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Non-uniformly spaced linear antenna array design by means of PEEC approach applying Cheetah optimization algorithm

INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2019年第1_SUPPL期60卷 S15-S24页

作者： Moreno, Sinvaldo R. Klein, Carlos E. Mariani, Viviana C. Coelho, Leandro dos S. Alotto, Piergiorgio Torchio, Riccardo Bauernfeind, Thomas Baumgartner, Paul Univ Fed Parana Dept Elect Engn Curitiba Parana Brazil Pontificia Univ Catolica Parana Ind Syst Engn Grad Program Curitiba Parana Brazil Pontificia Univ Catolica Parana Dept Mech Engn Curitiba Parana Brazil Univ Padua Dipartimento Ingn Ind Padua Italy Graz Univ Technol Inst Fundamentals & Theory Elect Engn Graz Austria

This paper presents the application of the Partial Element Equivalent Circuit (PEEC) approach, which is a full wave electromagnetic modelling technique for conductors embedded in arbitrary dielectrics based on equivalent circuits, to the optimal design of antennas with non-uniform spacing between the array elements. The design optimization problem is solved by means of the new nature-inspired Cheetah metaheuristic. The main aim of this paper is to introduce the Cheetah optimization algorithm to the electromagnetics and antenna community. The results are compared to two well-known optimization algorithms and to show the effectiveness of the proposed algorithm on a realistic benchmark problem.

关键词： Antenna array Cheetah optimization algorithm genetic algorithm partial element equivalent circuit particle swarm optimization algorithm

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Dynamic equivalent soil characteristics identification using earthquake records

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Earthquake Science 2018年第3期31卷 166-173页

作者： Asskar Janalizadeh Choobbasti Saman Soleimani Kutanaei Department of Civil Engineering Babol University of Technology

Techniques for soil property estimation can be categorized into two main groups, in-situ and laboratory methods. Previous investigations indicated that strong ground motions record provides a very useful tool to estimating the in-situ characteristics of soil. The main objective of the present work is to utilize the particle swarm optimization algorithm（PSOA） integrated with linear site response method to obtain the equivalent soil profile characteristics from the available surface and bedrock earthquake motion records. To demonstrate the numerical efficiency and the validity of this approach, the procedure is validated against an available case. Then this procedure is utilized to identify the soil properties profiles of the site by using strong ground motions data recorded during the Bam earthquake of December 26, 2003. The magnitude and PGA of Bam earthquake were MW 6.6 and 0.8 g respectively.

关键词： identification ground response particle swarm optimization algorithm site effect

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Gridless Net Routing of Integrate Circuit with particle swarm optimization algorithm

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Journal of Energy and Power Engineering 2011年第9期5卷 899-904页

作者： X.C. Huang Power Dispatching ＆ Communication Center Power Grid of Henan Zhengzhou 450000 China

particle swarm optimization algorithm is presented for the layout of ＂Integrate Circuit （IC）＂ design. particle swarm optimization based on swarm intelligence is a new evolutionary computational tool and is successfully applied in function optimization, neural network design, classification, pattern recognition, signal processing and robot technology and so on. A modified algorithm is presented and applied to the layout of IC design. For a given layout plane, first of all, this algorithm generates the corresponding grid group by barriers and nets＇ ports with the thought ofgridless net routing, establishes initialization fuzzy matrix, then utilizes the global optimization character to find out the best layout route only if it exits. The results of model simulation indicate that PSO algorithm is feasible and efficient in IC layout design.

关键词： particle swarm optimization algorithm gridless net routing layout optimization prufer number.

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Lifetime Enhancement of Wireless Sensor Networks by using MFO algorithm

Lifetime Enhancement of Wireless Sensor Networks by using MF...

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International Conference on Computing, Power and Communication Technologies (GUCON)

作者： Pandey, Ashish Rajan, Abhishek Nandi, Arnab Natl Inst Technol Silchar Dept Elect & Commun Engn Cachar 788010 Assam India Natl Inst Technol Sikkim Dept Elect & Elect Engn Sikkim 737139 India

ISBN: (纸本)9781538644911

Wireless sensor networks (WSNs) are extensively used in numerous applications from sensing and tracking to atmospheric quantity measurement. Sensor nodes used in the network are mostly battery powered and due to odd terrain of deployment it is not always easy to replace them. For managing this battery resource of the WSNs various schemes have been proposed and implemented. In this paper we have used Moth Flame optimization algorithm in the clustering and routing for enhancing the lifetime of the sensor network. Outcome through this algorithm are compared with the previously used various algorithms like particle swarm optimization, Genetic algorithm and Least Distance Clustering algorithm.

