检索结果-内蒙古大学图书馆

Color difference classification of solid color printing and dyeing products based on optimization of the extreme learning machine of the improved whale optimization algorithm

引用

TEXTILE RESEARCH JOURNAL 2020年第2期90卷 135-155页

作者： Zhou, Zhiyu Wang, Chao Zhang, Jianxin Zhu, Zefei Zhejiang Sci Tech Univ Sch Informat Sci & Technol Xiasha Hang Zhou Zhejiang Peoples R China Zhejiang Sci Tech Univ Res Ctr Modern Text Machinery Technol Minist Educ Xiasha Hang Zhou Zhejiang Peoples R China Hangzhou Dianzi Univ Sch Mech Engn Hangzhou Peoples R China

To mitigate the problem of low classification accuracy in solid color printing and dyeing, a color difference classification model based on the differential evolution (DE) improved whale optimization algorithm (WOA) for extreme learning machine (ELM) optimization, named the DE-WOA-ELM, was developed in this study. Considering that the initial population of the WOA has a significant influence on the solution speed and quality, DE was used to generate a more suitable initial population for the WOA by avoiding local optima, thereby improving the performance. The method used an excellent global search ability to improve the WOA for optimization and obtained an optimal parameter combination for the ELM. Thus, the problem of randomly initializing the input weight and the hidden layer bias of the ELM, which leads to a nonuniform training model and unstable algorithm, was solved. Finally, by optimizing the input weight and hidden layer bias, the color difference classification model of the ELM with a strong generalization ability was constructed. The results of the color difference classification experiments on fabric images collected under standard light sources show that the average classification accuracy for the dataset is increased by 2.15%, 11.06%, 12.11%, and 0.47% compared with those of the ELM, support vector machine, back propagation neural network, and kernel ELM, respectively.

关键词： color difference classification differential evolution extreme learning machine whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

An Author Gender Detection Method Using whale optimization algorithm and Artificial Neural Network

引用

IEEE ACCESS 2020年 8卷 48428-48437页

作者： Safara, Fatemeh Mohammed, Amin Salih Potrus, Moayad Yousif Ali, Saqib Quan Thanh Tho Souri, Alireza Janenia, Fereshteh Hosseinzadeh, Mehdi Islamic Azad Univ Islamshahr Branch Dept Comp Engn Islamshahr *** Iran Lebanese French Univ Dept Comp Engn Erbil 44001 Iraq Salahaddin Univ Erbil Dept Software & Informat Engn Erbil 44001 Iraq Sultan Qaboos Univ Dept Informat Syst Coll Econ & Polit Sci Muscat 123 Oman Vietnam Natl Univ Ho Chi Minh City Univ Technol Dept Software Engn Ho Chi Minh City 76000 Vietnam Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran *** Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Elect Elect Engn Da Nang 550000 Vietnam Iran Univ Med Sci Hlth Management & Econ Res Ctr Tehran *** Iran

Author gender detection (AGD) is a serious and crucial issue in Internet security applications, in particular in email, messenger, and social network communications. Detecting the gender of communication partner helps preventing massive fraud and abuses happening through social media such as email, blogs, forums. Text and writings of people on the Internet have valuable information that can be used to identify the gender of an author. Machine learning and meta-heuristic algorithms are valuable techniques to extract hidden patterns useful for detecting gender of a text. In this paper, an artificial neural network (ANN) is employed as a classifier to detect the gender of an email author and the whale optimization algorithm (WOA) is used to find optimal weights and biases for improving the accuracy of the ANN classification. Through this combination of ANN and WOA an accuracy of 98 & x0025;, precision of 97.16 & x0025;, and recall of 99.67 & x0025;were achieved, which indicates the superiority of the proposed method on Bayesian networks, regression, decision tree, support vector machine, and ANN examined.

