检索结果-内蒙古大学图书馆

2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Thi-Kien Dao Tien-Szu Pan Jeng-Shyang Pan Department of Electronics Engineering Kaohsiung University of Applied Sciences Harbin Institute of Technology Shenzhen Graduate School Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology

Due to the complex physical constraints in working space of robot, optimization for mobile robot operations satisfies not only one criterion but also several criteria. In this paper, a novel multi-objective method for optimal mobile robot path planning based on whale optimization algorithm(WOA) is proposed. In the proposed method, two criteria of distance and smooth path of the robot path planning issue are transformed into a minimization one. The positions of the target and the obstacles in the environment are considered the fitness for the solution in WOA. The position of the globally best whale in each iteration is selected, and reached by the robot in sequence. In addition, the robot processor updates its information during the motion, and the environment is partially unknown for the robot due to the limit of the detection range of its sensors. Series of simulations are implemented in different static environments for the optimal path when the robot reaches its target. The results show that the proposed method provides the robot reaches its target with colliding free obstacles, and the proposed method may be the alternative method of optimization for robot planning.

关键词： whale optimization algorithm Path planning

来源：评论

学校读者我要写书评

暂无评论

whale algorithm for Schedule optimization of Construction Projects Employing Building Information Modeling

ENGINEERING REPORTS

引用

ENGINEERING REPORTS 2025年第3期7卷

作者： Golmaei, S. M. Vahidi, J. Jamshidi, Morteza Islamic Azad Univ Dept Civil Engn Sari Branch Sari Iran Iran Univ Sci & Technol Dept Comp Sci Tehran Iran Islamic Azad Univ Dept Civil Engn Chalous Branch Chalous Iran

This study introduces a new approach by applying the whale optimization algorithm (WOA) to create construction schedules using geometric data from Building Information Modeling (BIM). The algorithm utilizes 3D model information to establish stability criteria, which are organized in a Directed Design Structure Matrix (DSM). These criteria are integrated into the WOA Fitness function to enhance the constructability of schedules, where each schedule is symbolized as a unique whale. Through iterative WOA computations, the approach consistently achieves maximum constructability scores starting from randomly generated schedules, affirming the efficacy of this method. The results reveal that the proposed algorithm effectively produced fully executable project schedules from diverse inputs. Despite variations in computational times due to different input parameters, the experiments verified the consistent generation of schedules that are 100% executable.

关键词： construction scheduling design process industry foundation classes (IFC) three-dimensional model whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Price Penalty Factors based Approach for Emission Constrained Economic Dispatch Problem Solution using whale optimization algorithm

Price Penalty Factors based Approach for Emission Constraine...

引用

International Conference on Power Electronics, Intelligent Control and Energy Systems

作者： Indrajit N. Trivedi Narottam Jangir Pradeep Jangir Mahesh H. Pandya R.H. Bhesdadiya Arvind Kumar Department of Electrical Engineering Govt. Engineering College Department of Electrical Engineering Lukhdhirji Engineering College Department of Electrical Engineering S.S. Engineering College

ISBN: (纸本)9781467385886

In The main ambition of utility is to provide continuous reliable supply to customers, satisfying power balance, transmission loss while generators are allowed to be operated within rated limits. Meanwhile, achieve this purpose emission value and fuel cost should be as less as possible. An allowable deviation in fuel cost and feasible tolerance in fuel cost has been called emission constrained economic dispatch (ECED) problem. A new nature-inspired whale optimization algorithm (WOA) is based on concept of bubble-net hunting strategy is applied to solve ECED problem. ECED is a multi-criteria problem can transformed to single criteria using price penalty factor method. In this paper quadratic function together with emission value and fuel cost are considered as individual objective makes it multi-criteria problem. The effect of six penalty factors like "Min-Max", "Max-Max", "Min-Min", "Max-Min", "Average", "Common" price penalty factors and emission value of various pollutants gases exhalation are included. The emission constrained economic dispatch (ECED) problem is analysed for an IEEE-30 Bus system with six operational generator units. Results prove capability of WOA in solving ECED problem with different penalty factors.

