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

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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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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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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A Novel Impedance Estimation Method for Determining Voltage Unbalance Contributions Based on an optimization algorithm

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IEEE ACCESS 2024年 12卷 145944-145954页

作者： Brito, Vinicius Henrique Farias de Oliveira, Jose Carlos Gregory, Raquel Cristina Filiagi Univ Fed Uberlandia Elect Engn Fac BR-38400902 Uberlandia Brazil Univ Fed Ceara Dept Elect Engn BR-60020181 Fortaleza Brazil

The problem of "unbalance responsibility-sharing" is a matter of great concern for utility and consumer entities due to the increase in voltage unbalances, especially in electrical distribution systems. This issue arises from the need to determine the individual contributions of the utility and the consumer to the total unbalance at the point of common coupling between the entities. In the literature, the Superposition Method stands out as an effective approach to solving this problem. However, this procedure requires utility and consumer negative-sequence impedances, which are difficult to obtain in real-life scenarios. There is a variety of impedance estimation methods to overcome this issue, with emphasis on non-invasive methods. According to the literature, some of these procedures have practical and accuracy limitations. Therefore, this paper proposes a novel non-invasive impedance estimation method, termed the Correlation Minimization Method (CMM), which is based, essentially, on an optimization algorithm. In addition, computational simulations and field tests were carried out to evaluate the performance of the proposed method in comparison with existing approaches. The results show that the proposed method performed satisfactorily, even in the most adverse situations.

关键词： Impedance Estimation Equivalent circuits Fluctuations Harmonic analysis Voltage measurement optimization Frequency modulation Voltage Vectors Power quality Impedance estimation method non-invasive method optimization algorithm power quality unbalance unbalance responsibility-sharing problem

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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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Studying the varied shapes of gold clusters by an elegant optimization algorithm that hybridizes the density functional tight-binding theory and the density functional theory

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COMPUTER PHYSICS COMMUNICATIONS 2017年 220卷 143-149页

作者： Yen, Tsung-Wen Lim, Thong-Leng Yoon, Tiem-Leong Lai, S. K. Natl Cent Univ Dept Phys Complex Liquids Lab Chungli 32001 Taiwan Multimedia Univ Fac Engn & Technol Melaka CampusJalan Ayer Keroh Lama Bukit Beruang 75450 Melaka Malaysia Univ Sains Malaysia Sch Phys Usm 11800 Pulau Penang Malaysia

We combined a new parametrized density functional tight-binding (DFTB) theory (Fihey et al. 2015) with an unbiased modified basin hopping (MBH) optimization algorithm (Yen and Lai 2015) and applied it to calculate the lowest energy structures of Au clusters. From the calculated topologies and their conformational changes, we find that this DFTB/MBH method is a necessary procedure for a systematic study of the structural development of Au clusters but is somewhat insufficient for a quantitative study. As a result, we propose an extended hybridized algorithm. This improved algorithm proceeds in two steps. In the first step, the DFTB theory is employed to calculate the total energy of the cluster and this step (through running DFTB/MBH optimization for given Monte-Carlo steps) is meant to efficiently bring the Au cluster near to the region of the lowest energy minimum since the cluster as a whole has explicitly considered the interactions of valence electrons with ions, albeit semi-quantitatively. Then, in the second succeeding step, the energy-minimum searching process will continue with a skilledly replacement of the energy function calculated by the DFTB theory in the first step by one calculated in the full density functional theory (DFT). In these subsequent calculations, we couple the DFT energy also with the MBH strategy and proceed with the DFT/MBH optimization until the lowest energy value is found. We checked that this extended hybridized algorithm successfully predicts the twisted pyramidal structure for the Au-40 cluster and correctly confirms also the linear shape of C-8 which our previous DFTB/MBH method failed to do so. Perhaps more remarkable is the topological growth of Au-n: it changes from a planar (n = 3-11) -> an oblate-like cage (n = 12-15) -> a hollow-shape cage (n = 16-18) and finally a pyramidal-like cage (n = 19, 20). These varied forms of the clusters shapes are consistent with those reported in the literature. (C) 2017 Elsevier B.V. All righ

关键词： Metallic cluster Density functional theory optimization algorithm Topological transition

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Optimal operation for marine hybrid power system: Novel optimization algorithm and comprehensive analysis for penalty functions

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第5期43卷 6627-6649页

作者： An, Qing Tang, Ruoli Li, Xueyan Zhang, Xiaodi Li, Xin Wuchang Univ Technol Sch Artificial Intelligence Wuhan Peoples R China Wuhan Univ Technol Sch Naval Architecture Ocean & Energy Power Engn Wuhan Peoples R China State Grid Beijing Elect Maintenance Co Beijing Peoples R China

In order to optimally control the marine hybrid power system (HPS) under increasingly complex regulation constraints or hardware constraints, an efficient power-flowscheduling model and optimization algorithm are of great importance. This work focuses on the optimal power-flow scheduling of marine HPS, especially on the efficiency improvement of the penalty functions for satisfying complex constraints. To be specific, an optimal operation model of marine HPS is discussed, and the complex model constraints are described as various penalty functions. Secondly, a novel optimization algorithm, namely adaptive multi-context cooperatively coevolving differential evolution algorithm with random topology and mutated context vector (AMCCDE(-rt-mcv)) is developed to optimize the aforementioned model. In order to ensure the satisfaction of the complex model constraints, the detailed forms for penalty functions are researched and the optimal parameters for penalty functions are comprehensively compared, analysed and tested by a set of numerical experiments. Finally, the developed methodologies are tested by simulation experiments. Experimental results show that the damping factor, exponent parameter and punish strength constant effect the efficiency of penalty functions a lot, and the developed penalty functions can effectively satisfy all the model constraints with fast response speed. With the integration of penalty functions, the developed methodology can obtain promising performance on the optimal scheduling of the evaluated marine HPS.

关键词： Hybrid power system optimal energy management penalty function optimization algorithm differential evolution

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Multi-core-, multi-thread-based optimization algorithm for large-scale traveling salesman problem

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ALEXANDRIA ENGINEERING JOURNAL 2021年第1期60卷 189-197页

作者： Wei, Xin Ma, Liang Zhang, Huizhen Liu, Yong Univ Shanghai Sci & Technol Sch Management Shanghai 200093 Peoples R China

With the rapid development of general hardware technology, microcomputers with multi-core CPUs have been widely applied in commercial services and household usage in the last ten years. Multi-core chips could, theoretically, lead to much better performance and computational efficiency than single-core chips. But so far, they have not shown general advantages for users, other than for operating systems and some specialized software. It is not easy to transform traditional single-core-based algorithms into multi-core-, multi-thread-based algorithms that can greatly improve efficiency, because of difficulties in computation and scheduling of hardware kernels, and because some programming languages cannot support multi-core, multi-thread programming. Therefore, a kind of multi-core-, multi-thread-based fast algorithm was designed and coded with Delphi language for the medium- and large-scale traveling salesman problem instances from TSPLIB, which can fully speed up the searching process without loss of quality. Experimental results show that the algorithm proposed can, under the given hardware limitations, take full advantage of multi-core chips and effectively balance the conflict between increasing problem size and computational efficiency and thus acquire satisfactory solutions. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

关键词： Multi-core Multi-thread Traveling Salesman Problem optimization algorithm

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