关键词： WSN Moth flame optimization particle swarm optimization algorithm PSO Genetic algorithm Clustering Routing

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Selection of kernel function for least squares support vector machines in downburst wind speed forecasting 11

Selection of kernel function for least squares support vecto...

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11th International Symposium on Computational Intelligence and Design (ISCID)

作者： Li, Zhou Li, Chunxiang Shanghai Univ Dept Civil Engn Shanghai Peoples R China

ISBN: (纸本)9781538685273

In recent years, kernel machine learning algorithm (e.g. least square support vector machine, LSSVM) is a commonly method for time series prediction, as the main unit, the type of kernel function is very crucial. In this paper, a hybrid model of variational mode decomposition (VMD), particle swarm optimization (PSO) and LSSVM is proposed to forecast the downburst wind speed series. Linear kernel function, polynomial kernel function, radial basis function, Morlet wavelet kernel function, Mexican Hat wavelet kernel function and their combinations are selected to demonstrate the influence of different kernel functions on prediction results. The results indicate that the Morlet+RBF combined kernel function is considerably effective in enhancing the forecasting accuracy of the VMD-PSO-LSSVM model.

关键词： kernel functions least squares support vector machine particle swarm optimization algorithm variational mode decomposition wind speed forecasting

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1st order nonstationary element optimization using PSO algorithm 23

1st order nonstationary element optimization using PSO algor...

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23rd International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Wiechetek, Katarzyna Kaszynski, Roman West Pomeranian Univ Technol Fac Elect Engn Szczecin Poland

ISBN: (纸本)9781538643259

The paper presents a new concept of establishing the varying function parameters in order to reduce the duration of the transition processes. The optimization was conducted using particle swarm optimization algorithm (PSO). The main object of the research was 1st order element with two outputs: a low-pass and a high-pass. By selecting the right values of the parameters a better operation and considerably shorter settling times were achieved. After applying multiobjective optimization algorithm the system could adapt to more requirements at once.

关键词： nonstationary filter time-varying parameters transient state multiobjective optimization particle swarm optimization algorithm

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Research on Hospital Operation Index Prediction Method Based on PSO-Holt-Winters Model 18

Research on Hospital Operation Index Prediction Method Based...

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2nd International Conference on Computer Science and Application Engineering (CSAE)

作者： Zheng, Tao Zhang, Ya Fan, Chenhao Shanghai Jiao Tong Univ Tongren Hosp Inst Image Commun & Networking Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Image Commun & Networking Shanghai Peoples R China Shanghai Jiao Tong Univ Tongren Hosp Shanghai Peoples R China

ISBN: (纸本)9781450365123

The prediction(1) of hospital operation indicators is of great significance and can provide an important basis for hospital operation and management, so as to assist managers to make decisions such as resource allocation and task planning. In order to solve this problem, a novel Holt-Winters model based on particle swarm optimization (PSO) is proposed, aiming at the accurate prediction of hospital operating indicators. In the process of model construction, according to the characteristics of time series data of hospital operation indicators, a time decay mean square error function is constructed as an optimization function of particle swarm optimization algorithm, which enables particle swarm optimization algorithm to better fit recent historical data and grasp the characteristics of recent time series, so as to improve the prediction accuracy. An example is given to analyze the hospital operation index data of a third-class hospital from 2014 to 2017. By initializing the parameters of the model and optimizing the parameters, the improved PSO-Holt-Winters model of TDMSE-1 is established, which can accurately predict the outpatient, inpatient, emergency, discharged and surgical cases.

关键词： Hospital operation indicators particle swarm optimization algorithm Holt - Winters model

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A Optimal Path Planning of Multi-destinations for Mobile Robot in Complex Environment

A Optimal Path Planning of Multi-destinations for Mobile Rob...

