检索结果-内蒙古大学图书馆

A new optimization algorithm for the design of aperiodic multilayer mirror for 17.1 nm and 30.4 nm

OPTOELECTRONICS AND ADVANCED MATERIALS-RAPID COMMUNICATIONS 2013年第3-4期7卷 167-172页

作者： Zhu, Huaxin Gao, Jinsong Wang, Tongtong Zhao, Jingli Liu, Guilin Li, Shuai Jiangnan Univ Sch Sci Wuxi 214122 Jiangsu Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Key Lab Opt Syst Adv Mfg Technol Changchun 130033 Peoples R China

A new algorithm, Genetic/Tabu hybrid algorithm (GTHA) to optimize the aperiodic multilayer mirrors which combines the advantages of genetic algorithm (GAs) with tabu search (TS) algorithm is proposed in the extreme ultraviolet (EUV) range. Aperiodic multilayers are designed using GTHA for the selection of Fe-IX and He-II emission lines contrasting to the traditional periodic multilayers for a single wavelength. Materials of Mo and Si are selected for their high stability and fairly high reflectivity. High reflectance of 48.62% for the Fe-IX line (lambda=17.1nm) and reflectance of 20.57% for the He-II line (lambda=30.4nm) are reached by the new algorithm. Comparisons between aperiodic multilayers found by GTHA and the ones optimized using GAs indicate the effectiveness and reliability of the new hybrid algorithm. The aperiodic designs are compared with the periodic ones as well. And the aperiodic multilayers found by GTHA also have the better performance than periodic ones. The practicability of the aperiodic design optimized by GHTA is verified by the sensitivity analyses to thicknesses errors, which indicated it more feasible to fabricate the aperiodic multilayers in practice.

关键词： Optical thin Elm optimization algorithm Aperiodic multilayers

来源：评论

学校读者我要写书评

暂无评论

Dove Swarm optimization algorithm

引用

IEEE ACCESS 2022年 10卷 46690-46696页

作者： Su, Mu-Chun Chen, Jieh-Haur Utami, Andina Mugi Lin, Shih-Chieh Wei, Hsi-Hsien Natl Cent Univ Dept Comp Sci & Informat Engn Taoyuan Taiwan Natl Cent Univ Dept Civil Engn Taoyuan Taiwan Natl Cent Univ Res Ctr Smart Construct Taoyuan 320317 Taiwan Hong Kong Polytech Univ Dept Bldg & Real Estate Hong Kong Peoples R China

Popular methods to deal with computation become strenuous due to the optimization demands that develop more complex nowadays. This research aims to propose a new optimal algorithm, Dove Swarm optimization (DSO), that adopts the foraging behaviors of doves to have six benchmark functions expressing DSO performance. By considering competition for forage, DSO is designed to ensure the most satisfied dove as well as optimization, then compared with 15 popular optimization algorithms using random initial and lattice initial values. The results reveal that DSO performs the best in time efficiency and well in both convergences for these functions in a reasonable region from 1 to 3 seconds, and population diversity for the initialization method from less than 1 second to 9 seconds dependent on the population size. As a result, DSO is indeed a time-efficient and effective algorithm in solving optimization problems.

关键词： optimization Particle swarm optimization Genetic algorithms Birds Neurons Statistics Sociology Swarm intelligence optimization algorithm computational intelligence

来源：评论

学校读者我要写书评

暂无评论

A New Class Topper optimization algorithm with an Application to Data Clustering

引用

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2020年第4期8卷 948-959页

作者： Das, Pranesh Das, Dushmanta Kumar Dey, Shouvik Natl Inst Technol Nagaland Comp Sci & Engn Dimapur 797103 India Natl Inst Technol Nagaland Dimapur 797103 India Jadavpur Univ Comp Sci Kolkata 700103 India

In this paper, a new Class Topper optimization (CTO) algorithm is proposed. The optimization algorithm is inspired from the learning intelligence of students in a class. The algorithm is population based search algorithm. In this approach, solution is converging towards the best solution. This may lead to a global best solution. To verify the performance of the algorithm, a clustering problem is considered. Five standard data sets are considered for real time validation. The analysis shows that the proposed algorithm performs very well compared to various well known existing heuristic or meta-heuristic optimization algorithms.

