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Fast and accurate synthesis of electronically reconfigurable annular ring monopole antennas using particle swarm optimisation and artificial bee colony algorithms

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IET MICROWAVES ANTENNAS & PROPAGATION 2016年第4期10卷 362-369页

作者： Brito Rodrigues, Eduardo Jorge Castro Lins, Hertz Wilton D'Assuncao, Adaildo Gomes Univ Fed Rio Grande do Norte UFRN Dept Commun Engn Caixa Postal 1655 BR-59072970 Natal RN Brazil Anatel Natl Agcy Telecommun BR-58043010 Joao Pessoa Paraiba Brazil

The synthesis of a simple electronically reconfigurable annular ring monopole antenna using a designing optimisation process based on particle swarm optimisation (pso) and artificial bee colony (ABC) algorithms is proposed. Several antenna dimensions are selected as objective functions of pso and ABC and their best solutions are considered as the optimised dimensions of the antenna geometry. Two radio-frequency p-i-n diodes, connecting the antenna feeding line to two microstrip stubs are used to change the antenna frequency response from ultra-wideband (UWB), from 3.1 to 10.6 GHz, to narrowband (NB) operation about 5.8 GHz. The antenna design and simulation are performed using Ansoft high-frequency structure simulator software. pso and ABC algorithms are written in Java language. Thereafter, antenna prototypes are fabricated and measured for validation purposes. Simulation and measurement results are obtained showing good agreement. The measured optimised impedance bandwidths of the UWB and NB bands are up to 128 and 23%, respectively. Additionally, simulated and measured radiation patterns are very similar when the reconfigurable antenna is operating in OFF-state (UWB sensing antenna) and in ON-state (NB transmitting antenna) modes, indicating the proposed antenna geometry as a promising candidate for applications such as UWB and cognitive radio.

关键词： monopole antennas particle swarm optimisation ultra wideband antennas frequency response Java antenna radiation patterns electrical engineering computing electronically reconfigurable annular ring monopole antenna synthesis particle swarm optimisation artificial bee colony bioinspired algorithms antenna dimensions objective functions antenna geometry radiofrequency p-i-n diodes antenna feeding line microstrip stubs frequency response ultrawideband antenna antenna design pso algorithms ABC algorithms Java language ANSOFT high-frequency structure simulator software antenna prototypes validation purposes impedance bandwidths antenna radiation patterns OFF-state modes ON-state modes cognitive radio NB transmitting antenna UWb sensing antenna

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Improving the Intersection's Throughput using V2X Communication and Cooperative Adaptive Cruise Control

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IFAC-PapersOnLine 2016年第5期49卷 359-364页

作者： Liu, Bing Sun, Qi El Kamel, Abdelkader Ècole Centrale de Lille Villeneuve d'Ascq 59651 France

In this paper, a decentralized cooperative adaptive cruise control algorithm for vehicles in the vicinity of intersections (CACC-VI) is proposed. This algorithm is designed to make use of the road capacity to let more vehicles get through the intersection within a limited traffic signal period in consideration of safety, fuel consumption, speed limit and heterogeneous features of vehicles, and passenger comfort. Within a platoon, vehicles try to find the optimal control input by a distributed pso algorithm in order to reduce tracking errors while respecting different constraints. A concept of opportunity space is proposed to facilitate platoon reorganization in which a sub-platoon or an individual vehicle can choose to accelerate to join in the preceding platoon or to decelerate to depart from the current one. It is found that the throughput of the intersection during the green phase can be improved, and an optimal control input can be found for each vehicle within the defined constraints. © 2016

关键词： Adaptive cruise control Cooperative communication Traffic signals Vehicles Cooperative adaptive cruise control Eco driving Heterogeneous features Optimal controls Passenger comfort pso algorithms pso with constraints Tracking errors

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Process parameter optimisation for plasma sprayed nanostructured ZrO₂-7%Y₂O₃ coatings based on pso algorithm

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INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY 2015年第3期51卷 220-227页

作者： Yang, Bin Zhao, Hongwei Zhang, Yingfu Tianjin Univ Technol & Educ Sch Automobile & Transportat Tianjin 300222 Peoples R China Great Wall Motor Co Ltd Baoding 071000 Hebei Peoples R China Jilin Univ Coll Machinery & Engn Changchun 130025 Peoples R China

The process parameters of plasma sprayed nanostructured ZrO2-7%Y2O3 coatings were optimised based on a particle swarm optimisation (pso) algorithm. A BP neural network was applied to compute the fit of the pso algorithm, and was assembled with four process parameters which included spraying distance, spraying electric current, primary gas pressure, and secondary gas pressure as the inputs;the bonding strength of the coating was the output. The results of the pso algorithm and BP neural network show that the maximal bonding strength of the coatings was 42.5822 MPa. And the optimal process parameters discovered in this research for the plasma sprayed nanostructured ZrO2-7%Y2O3 coatings are a spraying distance of 80 mm, spraying electric current of 994.3707 A, primary gas pressure of 0.2575 MPa, and secondary gas pressure 1.1611 MPa.

关键词： plasma spraying nanostructured coating neural networks pso algorithms process parameters optimisation

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Particle Swarm Optimisation Failure Prediction Based on Fitness Landscape Characteristics

Particle Swarm Optimisation Failure Prediction Based on Fitn...

