivity of each optimization feature to the objective function is determined, showing that the blade suction surface control point near the trailing edge has the greatest impact on aerodynamic performance.

关键词： optimization algorithm Support Vector Machine Aerodynamic Characteristics High Pressure Turbine Turbomachinery Computational Fluid Dynamics XGBoost MaxLIPO GPU High Performance Computing

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