关键词： whale optimization algorithm Bubble-net Hunting Strategy Emission Constrained Economic Dispatch Fuel Cost Emission Value

来源：评论

学校读者我要写书评

暂无评论

optimization of Leaky-ESN parameters based on dragonfly and whale algorithm

引用

PHYSICA SCRIPTA 2024年第1期99卷 016002-016002页

作者： Gong, Yuanpeng Lun, Shuxian Li, Ming Lu, Xiaodong Bohai Univ Coll Math Sci Jinzhou 121013 Peoples R China Bohai Univ Coll Control Sci & Engn Jinzhou 121013 Peoples R China Suqian Univ Suqian 223800 Peoples R China

In this research, we present a spiral flying mode-based dragonfly algorithm (DA-WOA). Since the traditional Dragonfly algorithm (DA) has a Levy flight, the final results obtained are random in nature and have poor global search capability. Changing to a spiral flight pattern not only enlarges the search area but also makes it easy to find the optimal solution. The Swish function is also used to optimize the various flight coefficients of the DA to avoid the gradient descent approaching 0. Adding a fitness coefficient to the final position updating equation can better control the distance between dragonflies and natural enemies and thus find the optimal solution. The convergence speed of the DA-WOA algorithm and the superiority of the global search are verified by 13 test functions. In this paper, DA-WOA is applied to Leaky-ESN to optimize three parameters of the Leaky-ESN system, namely, the leakage rate (alpha), the spectral radius of the connectivity weight matrix (rho), and the input scale factor (s in ). The experimental results demonstrate that DA-WOA has very strong prediction ability as well as very quick convergence speed for the predicted Sin and MG time series.

关键词： time series prediction meta-heuristic algorithm dragonfly algorithm whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

whale algorithm-Based optimization of Orderly Charging Strategies for Electrical Vehicles 3

Whale Algorithm-Based Optimization of Orderly Charging Strat...

引用

3rd International Conference on Energy and Electrical Power Systems (ICEEPS)

作者： Lu, Jianbin Wu, Ning Chen, Weidong Lin, Rui Xiao, Jing Guangxi Power Grid Co Ltd Elect Power Res Inst Nanning Peoples R China Guangxi Key Lab Intelligent Control & Maintenance Nanning Peoples R China Guangxi Power Grid Co Ltd Nanning Peoples R China

ISBN: (纸本)9798350375145;9798350375138

As of October 2023, the number of new energy vehicles in China has surpassed 18 million. The surge in electrical vehicle (EV) numbers has led to significant adverse impacts on the power grid due to the charging load they create. Unorderly large-scale charging of EVs, in particular, exacerbates the problem by adding additional peaks to the existing demand peaks, severely affecting the stability of the power system. Therefore, it holds paramount significance in guiding EVs toward orderly charging. This paper commences by analyzing the impact of unorderly EV charging on the power grid, culminating in an orderly charging strategy. This strategy clearly defines the expected outcomes through an objective function and ensures feasibility and effectiveness through constraint conditions. The orderly charging strategy undergoes optimization with the aid of an enhanced whale optimization algorithm (WOA), followed by corresponding case studies. This paper considers multiple objectives and constraints in the construction of the orderly charging strategy and effectively optimizes the strategy using an enhanced optimization algorithm, thereby facilitating orderly charging for EV users and reducing fluctuations in grid load.

关键词： Orderly charging Charging load peak-valley difference whale optimization algorithm Strategy optimization

来源：评论

学校读者我要写书评

暂无评论

optimization of Adaptive Fuzzy Controller for Maximum Power Point Tracking Using whale algorithm

引用

Computers, Materials & Continua 2022年第12期73卷 5041-5061页

作者： Mehrdad Ahmadi Kamarposhti Hassan Shokouhandeh Ilhami Colak Kei Eguchi Department of Electrical Engineering Jouybar BranchIslamic Azad UniversityJouybarIran Department of Electrical Engineering Semnan UniversitySemnanIran Department of Electrical and Electronics Engineering Faculty of Engineering and ArchitecturesNisantasi UniversityIstanbulTurkey Department of Information Electronics Fukuoka Institute of TechnologyFukuokaJapan