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IEEE Symposium on Swarm Intelligence (SIS)

作者： Malan, Katherine M. Engelbrecht, Andries P. Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa

ISBN: (纸本)9781479944583

Particle swarm optimisation (pso) algorithms have been successfully used to solve many complex real-world optimisation problems. Since their introduction in 1995, the focus of research in psos has largely been on the algorithmic side with many new variations proposed on the original pso algorithm. Relatively little attention has been paid to the study of problems with respect to pso performance. The aim of this study is to investigate whether a link can be found between problem characteristics and algorithm performance for psos. A range of benchmark problems are numerically characterised using fitness landscape analysis techniques. Decision tree induction is used to develop failure prediction models for seven different variations on the pso algorithm. Results show that for most pso models, failure could be predicted to a fairly high level of accuracy. The resulting prediction models are not only useful as predictors of failure, but also provide insight into the algorithms themselves, especially when expressed as fuzzy rules in terms of fitness landscape features.

关键词： decision trees failure analysis fuzzy set theory particle swarm optimisation pso algorithms decision tree induction failure prediction models fitness landscape analysis techniques fitness landscape characteristics fuzzy rules particle swarm optimisation failure prediction Accuracy Linear programming Mathematical model Prediction algorithms Predictive models Testing Training

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Comparison of Self-Adaptive Particle Swarm Optimizers

Comparison of Self-Adaptive Particle Swarm Optimizers

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IEEE Symposium on Swarm Intelligence (SIS)

作者： van Zyl, E. T. Engelbrecht, A. P. Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa

ISBN: (纸本)9781479944583

Particle swarm optimization (pso) algorithms have a number of parameters to which their behaviour is sensitive. In order to avoid problem-specific parameter tuning, a number of self-adaptive pso algorithms have been proposed over the past few years. This paper compares the behaviour and performance of a selection of self-adaptive pso algorithms to that of time-variant algorithms on a suite of 22 boundary constrained benchmark functions of varying complexities. It was found that only two of the nine selected self-adaptive pso algorithms performed comparably to similar time-variant pso algorithms. Possible reasons for the poor behaviour of the other algorithms as well as an analysis of the more successful algorithms is performed in this paper.

关键词： particle swarm optimisation pso algorithms boundary constrained benchmark functions problem-specific parameter tuning self-adaptive particle swarm optimizers time-variant pso algorithms Acceleration Algorithm design and analysis Benchmark testing Equations Mathematical model Particle swarm optimization Tuning

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Study on Rotor Speed Identification of DTC System Based on pso algorithms of BP Neural Network

Study on Rotor Speed Identification of DTC System Based on P...

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IITA International Conference on Services Science, Management and Engineering

作者： Cao, Chengzhi Lu, Yuanyuan Wang, Fang Zheng, Haiyin Shenyang Univ Technologyorganizat Dept Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9780769537290

To solve the strong randomicity and slow convergence of the Particle Swarm Optimization(pso) algorithms, two new particle's position renewal formulas were analyzed on the basis of extrapolation in mathematics. A new modified pso algorithms (called Leading pso algorithms) was put forward. The Direct Torque Control(DTC) System was built in the environment of Matlab(Simulink). The weight and threshold values of BP Neural Network were trained using the modified pso algorithms. Some disadvantages such as slow convergence speed and easily plunging into the local solution were avoided effectively. The simulation result shows that the system works well, and the rotor speed identifier has great static and dynamic performance.

关键词： pso algorithms DTC system rotor speed identification BP neural network

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Model of coal product structure based on particle swarm optimization algorithm

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Procedia Earth and Planetary Science 2009年第1期1卷 640-647页

作者： Zhang-guo, Wang Ya-li, Kuang Zhe, Lin Chang-sheng, Shi School of Chemical Engineering and Technology China University of Mining and Technology Xuzhou 221116 China Environmental Engineering Department North China Institute of Science and Technology Beijing 101601 China

Planning rational product structure of coal preparation is the key to attain the maximization of economic benefit in coal preparation enterprise and to save energy resources. There are many factors effect the preparation product structure, such as raw coal quality, separating methods, coal price, processing cost, product quality demands and equipment performance, etc. The research focuses on the optimization of product structure under the Multi-factor influences. In order to maximizing the economic benefit, the algorithm model of product structure is established, and the multiple influence factors are transformed as model parameters and constraint conditions. Then the particle swarm optimization (pso) algorithm is used to search the optimal scheme of product structure. According to the actual requirement, the model was divided into several child models during the calculation. A set of practical software has been developed based on the research. The result shows that using pso algorithm can get better convergence effect and avoid the local optimization for the Multi-factor model and that the optimal scheme of product structure from the model accord with the practical situation. © 2009.

关键词： Particle swarm optimization (pso) algorithms Coal deposits Coal industry Coal preparation Coal research Convergence of numerical methods Energy resources Industrial research Shape optimization Structural optimization Algorithm model Child models Coal products Constraint conditions Economic benefits Equipment performance Gravity separation Influence factors Local optimizations Model optimization Model parameters Multi factor Optimal scheme Particle swarm optimization algorithm Processing costs Product quality Product structure pso algorithms Raw coal Save energy

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