检索结果-内蒙古大学图书馆

7th International Conference on Information and Communication Technology (ICOICT)

作者： Saifullah, Akhmad Baizal, Z. K. A. Gunawan, P. H. Telkom Univ Sch Comp Jl Telekomun 1 Bandung 40257 Indonesia

ISBN: (纸本)9781538680520

Bandung is a region in Indonesia that has good potential tourism. Every year, the number of tourists coming to Bandung is increasing. However, most tourists do not have enough information to organize a tour for visiting several touristic places. Therefor a system for tour scheduling is needed to help tourist to enjoy their holiday. Here, we develop a tour scheduling system using the firefly Algorithm. This algorithm generally is used to solve Traveling Salesman Problem (TSP). The firefly algorithm generates tour scheduling based on three criteria of user needs, such as distance, popularity and budget. We calculate these three criteria with the Multi Attribute Utility Theory (MAUT). Each criterion has a priority value from user. We use the result of MAUT as a fitness of firefly Algorithm. The firefly algorithm also considers opening hours and closing hours for each tourist attractions. This system also recommends some hotels based on two criteria, i.e., distance and facilities. We calculate these two criteria using MAUT. For evaluation, we compare n-days tour scheduling modeled by one step optimization, with per-day tour optimization (n-steps). We employ some parameters, i.e., running time, fitness value and number of visited destinations. Based on the experiment, the running time of per-day tour optimization is 7% faster than one-step optimization. Meanwhile, the fitness value of per-day tour optimization is 14% higher than one-step optimization. In addition, the number of nodes in tour of per-day tour optimization is 30% more than one step optimization. Moreover, based on popularity, travel time and budget, the higher fitness of per-day tour optimization is obtained.

关键词： tourism firefly algorithms scheduling Traveling Salesman Problem (TSP) Multi Attribute Utility Theory (MAUT) recommend hotels

来源：评论

学校读者我要写书评

暂无评论

Critical Analysis of Optimization Techniques for a MRPID Thermal System Controller

引用

IETE JOURNAL OF RESEARCH 2023年第1期69卷 149-164页

作者： Kanungo, Abhas Mittal, Monika Dewan, Lillie Delhi NCR KIET Grp Inst Dept Elect & Instrumentat Ghaziabad 201206 Uttar Pradesh India NIT Dept Elect Engn Kurukshetra 136119 Haryana India

In this paper, critical analysis of performance comparison of various optimization techniques for a MRPID (Multi Resolution Proportional Integral Derivative) thermal system controller is presented. This controller controls the uncertainty or mismatch between real and reference temperature. Wavelet coefficients of error between the two temperatures and their corresponding gains are added to produce a control signal for the thermal system. The main purpose of the proposed method is to optimize the tuning parameters of a wavelet-based MRPID controller to regulate the switching pulse of the thermal system. Modeling and simulations are carried out using MATLAB/Simulink@2015. Figures-of-merit such as disturbance rejection, system stability and transient response are also compared using various techniques like Particle swarm optimization (PSO)-MRPID, Genetic Algorithm (GA)-MRPID (existing techniques), firefly-MRPID controllers and Elephant herding optimization (EHO) based Recurrent neural network (RNN)-MRPID algorithm. firefly-MRPID and EHO with RNN-based MRPID are proposed for determining unique (best) operating conditions of the thermal system in this paper.

关键词： EHO algorithm firefly algorithms GA MRPID controller PSO thermal system

来源：评论

学校读者我要写书评

暂无评论

Bio-Inspired Scheduling for Factory Automation in the TD-LTE System

引用

IETE TECHNICAL REVIEW 2022年第5期39卷 1189-1207页

作者： Ryu, Won Jae Satrya, Gandeva Bayu Shin, Soo Young Kumoh Natl Inst Technol ICT Convergence Res Ctr Gumi Si South Korea Telkom Univ Sch Appl Sci Bandung Indonesia Kumoh Natl Inst Technol Dept IT Convergence Engn Gumi Si South Korea

Wireless technology has advantages such as low cost, flexibility, relatively easy installation, and the ability to address routing issues. Consequently, wireless factory automation has received considerable attention recently. However, most wireless technologies in factory automation have adopted unlicensed bandwidth, which can be affected by interference from other users. Long-term evolution adopts a licensed bandwidth;therefore, it can be a solution for factory automation. This paper presents scheduling algorithms in wireless industrial networks for factory automation in wireless sensor networks using the TD-LTE system. To satisfy real-time requirements, scheduling algorithms based on bio-inspired optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), and firefly algorithm (FA), and dynamic frame configuration (DFC) are proposed to cover more nodes. Furthermore, the performances of these algorithms with and without DFC are evaluated.