关键词： optimization Clustering algorithms Heuristic algorithms Data analysis Whales Dolphins Sociology Data clustering optimization algorithm learning intelligence data analysis and nature inspired optimization

来源：评论

学校读者我要写书评

暂无评论

An improved grasshopper optimization algorithm based on dynamic dual elite learning and sinusoidal mutation

引用

COMPUTING 2022年第5期104卷 981-1015页

作者： Chen, Lei Tian, Yu Ma, Yunpeng Tianjin Univ Commerce Sch Informat Engn Tianjin Peoples R China Tianjin Univ Commerce Sch Sci Tianjin Peoples R China

Grasshopper optimization algorithm (GOA) is a meta-heuristic algorithm for solving optimization problems by modeling the biological habit and social behavior of grasshopper swarms in nature. Compared with other optimization algorithms, GOA still has room to improve its performance on solving complex problems. Therefore, this paper proposes an improved grasshopper optimization algorithm (EMGOA) based on dynamic dual elite learning and sinusoidal mutation. First of all, dynamic elite learning strategy is adopted to improve the influence of elites on the update process, enabling the algorithm to have a faster convergence speed. Then, sinusoidal function is utilized to guide the mutation of the current global optimal individual during each iteration to avoid the algorithm falling into the local optimum and improve the convergence accuracy of the algorithm. In order to investigate the performance of the proposed EMGOA algorithm, experiments are conducted on 26 benchmark functions and CEC2019 in this paper. The experimental results show that the optimization performance of EMGOA is obviously better than GOA, and EMGOA is competitive with six state-of-the-art meta-heuristic optimization algorithms.

关键词： Grasshopper optimization algorithm Dynamic elite learning Sinusoidal mutation optimization algorithm Meta-heuristic algorithm

来源：评论

学校读者我要写书评

暂无评论

optimization of Self-Learning in Computer Engineering Courses: An Intelligent Software System Supported by Artificial Neural Network and Vortex optimization algorithm

引用

COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2017年第1期25卷 142-156页

作者： Kose, Utku Arslan, Ahmet Usak Univ Comp Sci Applicat & Res Ctr Usak Turkey Konya Food & Agr Univ Dept Comp Engn Konya Turkey

Self-learning process is an important factor that enables learners to improve their own educational experiences when they are away of face-to-face interactions with the teacher. A well-designed self-learning activity process supports both learners and teachers to achieve educational objectives rapidly. Because of this, there has always been a remarkable trend on developing alternative self-learning approaches. In this context, this study is based on two essential objectives. Firstly, it aims to introduce an intelligent software system, which optimizes and improves computer engineering students' self-learning processes. Secondly, it aims to improve computer engineering students' self-learning during the courses. As general, the software system introduced here evaluates students' intelligence levels according to the Theory of Multiple Intelligences and supports their learning via accurately chosen materials provided over the software interface. The evaluation mechanism of the system is based on a hybrid Artificial Intelligence approach formed by an Artificial Neural Network, and an optimization algorithm called as Vortex optimization algorithm (VOA). The system is usable for especially technical courses taught at computer engineering departments of universities and makes it easier to teach abstract subjects. For having idea about success of the system, it has been tested with students and positive results on optimizing and improving self-learning have been obtained. Additionally, also a technical evaluation has been done previously, in order to see if the VOA is a good choice to be used in the system. It can be said that the whole obtained results encourage the authors to continue to future works. (C) 2017 Wiley Periodicals, Inc.

关键词： self-learning theory of multiple intelligences artificial neural networks vortex optimization algorithm computer engineering

来源：评论

学校读者我要写书评

暂无评论

Research and application of an innovative combined model based on a modified optimization algorithm for wind speed forecasting

引用

MEASUREMENT 2018年 124卷 395-412页

作者： Jiang, Ping Li, Chen Dongbei Univ Finance & Econ Sch Stat Jianshan Rd Dalian 116025 Liaoning Peoples R China