The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point *** capability of online fuzzy tracking systems is maximum power,resistance to radiation and temperature changes,and no need for external sensors to measure radiation intensity and ***,the most important issue is the constant changes in the amount of sunlight that cause the maximum power point to be constantly *** controller used in the maximum power point tracking(MPPT)circuit must be able to adapt to the new radiation ***,in this paper,to more accurately track the maximumpower point of the solar system and receive more electrical power at its output,an adaptive fuzzy control was proposed,the parameters of which are optimized by the whale *** studies have repeated under different irradiation conditions and the proposed controller performance has been compared with perturb and observe algorithm(P&O)method,which is a practical and high-performance *** evaluate the performance of the proposed algorithm,the particle swarm algorithm optimized the adaptive fuzzy *** simulation results show that the adaptive fuzzy control system performs better than the P&O tracking *** accuracy and consequently more production power at the output of the solar panel is one of the salient features of the proposed control method,which distinguishes it from other *** the other hand,the adaptive fuzzy controller optimized by the whale algorithm has been able to perform relatively better than the controller designed by the particle swarm algorithm,which confirms the higher accuracy of the proposed algorithm.

关键词： Maximum power tracking photovoltaic system adaptive fuzzy control whale optimization algorithm particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Hybrid Noise Reduction Filter Using the Gaining–Sharing Knowledge-Based optimization and the whale optimization algorithms

引用

SN Computer Science 2024年第4期5卷 417页

作者： Nabahat, Mehrdad Khiyabani, Farzin Modarres Navmipour, Nima Jafari Department of Mathematics Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran

Noise reduction is one of the main challenges for researchers. Classical image de-noising methods reduce the image noise but sometimes lose image quality and information, such as blurring the edges of the image. To solve this challenge, this work proposes two optimal filters based on a generalized Cauchy (GC) distribution and two different nature-inspired algorithms that preserve image information while decreasing the noise. The generalized Cauchy filter and the bilateral filter are two parameter-based filters that significantly remove image noise. Parameter-based filters require proper parameter selection to remove the noise and maintain the edge details. To this end, two filters are considered. In the previous works, the parameters of the mask that was made with the GC function were optimized and the mask size was considered fixed. By studying different noisy images, we find that the selected mask size significantly impacts the designed filter performance. Therefore in this paper, a mask is designed using the GC function to formulate the first filter, and despite the optimization of the filter parameters, the selected mask size is also optimized using the peak signal-to-noise ratio (PSNR) as a fitness function. In most metaheuristic-based bilateral filters, only the domain and range parameters, which are based on Gaussian distribution, are optimized and the neighboring radius is a constant value. Filter results on different noisy images show that the neighboring radius has a major effect on the filter performance. Since the filter designed with the GC function causes significant noise removal, this function is effective, and on the other hand, it’s almost similar behavior with the Gaussian function has caused it to be combined with the bilateral filter to design the second filter in this paper. The kernel of the domain and range is considered to be the GC function instead of the Gaussian function. The domain and range parameters and the neighboring radius are opti

关键词： Bilateral filter Gaining sharing knowledge-based optimization algorithm Generalized Cauchy filter Image de-noising Image noise Particle swarm optimization algorithm whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Thickness optimization and Experimental Validation of Incremental Collar Forming of Explosive Welded Al/Cu Bimetal Sheet with an Obround Hole Using Finite Element, whale algorithm and Neural Network

引用

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF MECHANICAL ENGINEERING 2022年第4期46卷 885-900页

作者： Taheri, Ali Abdollahi Golabi, Sa'id Univ Kashan Fac Mech Engn Kashan Iran

Bimetal sheets are prominently expanded in industries due to their unique properties including high strength, low density, and corrosion resistance compared to single-layer sheets. On the other hand, incremental forming is found to be one of the most suitable methods to form complex parts. In this regard, a numerical and experimental study was conducted to consider the behavior of explosively welded aluminum-copper sheets. A nonlinear explicit solver was employed to simulate the incremental collar forming process. Experimental tests were performed to validate thickness variation, dimension and tool average force. A coupled artificial neural network and finite element method are utilized to generate an objective function of thickness reduction. The new metaheuristic whale optimization algorithm using a trained neural network (ANN-WOA) is utilized to improve sheet thickness distribution by finding optimum parameters. The developed artificial neural network could reliably predict thickness reduction in Al/Cu bimetal sheet during incremental collar forming while minimum thickness reduction occurs in the circular area of obround shape with less than 4% deviation from ANN results. It is also shown that optimum incremental forming parameters of obround shapes with length to diameter ratio between 2 and 3 are very close.