关键词： Evolutionary computation Factory automation firefly algorithms Genetic algorithms Particle swarm optimization Scheduling algorithms TD-LTE Wireless communication

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid metaheuristic optimization method to estimate medium-term output power for horizontal axis wind turbine

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY 2019年第5期233卷 646-658页

作者： Ekinci, Firat Demirdelen, Tugce Aksu, Inayet Ozge Aygul, Kemal Esenboga, Burak Bilgili, Mehmet Adana Sci & Technol Univ Energy Syst Engn Adana Turkey Adana Sci & Technol Univ Elect & Elect Engn Adana Turkey Adana Sci & Technol Univ Comp Engn Adana Turkey Cukurova Univ Elect & Elect Engn Adana Turkey Cukurova Univ Mech Engn Adana Turkey

The increasing damage caused by fossil fuels has made it a necessity for new and clean energy sources. In recent years, the use of wind energy from renewable energy sources has increased, which is a new and clean energy source. Wind energy is everywhere in nature. The wind speed changes depending on time. Thus, the wind power is unstable. In order to keep this disadvantage at a minimum level, future power estimation studies have been carried out. In these studies, different methods and algorithms are applied to estimate short and medium term in wind power. In this study, artificial neural network, particle swarm optimization and firefly algorithm (FA) as a new method are used for the first time in predicting wind power. As input data, temperature, wind speed and rotor speed the data recorded in the SCADA in wind turbines are used to predict medium-term wind speed and also wind power. Each method is compared in detail and their performances are revealed.

关键词： Engineering optimization wind power prediction artificial neural network metaheuristic optimization firefly algorithms

来源：评论

学校读者我要写书评

暂无评论

Wheel wear reduction of a mechatronic two-axle vehicle controlled with feedforward wheelset steering approaches

引用

VEHICLE SYSTEM DYNAMICS 2024年第4期62卷 1037-1062页

作者： Giossi, Rocco Libero Persson, Rickard Stichel, Sebastian KTH Royal Inst Technol Dept Mech Engn Stockholm Sweden

The mechatronic vehicle developed within the Shift2Rail projects Run2Rail, Pivot, NEXTGEAR, and Pivot2 is evaluated with respect to wheel wear. The KTH wear model is used to determine the coefficients of Archard's wear map to reproduce measured worn wheel profiles of the present vehicle running on Metro Madrid line 10. The same wear model is then used to evaluate the performance of the mechatronic vehicle controlled with two variants of a feedforward controller. The first one uses on-board measurements, while the second one is optimised using firefly optimisation algorithms assuming knowledge of the travelled track. The control strategy based on on-board measurements shows improvements above 60% in terms of lost wheel volume due to wear, compared to the standard bogie vehicle. The optimised controller reaches improvements above 70%. Good coherence is found between improvements predicted with the wear number and the ones achieved in terms of lost wheel volume.

关键词： Two-axle mechatronic vehicle feedforward active steering control firefly algorithms KTH wear model experimental data fitting

来源：评论

学校读者我要写书评

暂无评论

Unit commitment - a survey and comparison of conventional and nature inspired algorithms

引用

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2014年第2期6卷 71-90页

作者： Mallipeddi, Rammohan Suganthan, Ponnuthurai Nagaratnam Kyungpook Natl Univ Sch Elect Engn Taegu 702701 South Korea Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Unit commitment problem (UCP) which has a significant influence on secure and economic operation of power systems is considered to be one of the most difficult optimisation problems due to the number/type of variables and constraints present. To provide quality solutions to UCP several solution methodologies that include deterministic and stochastic search algorithms have been proposed. Deterministic and stochastic algorithms have their own share of advantages and disadvantages. In this paper, we provide a literature survey on the algorithms developed for UCP and try to compare their performance on some standard benchmark problems by taking the results from the literature. The literature survey along with the performance comparison will be useful for the researchers in the area of power engineering.

关键词： unit commitment deterministic algorithms particle swarm optimisation PSO ant colony algorithms bacterial foraging differential evolution evolutionary algorithms genetic algorithms firefly algorithms harmony search simulated annealing

来源：评论

学校读者我要写书评

暂无评论

Performance Comparison of Metaheuristic algorithms for the Optimal Design of Space Trusses

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第10期43卷 5265-5281页

作者： Sonmez, Mustafa Aksaray Univ Dept Civil Engn Fac Engn TR-68100 Aksaray Turkey

In this study, eight population-based metaheuristic algorithms were employed for the design of truss structures with continuous design variables. The selected algorithms were genetic, ant colony, particle swarm, artificial bee colony, gravitational search, firefly, gray wolf optimization and Jaya. The purpose was to objectively evaluate the performance of these algorithms under the same conditions and select the best efficient algorithm by assessing three example truss structures. The results obtained from the examples showed that the algorithms were both computationally efficient and robust when the number of design variables was approximately 10 and a significant number of iterations were performed. When the number of design variables was increased to 53, artificial bee colony, Jaya and gray wolf optimization were found to be computationally more effective than the remaining algorithms.

