检索结果-内蒙古大学图书馆

BWDO algorithm and its application in antenna array and pixelated metasurface synthesis

IET MICROWAVES ANTENNAS & PROPAGATION 2019年第9期13卷 1263-1270页

作者： Ranjan, Prakash Mahto, Santosh Kumar Choubey, Arvind Natl Inst Technol Jamshedpur Dept Elect & Commun Engn Jamshedpur Bihar India NIT Jamshedpur Dept Elect & Commun Engn Indian Inst Informat Technol Ranchi Jamshedpur Bihar India

This article introduces a novel, binary version of wind driven optimisation (WDO) called binary WDO (BWDO), which can be specifically used for solving binary valued problems such as antenna array and metasurface synthesis. Validation of proposed algorithm is done with standard benchmark functions. To demonstrate the efficiency of BWDO, examples of thinned antenna array and metasurface synthesis are considered. Thinned antenna array problem is solved by formulating single and multi-objective optimisation problems which achieve minimum SLL of -22.30 dB and -20.28 dB respectively, as compared to -13.2 dB by periodic array. Further, the proposed BWDO algorithm is interface with Ansys-HFSS and three design examples of metasurfaces such as MPA and AMC are further considered to exemplify the proficiency of BWDO. Simulation of optimised MPA exhibits five discrete bands of high absorptions at 8.7, 9.7, 14.6, 15.3 and 16.5 GHz with simulation reflection co-efficient value of -11.46, -10.00, -12.17, -16.19 and -20.58 dB which is less than -10 dB reflection under normal incidence of wave. Synthesised single and dual band AMC has in-phase reflection bandwidth of 0.58 and 0.34, 0.16 GHz at central frequency of 5.21 and 5.07, 6.49 GHz respectively. The measured results almost matched with the respective simulated results with slight variation due to fabrication tolerance.

关键词： optimisation antenna arrays frequency selective surfaces conductors (electric) antenna radiation patterns electromagnetic wave reflection electromagnetic wave absorption metamaterial antennas synthesised metasurfaces BWDO algorithm metasurface synthesis binary version wind driven optimisation binary-valued problems standard benchmark functions multiobjective optimisation problems minimum side-lobe level periodic array frequency selective surface human experience standard WDO algorithm electromagnetic problems binary string artificial magnetic conductor reflection coefficient value trial-and-error technology single objective optimisation problems antenna array problem frequency 0 16 GHz frequency 8 7 GHz frequency 6 49 GHz frequency 9 7 GHz frequency 14 6 GHz frequency 15 3 GHz frequency 16 5 GHz frequency 0 58 GHz frequency 0 34 GHz frequency 5 21 GHz frequency 5 07 GHz

来源：评论

学校读者我要写书评

暂无评论

Multi-objective bat algorithm with time-varying inertia weights for optimal design of passive power filters set

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2015年第7期9卷 644-654页

作者： Yang, Nien-Che Minh-Duy Le Yuan Ze Univ Dept Elect Engn Taoyuan 32003 Taiwan Yuan Ze Univ Innovat Ctr Big Data & Digital Convergence Taoyuan 32003 Taiwan

In this study, a novel method is developed to optimise the design of passive power filters (PPFs). The proposed method is based on the famous bat algorithm (BA) and is expected to make the PPF design easier and more effective than conventional trial-and-error approaches. Furthermore, the effects of inertia weight variations on the performance of the proposed method are examined. To optimise performance, the most suitable inertia weight, with the best effects, will be selected. For solving multi-objective optimisation problems, a so-called external archive is also integrated into the proposed method. A case study of PPF design is also presented to demonstrate the accuracy, efficiency and superiority of the proposed method.