Wind energy is emerging as a promising substitute for conventional energy and plays a pivotal role in the power industry. For wind speed forecasting, many challenges have exposed due to its fluctuation and intermittence. To address these difficulties, different models have been adopted to various wind speed time series in previous studies. However, few methodologies have focused on the importance of model parameter optimization or data pre-processing, resulting in undesirable forecasting performance. In this study, an innovative combined model that combines data pre-processing, modified optimization algorithms, three neural networks and an effective deciding weight method is proposed for short-term wind speed forecasting. To improve the forecasting capacity of the combined model, a modified optimization algorithm is proposed and employed to determine the parameters of the single models. Furthermore, a deciding weight method based on multivariate statistical estimation is applied for weight optimization. Additionally, ten-minute wind speed data from a wind farm in Penglai, China, are selected for multi-step ahead forecasting. The results obtained confirmed an adequate approximation of the actual wind speed series and a significant improvement of the forecasting accuracy of the proposed model.

关键词： Wind speed forecasting optimization algorithm Multi-step ahead forecasting Combined model Weight coefficient optimization

来源：评论

学校读者我要写书评

暂无评论

Jaya optimization algorithm for transient response and stability enhancement of a fractional-order PID based automatic voltage regulator system

引用

ALEXANDRIA ENGINEERING JOURNAL 2020年第4期59卷 2429-2440页

作者： Jumani, Touqeer Ahmed Mustafa, Mohd Wazir Hussain, Zohaib Rasid, Madihah Md Saeed, Muhammad Salman Memon, Mehran M. Khan, Ilyas Nisar, Kottakkaran Sooppy Univ Teknol Malaysia Sch Elect Engn Skudai Johor Bahru Malaysia Mehran Univ Engn & Technol Dept Elect Engn SZAB Campus Khairpur Mirs Pakistan Mehran Univ Engn & Technol Dept Elect Engn Jamshoro Pakistan Multan Elect Power Co Multan Pakistan Ton Duc Thang Univ Fac Math & Stat Ho Chi Minh City 72915 Vietnam Prince Sattam bin Abdulaziz Univ Coll Arts & Sci Dept Math Wadi Aldawaser 11991 Saudi Arabia

Considering the higher flexibility in tuning process and finer control action of the fractional-order proportional integral derivative (FOPID) controller over the conventional propor-tional integral derivative (PID) controller, this paper explores its application in the automatic volt-age regulator system. FOPID contains five tuning parameters as compared to three in the conventional PID controller. The additional tuning knobs in FOPID provide increased control flex-ibility and precise control action, however, their inclusion makes the tuning process more complex and tedious. Thus, the intelligence of an artificial intelligence (AI) technique called jaya optimiza-tion algorithm (JOA) is utilized in order to obtain an optimal combination of FOPID gains which further led to the optimal transient response and improved stability of the considered AVR system. To validate the performance superiority of the proposed approach its corresponding system's dynamic response is compared with that of the other well-known AI-based approaches explored in recent literature. Furthermore, the stability study of the proposed AVR system is carried out by evaluating its pole/zero and bode maps. Finally, the robustness of the proposed optimized AVR system against the system's parameter variation is evaluated by varying the time constants of all the four components of AVR (generator, exciter, amplifier and sensor) from-50% to +50% independently. The proposed algorithm based FOPID tuning technique provides 59.82%, 56.09%, 14.94%, 34.24%, 35.70%, 21.64%, 12.0%, 41.33%, 14.84% and 15.17% reduced over-shoot than that of differential evolution (DE), particle swarm optimization (PSO), Artificial Bee Colony (ABC), Bibliography Based optimization (BBO), Grasshopper optimization algorithm (GOA), Pattern Search algorithm (PSA), Improved Kidney Inspired algorithm (IKA), Whale optimization algorithm (WOA), Salp Swarm algorithm (SSA) and Local Unimodal Sampling (LUS) algorithm respectively, thus validat

关键词： optimization algorithm Transient response FOPID AVR JOA

来源：评论

学校读者我要写书评

暂无评论

Efficient and merged biogeography-based optimization algorithm for global optimization problems

引用

SOFT COMPUTING 2019年第12期23卷 4483-4502页

作者： Zhang, Xinming Kang, Qiang Tu, Qiang Cheng, Jinfeng Wang, Xia Henan Normal Univ Coll Comp & Informat Engn Xinxiang Peoples R China Engn Technol Res Ctr Comp Intelligence & Data Min Xinxiang Peoples R China