关键词： Author gender detection machine learning artificial neural network whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Rate of penetration modeling using hybridization extreme learning machine and whale optimization algorithm

引用

EARTH SCIENCE INFORMATICS 2020年第4期13卷 1351-1368页

作者： Youcefi, Mohamed Riad Hadjadj, Ahmed Bentriou, Abdelhak Boukredera, Farouk Said Univ Mhamed Bougara Boumerdes Fac Hydrocarbons & Chem Lab Petr Equipments Reliabil & Mat Ave Independance Boumerdes 35000 Algeria

Modeling the rate of penetration (ROP) plays a fundamental role in drilling optimization since the achievement of an optimum ROP can drastically reduce the overall cost of drilling activities. Evolved Extreme learning machine (ELM) with the evolutionary algorithms and multi-layer perceptron with Levenberg-Marquardt training algorithm (MLP-LMA) were proposed in this study to predict ROP. This paper focused mainly on two aspects. The first one was the investigation of the whale optimization algorithm (WOA) to optimize the weights and biases between input and hidden layers of ELM to enhance its prediction accuracy. The other was to adopt a prediction methodology that seeks to update the predictive model at each formation in order to reduce the dimension of input data and mitigate the effect of non real-time data such as the formation properties on the bit speed prediction. The prediction models were trained and tested using 3561 data points gathered from an Algerian field. The statistical and graphical evaluation criteria show that the ELM-WOA exhibited higher accuracy and generalization performance compared with the ELM-PSO and MLP-LMA. Furthermore, ELM-WOA was compared with two well-known ROP correlations in the literature, and the comparison results reveal that the proposed ELM-WOA model is superior to the pre-existing correlations. The findings of this study can help for the achievement of an optimum ROP and the reduction of the non-productive time. In addition, the outputs of this study can be used as an objective function during the real-time optimization of the drilling operation.

关键词： Rate of penetration Drilling optimization Extreme learning machine Multi-layer perceptron whale optimization algorithm Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Optimum utilization of grid-connected hybrid power system using hybrid particle swarm optimization/whale optimization algorithm

ENERGY STORAGE

引用

ENERGY STORAGE 2022年第4期4卷

作者： Oladepo, Olatunde Ajewole, Titus O. Awofolaju, Tolulope T. Osun State Univ Dept Elect & Elect Engn Osogbo Osun State Nigeria

Network management and optimization play a crucial role in integrating renewable energy sources on the distribution network. This paper presents the optimization of the grid-connected photovoltaic/small hydropower hybrid power system using a hybrid particle swarm optimization-whale optimization algorithm. The exploration strength of the particle swarm optimization is hybridized with the exploitation capability of the whale optimization algorithm for improved convergence, speed, and optimal network performance. The objective function is to minimize the feeder voltage deviation and power loss. The effectiveness of the proposed approach is verified on the standard 118-bus distribution system. The simulation is carried out with step changes in irradiation and loading conditions. The optimization result from the two hybridized algorithms is compared to its standalone form, simulated under the same conditions. The results show that the proposed method is superior in the optimal solution and convergence property. The results confirm the technique's contribution to improved network management and grid-connected hybrid renewable power systems. It significantly reduced the voltage deviation by 62.58% on multiple locations of renewable sources. The voltage is achieved within the statutory range at all hours of the daily simulation. The technique is envisaged to be very useful in distribution network operators.

关键词： distribution network hybrid power and energy storage system particle swarm optimization renewable energy sources small hydropower solar photovoltaic whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Apache Spark Implementation of whale optimization algorithm

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2020年第3期23卷 2021-2034页

作者： AlJame, Maryam Ahmad, Imtiaz Alfailakawi, Mohammad Kuwait Univ Dept Comp Engn Kuwait Kuwait

Population-based meta-heuristic algorithms are among the dominant algorithms used to solve challenging real world problems in diverse fields. whale optimization algorithm (WOA) is a recent swarm intelligence meta-heuristic algorithm based on the bubble-net feeding behavior of humpback whales. Despite its capability to solve complex optimization problems, WOA requires enormous amount of computations when solving large size problems. This work proposes Spark-WOA, a distributed implementation of WOA on Apache Spark platform to enhance its performance and reduce computational complexity. The proposed algorithm exploits in-memory computations and broadcast features of Apache Spark to provide better performance and scalability. Details of the proposed algorithm are presented and its performance as compared to a recent Apache Hadoop implementation is discussed. Experimental results demonstrated the superiority of the proposed implementation in terms of both speed and scalability.