关键词： power filters optimisation passive filters multiobjective bat algorithm time-varying inertia weights optimal design passive power filters trial-and-error approach multiobjective optimisation problems external archive

来源：评论

学校读者我要写书评

暂无评论

Improved normalised normal constraint method to solve multi-objective optimal power flow problem

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第4期12卷 859-872页

作者： Rahmani, Shima Amjady, Nima Semnan Univ Dept Elect Engn Semnan Iran

This study proposes an improved normalised normal constraint (NNC) method for solving optimal power flow (OPF), which is formulated as a multi-objective problem. While NNC is an efficient solution method to solve multi-objective problems, it may not cover the entire Pareto frontier in the objective space when three or more objectives are present in the problem, i.e. when the Pareto frontier becomes a hyper-surface. This research work aims to solve this limitation of NNC and propose a more effective solution method, named improved NNC (INNC), for multi-objective optimisation problems with three or more objective functions. The proposed INNC is applied for solving multi-objective OPF problem with three objectives of generation cost, transmission loss and voltage regulation. The effectiveness of the proposed INNC method for solving the multi-objective OPF problem is extensively illustrated on the IEEE 30-bus and IEEE 118-bus test systems in comparison with several recently published multi-objective solution approaches.

关键词： load flow Pareto optimisation voltage control multiobjective optimal power flow problem improved normalised normal constraint method NNC method hyper-surface Pareto frontier multiobjective optimisation problems generation cost transmission loss voltage regulation IEEE 30-bus test systems IEEE 118-bus test systems

来源：评论

学校读者我要写书评

暂无评论

Pareto-optimal design of UHF antenna using modified non-dominated sorting genetic algorithm II

引用

IET MICROWAVES ANTENNAS & PROPAGATION 2020年第12期14卷 1404-1410页

作者： Bin, Feng Wang, Feng Chen, She Sun, Qiuqin Zhong, Lipeng Lin, Shu Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Yili Normal Univ Coll Elect & Informat Engn Yili 835000 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Heilongjiang Peoples R China

In this study, the ultra-high-frequency (UHF) antenna for partial discharge (PD) detection is optimised to simultaneously satisfy the requirements of low return loss and high fidelity factor (FF) in the frequency band of interest by using the modified non-dominated sorting genetic algorithm II (MNSGA-II). Based on the labour division strategy, the MNSGA-II adopts an adaptive crossover and mutation possibilities instead of the fixed ones, resulting in the significant improvement of convergence rate and exploration ability. One of the Pareto-optimal solutions is presented as the authors' proposed UHF antenna, whose performance is compared with those of both the antenna optimised by genetic algorithm and the existing wideband antennas. The experimental results show that the proposed antenna with a compact size of 0.201 lambda(L) x 0.198 lambda(L)realises the reflection coefficient less than -10 dB from 490 MHz to 1.52 GHz, and its FFs in the face-to-face and side-by-side scenarios are 0.897 and 0.845, respectively. Furthermore, the simulation results reveal that the high FF of antenna greatly increases the accuracy of PD source localisation. It indicates that the MNSGA-II provides an excellent solution to the multiobjective optimisation problems in antenna design.

关键词： Pareto optimisation genetic algorithms broadband antennas UHF antennas Pareto-optimal design UHF antenna modified nondominated sorting genetic algorithm II partial discharge detection high fidelity factor MNSGA-II labour division strategy adaptive crossover mutation possibilities Pareto-optimal solutions wideband antennas ultra-high-frequency antenna multiobjective optimisation problems frequency 490 0 MHz to 1 52 GHz

来源：评论

学校读者我要写书评

暂无评论

Improved solutions to a TEAM problem for multi-objective optimisation in magnetics

引用

IET SCIENCE MEASUREMENT & TECHNOLOGY 2020年第8期14卷 964-968页

作者： Di Barba, Paolo Mognaschi, Maria Evelina Lowther, David A. Sykulski, Jan K. Univ Pavia Dept Elect Comp & Biomed Engn Pavia Italy McGill Univ Dept Comp & Elect Engn Montreal PQ Canada Univ Southampton Elect & Comp Sci Southampton Hants England

New solutions to a recently proposed benchmark TEAM problem for Pareto optimisation are presented. In the benchmark, an air-cored solenoid of small size, which can be used, for example, for magnetic fluid hyperthermia, is considered. Two shape optimisations of the solenoid are proposed in the benchmark: synthesising a uniform magnetic field in a control region, considering also a sensitivity function (Problem 1) or synthesising a uniform magnetic field, simultaneously minimising the power losses (Problem 2). The benchmark is solved by means of three different nature-inspired algorithms and a genetic one, namely micro biogeography-inspired algorithm, wind-driven optimisation, and the cuckoo search, taking the genetic algorithm NSGA-II as a reference, because all these methods have proven to be effective in solving multi-objective optimisation problems.