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Chinese Automation Congress (CAC)

作者： Liu, Ping Wang, Chunying Qin, Hongzheng Sun, Jinju Shandong Agr Univ Coll Mech & Elect Engn Tai An 271018 Shandong Peoples R China

ISBN: (纸本)9781728113128

A lot of progress has been made in the research of point-to-point and complete coverage path planning of mobile robots, while the multi-destinations path planning is seldom reported in the literatures. Based on particle swarm optimization and vortex search algorithms, this paper proposes a multi-destinations path planning approach, which is suited to plan a feasible, safe and optimal path between multi-destinations in complex home environment for mobile robot. Firstly, the sequence of the destinations is quickly optimized by using the particle swarm optimization algorithm. Then, the collision-free path between destinations is obtained via vortex search algorithm with its advantages of high efficiency and small computation. Finally, simulation results show that the proposed multi-destinations path planning approach has the good explorative and exploitation ability, while the path planned by proposed approach is smooth, short.

关键词： Multi-destinations optimization particle swarm optimization algorithm Vortex search algorithm Optimal path planning

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Iterative PSO algorithms for GRAP Problems

Iterative PSO Algorithms for GRAP Problems

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2019 International Conference on Artificial Intelligence, Control and Automation Engineering(AICAE 2019)

作者： Fang LU Dong-kui LI Faculty of Mathematics Science School Baotou Teachers College Editorial Department of Journal Baotou Teachers College

This paper studies the grap problems of two-state, in which the subsystem allows components to be mixed(i.e. the subsystem selects components from several types of heterogeneous components, and the number of selected components types >=1). Each component has a fixed reliability, weight and price, and determines the number of selected components, so that the system has the greatest reliability under the given cost and weight constraints. The coding method of the solution is that the number of elements of each type of subsystem is a variable, and the whole system is arranged in the order of subsystems to form row vectors. An iterative particle swarm optimization algorithm with fixed compression coefficient and dynamic inertia weight is constructed to solve the problem. Typical improved fyffe problems are tested, and the optimal solutions are obtained, which are consistent with the results given by the substitution constraint method. The pso algorithm presented in this paper can effectively solve the grap problem which is allowed to mix components in subsystems.

关键词： Mixed components The reliability redundancy allocation problem particle swarm optimization algorithm Encoding Optimal solution

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particle swarm and bat-inspired optimization algorithm-based parameter identification of Krasnosel’skii-Pokrovskii model for piezoelectric hysteresis

Particle swarm and bat-inspired optimization algorithm-based...

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第五届国际仿生工程学术大会(ICBE2016)

作者： Rui Xu Miaolei Zhou Yantao Tian Department of control science and engineering Jilin University

The piezoelectric ceramics is a kind of novel intelligent materials and possesses advantages of ultrafine resolution, high stiffness and fast frequency response. It is an excellent choice as an actuator to be applied widely to micro-nano positioning system. However, one of the drawbacks of the piezoelectric actuator is the obvious hysteresis nonlinearity to limit the accuracy of positioning. The present study focuses on describing the hysteresis nonlinearity between input voltage and output displacement of piezoelectric ceramics. In order to investigate the hysteresis nonlinearity of piezoelectric ceramics, a series of models are presented, such as BoucWen model, Preisach model, Krasnosel’skii-Pokrovskii model and so on. However, the main difficulty is how to select an effective method to identify the unknown parameters of these hysteresis nonlinearity models. In this paper, we propose a novel hybrid optimization algorithm of particle swarm and bat-inspired to identify the density function of Krasnosel’skii-Pokrovskii model. The proposed hybrid optimization algorithm has the fast convergence of bat-inspired and the global search ability of particle swarm. During operation of the optimization algorithm, we divided the swarm into two different parts randomly as the initial value. One set of data is optimized by particle swarm algorithm and find the optimal value i.e. best1, the other set of data is optimized by bat-inspired algorithm and the optimal value is best2. Then we select the bigger one between best1 and best2 i.e. Gbest as the global optimal value of the two algorithms for the next iteration *** taking advantage of the optimization algorithm, an objective function is defined firstly. Then the density function of Krasnosel’skii-Pokrovskii model is identified by using bat-inspired algorithm and hybrid optimization algorithm of particle swarm and bat-inspired respectively. The simulations show that the displacement error of identification of Kras

关键词： particle swarm optimization algorithm bat-inspired algorithm Krasnosel’skii-Pokrovskii model

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