关键词： Single point incremental collar forming (SPICF) Thickness reduction Finite element whale optimization algorithm Artificial neural network Al-Cu bimetal

来源：评论

学校读者我要写书评

暂无评论

Magnetic Resonance Image of Breast Segmentation by Multi-Level Thresholding Using Moth-Flame optimization and whale optimization algorithms

引用

PATTERN RECOGNITION AND IMAGE ANALYSIS 2022年第1期32卷 174-186页

作者： Patra, Dipak Kumar Si, Tapas Mondal, Sukumar Mukherjee, Prakash RNL Khan Womens Coll Autonomous Res Ctr Nat & Appl Sci Dept Comp Sci Paschim Medinipur 721102 W Bengal India Bankura Unnayani Inst Engn Dept Comp Sci & Engn Bankura 722146 W Bengal India RNL Khan Womens Coll Autonomous Dept Math Paschim Medinipur 721102 W Bengal India Hijli Coll Dept Math Kharagpur 721306 W Bengal India

In this paper, we propose two breast lesion segmentation methods in dynamic contrast enhanced magnetic resonance image (DCE-MRI) using moth-flame optimizer (MFO) and whale optimization algorithm (WOA). In the first method, at the outset, MR images are denoised using the median filter in the preprocessing step. After that, a multi-level thresholding technique using MFO is used to search the suitable thresholds through entropy maximization to segment the lesions in MR images. In the second method, WOA is used in place of MFO in the framework of the first method. Segment the breast DCE-MRI lesion detection using MFO and WOA. The proposed methods are applied to 50 Sagittal T2-weighted DCE-MRI slices of 10 patients. The proposed methods are compared with algorithms such as particle swarm optimizer (PSO), improved Markov random field (IMRF), hidden Markov random field (HMRF), and conventional Markov random field (CMRF) methods. The high accuracy level of 99.88% and sensitivity 95.51% are achieved using the proposed MFO segmentation method. The high accuracy and sensitivity level of another proposed method WOA are achieved, 99.78 and 93.09%, respectively. The experimental results demonstrate that the proposed methods perform better than other methods in breast lesion segmentation in DCE-MRI.

关键词： breast cancer dynamic contrast enhanced-magnetic resonance image segmentation entropy moth-flame optimization whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Multipopulation whale optimization-Based Feature Selection algorithm and Its Application in Human Fall Detection Using Inertial Measurement Unit Sensors

引用

SENSORS 2024年第24期24卷 7879页

作者： Cao, Haolin Yan, Bingshuo Dong, Lin Yuan, Xianfeng Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China

Feature selection (FS) is a key process in many pattern-recognition tasks, which reduces dimensionality by eliminating redundant or irrelevant features. However, for complex high-dimensional issues, traditional FS methods cannot find the ideal feature combination. To overcome this disadvantage, this paper presents a multispiral whale optimization algorithm (MSWOA) for feature selection. First, an Adaptive Multipopulation merging Strategy (AMS) is presented, which uses exponential variation and individual location information to divide the population, thus avoiding the premature aggregation of subpopulations and increasing candidate feature subsets. Second, a Double Spiral updating Strategy (DSS) is devised to break out of search stagnations by discovering new individual positions continuously. Last, to facilitate the convergence speed, a Baleen neighborhood Exploitation Strategy (BES) which mimics the behavior of whale tentacles is proposed. The presented algorithm is thoroughly compared with six state-of-the-art meta-heuristic methods and six promising WOA-based algorithms on 20 UCI datasets. Experimental results indicate that the proposed method is superior to other well-known competitors in most cases. In addition, the proposed method is utilized to perform feature selection in human fall-detection tasks, and extensive real experimental results further illustrate the superior ability of the proposed method in addressing practical problems.

关键词： feature selection whale optimization algorithm multipopulation human fall detection

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：