关键词： genetic algorithms hyperthermia search problems Pareto optimisation magnetic fluids evolutionary computation solenoids optimisation minimisation biomagnetism TEAM problem magnetics Pareto optimisation air-cored solenoid magnetic fluid hyperthermia shape optimisations uniform magnetic field control region sensitivity function different nature-inspired algorithms microbiogeography-inspired wind-driven optimisation genetic algorithm NSGA-II multiobjective optimisation problems

来源：评论

学校读者我要写书评

暂无评论

Solving path planning problem based on logistic beetle algorithm search-pigeon-inspired optimisation algorithm

引用

ELECTRONICS LETTERS 2020年第21期56卷 1105-1107页

作者： Liu, Ang Jiang, Jin Xiangtan Univ Sch Automat & Elect Informat Xiangtan Peoples R China

Mobile robot path planning is an important part of the mobile robot field. The swarm intelligence optimisation algorithm has certain advantages in solving such multi-objective optimisation problems, so this Letter proposes to apply the pigeon-inspired optimisation (PIO) algorithm to the path planning problem. However, the traditional PIO algorithm is suffering from the problems of easy to get into local optimisation, low stability and premature convergence. To overcome its shortcomings, this Letter proposes a logistic beetle algorithm search-PIO (LBAS-PIO) algorithm. In the optimisation process of the LBAS-PIO algorithm, the PIO algorithm is initialised by the logistic mapping, making the search space more extensive. Learning from the idea of the beetle algorithm search, each pigeon has its own judgment of the environment and the number of iterations and search time are reduced. Further, the path evaluation function of the algorithm and the execution order of the operators are optimised to make the algorithm smoother and easier to find the global optimal solution. Simulation and experimental results illustrated the superiority of the LBAS-PIO algorithm and the LBAS-PIO algorithm better meets the needs of mobile robots for the optimal path planning.

关键词： mobile robots swarm intelligence search problems path planning particle swarm optimisation multi-robot systems logistic beetle algorithm search-pigeon-inspired optimisation algorithm mobile robot path planning swarm intelligence optimisation algorithm multiobjective optimisation problems local optimisation search-PIO algorithm optimisation process LBAS-PIO algorithm optimal path planning

来源：评论

学校读者我要写书评

暂无评论

A review and analysis of optimisation techniques applied to floating offshore wind platforms

引用

OCEAN ENGINEERING 2023年第Part1期285卷

作者： Sykes, Victoria Collu, Maurizio Coraddu, Andrea Univ Strathclyde Glasgow Scotland Delft Univ Technol Delft Netherlands

The deployment of offshore wind in the UK has seen a rapid increase in the past decade and will continue to increase with the securement of the recent Scotwind sites. Floating platforms will be utilised for 60% of these new sites, creating opportunities to try new platform typologies and further solidify the validity of existing concepts. Since there is no consensus on the platform typology, the cost will vary;however, it is predicted to be double the price of traditional fixed platforms. Finding the most optimal solution in terms of cost and performance is key to keeping cost low, allowing the technology to be more competitive. A technique which has been used in other industries is multi-objective optimisation, searching a large design space much more quickly than traditional methods. By carrying out a multi-objective approach, the optimal platform geometry can be identified over the Pareto Frontier, considering conflicting objectives such as cost and performance. The aim of this work is to review the existing literature on multi-objective optimisation of floating offshore wind (FOW) platforms, highlighting the gaps and shortfalls in the current literature. This review highlights the majority of work has been carried out for the 5 MW NREL turbine on a SPAR platform, utilising a genetic algorithm. Cost reduction has been noted as the main objective, however, the models found within the literature are simplistic, with a number of assumptions. The overall findings of this work highlight future work that could be improved: cost models, the inclusion of an energy production model linked to the platform motion, the requirement for analysis of larger turbines and the potential for a concept selection tool to reduce computational time.

关键词： Floating offshore wind Wind turbine Levelised Cost of Energy multiobjective optimisation problems Review Floating sub-structure

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：