In order to improve the optimization efficiency of the biogeography-based optimization (BBO) algorithm, this study proposes a novel BBO algorithm, namely an efficient and merged biogeography-based optimization (EMBBO) algorithm. Firstly, BBO's mutation operator is got rid of. Then, a differential mutation operator and a sharing operator are merged into BBO's migration operator to obtain an improved migration operator. In the improved migration operator, the emigration habitats are selected by a new example learning approach. The above improvements can enhance the optimization performance and reduce the computation complexity. Thirdly, a new single-dimensional and all-dimensional alternating strategy is combined with the improved migration operator to balance exploration and exploitation and reduce more computation complexity. Fourthly, the opposition-based learning approach is merged to prevent the algorithm from falling into the local optima. Finally, the greedy selection method is used instead of the elitist strategy to avoid setting the elitist parameter and to get rid of one sorting step. We make a large number of experiments on a set of classic benchmark functions and CEC2017 test set and apply EMBBO to clustering optimization. Experiment results verify that EMBBO can obtain the highest optimization efficiency compared with quite a few state-of-the-art algorithms.

关键词： optimization algorithm Evolutionary algorithm Biogeography-based optimization Differential mutation operator Example learning approach

来源：评论

学校读者我要写书评

暂无评论

An optimization algorithm for solving the rich vehicle routing problem based on Variable Neighborhood Search and Tabu Search metaheuristics

引用

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2016年 291卷 468-477页

作者： Antonio Sicilia, Juan Quemada, Carlos Royo, Beatriz Escuin, David Univ Int La Rioja Escuela Ingn Logrono 26002 Spain Virginia Polytech Inst & State Univ Blacksburg VA USA Univ Zaragoza Dept Mech Engn Zaragoza 50018 Spain Inst Tecnol Aragon Zaragoza 50018 Spain

This paper presents a novel optimization algorithm that consists of metaheuristic processes to solve the problem of the capillary distribution of goods in major urban areas taking into consideration the features encountered in real life: time windows, capacity constraints, compatibility between orders and vehicles, maximum number of orders per vehicle, orders that depend on the pickup and delivery and not returning to the depot. With the intention of reducing the wide variety of constraints and complexities, known as the Rich Vehicle Routing Problem, this algorithm proposes feasible alternatives in order to achieve the main objective of this research work: the reduction of costs by minimizing distances and reducing the number of vehicles used as long as the service quality to customers is optimum and a load balance among vehicles is maintained. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Rich vehicle routing problem optimization algorithm Metaheuristic methods Load balance

来源：评论

学校读者我要写书评

暂无评论

Sensing Cloud Computing in Internet of Things: A Novel Data Scheduling optimization algorithm

引用

IEEE ACCESS 2020年 8卷 42141-42153页

作者： Sun, Zeyu Lv, Zhiguo Wang, Huihui Li, Zhixian Jia, Fuqian Lai, Chunxiao Luoyang Inst Sci & Technol Sch Comp Sci & Informat Engn Luoyang 471023 Peoples R China Henan Inst Sci & Technol Sch Informat Engn Xinxiang 453003 Henan Peoples R China Jacksonville Univ Dept Engn Jacksonville FL 32211 USA

In order to addressing the issues of data matching deviation and load imbalance during the data scheduling process of the Internet of Things, Sensing Cloud Computing in IoT: A Novel Data Scheduling optimization (SCC-DSO) algorithm is proposed in this paper. First, according to the processing capacity of the IoT working node, a data placement algorithm is designed to reasonably place the input data of the job. Second, the data scheduling queue is optimized based on the data block storage location information to reduce non-local execution of data scheduling. Furthermore, a data prefetching method is designed to pull the data required for non-local data scheduling in advance, and shorten the waiting time of the task for input data. Finally, simulation experiment evaluated by the task localization execution rate and response time is performed. The effectiveness and stability of the algorithm is verified compared with other algorithms.

关键词： Task analysis Predictive models Processor scheduling Internet of Things Data models Load modeling Energy consumption Sensing-cloud computing Internet of Things data scheduling optimization algorithm

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：