关键词： Genetic algorithm Ant colony optimization Artificial bee colony Gray wolf optimization firefly algorithms Gravitational search algorithm Jaya

来源：评论

学校读者我要写书评

暂无评论

Review of ad-hoc on-demand distance vector protocol and its swarm intelligent variants for Mobile Ad-hoc NETwork

引用

IET NETWORKS 2017年第5期6卷 87-93页

作者： Persis, Devarajan Jinil Robert, T. Paul Hindustan Inst Technol & Sci Madras Tamil Nadu India Anna Univ Coll Engn Guindy Madras Tamil Nadu India

Mobile Ad-hoc NETwork (MANET) is a rapidly changing network having mobile nodes. The wireless links connecting a node pair are unreliable due to the mobility of nodes;thereby MANET is subjected to frequent link failures that subsequently lead to route failure. It is required for the routing algorithms to determine routes enduring connectivity providing uninterrupted data transmission without compromising quality of service and load balancing. Ad-hoc on-demand distance vector (AODV) routing protocol is predominantly used in MANET for making routing decisions because of its performance in terms of delay, throughput, packet delivery ratio and number of hops when compared to other protocols in the literature. However, this protocol often converges to less reliable routes leading to high control overhead and packet loss. In this study, a multi-objective AODV algorithm is proposed to consider distance, cost, delay, load and reliability as routing metrics. Also modified versions of AODV protocol by applying ant colony optimisation, bee colony optimisation and firefly algorithms have been proposed and the results are presented.

关键词： mobile ad hoc networks routing protocols ant colony optimisation ad-hoc on-demand distance vector protocol mobile ad-hoc network mobile nodes AODV routing protocol multiobjective AODV algorithm ant colony optimisation bee colony optimisation firefly algorithms

来源：评论

学校读者我要写书评

暂无评论

A Novel firefly Algorithm for Scheduling Bag-of-Tasks Applications Under Budget Constraints on Hybrid Clouds

引用

IEEE ACCESS 2019年 7卷 151888-151901页

作者： Zhang, Yi Zhou, Junlong Sun, Lulu Mao, Jingjing Sun, Jin Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

This paper aims at scheduling bag-of-tasks (BoT) applications under budget constraints on hybrid clouds for minimizing the makespan. To solve this NP-hard problem, we propose a novel firefly algorithm (NFA) in which the evaluation of a firefly consists of two steps: (1) mapping a firefly to a scheduling solution (a task sequence);(2) calculating the solutions objective (its corresponding makespan). In the first step, different from the well-known ranked-order value (ROV) rule, we propose a distance-based mapping operator that relies on the distance between a firefly and the brightest one to determine the mapping relationship between a firefly and a solution. We use a probability model in which solutions corresponding to fireflies closer to the brightest one would have higher probabilities to inherit tasks from the current best solution. In this manner, these solutions can inherit more good genes hidden inside the current best solution to evolve into more high-quality solutions. In the second step, we employ an effective heuristic to evaluate solution objectives. We further develop a composite heuristic to generate the initial best solution, providing the proposed NFA with a good start. We also establish a new movement scheme such that fireflies distant from the brightest one can explore a wide range in the search space, whereas fireflies nearby the brightest one can search in a small neighborhood. Experimental results show that, by employing the above-mentioned strategies, NFA outperforms the standard firefly algorithm and the existing best algorithm, in terms of scheduling effectiveness and computational efficiency. Specifically, the distance-based mapping operator is verified to be both more effective and more efficient than the ROV rule. The composite heuristic is capable of generating a good initial solution, leading to the high quality of the final schedule. The movement scheme can further reduce the makespan of BoT applications.

关键词： Cloud computing Task analysis Scheduling Processor scheduling Optimization Schedules Sun Bag-of-tasks applications hybrid clouds makespan firefly algorithms

来源：评论

学校读者我要写书评

暂无评论

Parameter Tuning in Modeling and Simulations by Using Swarm Intelligence Optimization algorithms 9

Parameter Tuning in Modeling and Simulations by Using Swarm ...

引用

9th International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Tan, Rabia Korkmaz Bora, Sebnem Ege Univ Dept Comp Engn Izmir Turkey

ISBN: (纸本)9781509050017

Modeling and simulation of real-world environments has in recent times being widely used. The modeling of environments whose examination in particular is difficult and the examination via the model becomes easier. The parameters of the modeled systems and the values they can obtain are quite large, and manual tuning is tedious and requires a lot of effort while it often it is almost impossible to get the desired results.. For this reason, there is a need for the parameter space to be set. The studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in modeling and simulations. In this study, work has been done for a solution to be found to the problem of parameter tuning with swarm intelligence optimization algorithms Particle swarm optimization and firefly algorithms. The performance of these algorithms in the parameter tuning process has been tested on 2 different agent based model studies. The performance of the algorithms has been observed by manually entering the parameters found for the model. According to the obtained results, it has been seen that the firefly algorithm where the Particle swarm optimization algorithm works faster has better parameter values. With this study, the parameter tuning problem of the models in the different fields were solved.

关键词： Swarm Intelligence Optimization algorithms Particle swarm optimization firefly algorithms Modeling and Simulation Parameter tuning

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：