关键词： Apache Spark Cluster Hadoop whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Particle whale optimization algorithm with application to workflow scheduling in cloud–fog environment

引用

Decision Analytics Journal 2023年 9卷

作者： Bansal, Sumit Aggarwal, Himanshu Department of CSE Punjabi University Patiala India

Workflow scheduling aims to optimize task allocation and execution in cloud–fog computing environments. Finding an optimal algorithm for workflow scheduling poses a significant challenge due to the complexity and variability of tasks and resources involved. Although meta-heuristic algorithms have been proposed to address these challenges, they often suffer from being trapped in local optima, failing to achieve the global optimal solution. Our work used a hybrid approach “Particle whale optimization algorithm” (PWOA) to overcome the above-mentioned problem by combining Particle Swarm optimization (PSO) and whale optimization algorithm (WOA). PSO's limitation in high-dimensional search spaces, characterized by slow convergence toward the global optimum, and the population-based WOA algorithm's limited exploration of search spaces are overcome by the proposed algorithm PWOA. The proposed algorithm PWOA overcomes the limitations of PSO and WOA. The objective of the PWOA algorithm is to minimize the Total Execution Time (TET) and Total Execution Cost (TEC) associated with dependent tasks in a cloud–fog computing environment. To evaluate the performance of the PWOA algorithm, extensive simulations are conducted using test cases (30, 50, 100 and 1000) from five different scientific workflows: Cybershake, Epigenomics, Inspiral, Montage and Sipht. These workflows encompassed varying numbers of assigned tasks. The simulation results demonstrated that the proposed PWOA algorithm outperformed the standard PSO and WOA algorithms to improve TET and TEC across all the diverse workflows. This study also provides a comparative view of the effectiveness of the PWOA algorithm in enhancing workflow scheduling performance. © 2023 The Author(s)

关键词： Cloud–fog computing optimization Particle Swarm optimization Simulation whale optimization algorithm Workflow scheduling

来源：评论

学校读者我要写书评

暂无评论

Joints Trajectory Planning of Robot Based on Slime Mould whale optimization algorithm

引用

algorithmS 2022年第10期15卷 363-363页

作者： Li, Xinning Yang, Qin Wu, Hu Tan, Shuai He, Qun Wang, Neng Yang, Xianhai Shandong Univ Technol Sch Mech Engn Zibo 255000 Peoples R China Gachon Univ Grad Sch Seongnam Si 13120 South Korea Natl Engn Res Ctr Prod Equipment Dongying 257091 Peoples R China

The joints running trajectory of a robot directly affects it's working efficiency, stationarity and working quality. To solve the problems of slow convergence speed and weak global search ability in the current commonly used joint trajectory optimization algorithms, a joint trajectory planning method based on slime mould whale optimization algorithm (SMWOA) was researched, which could obtain the joint trajectory within a short time and with low energy consumption. On the basis of analyses of the whale optimization algorithm (WOA) and slime mould algorithm (SMA) in detail, the SMWOA was proposed by combining the two methods. By adjusting dynamic parameters and introducing dynamic weights, the proposed SMWOA increased the probability of obtaining the global optimal solution. The optimized results of 15 benchmark functions verified that the optimization accuracy of the SMWOA is clearly better than that of other classical algorithms. An experiment was carried out in which this algorithm was applied to joint trajectory optimization. Taking 6-DOF UR5 manipulator as an example, the results show that the optimized running time of the joints is reduced by 37.674% compared with that before optimization. The efficiency of robot joint motion was improved. This study provides a theoretical basis for the optimization of other engineering fields.

关键词： slime mould algorithm whale optimization algorithm trajectory planning meta-heuristic algorithm zero-defect manufacturing

来源：评论

学校读者我要写书评

暂无评论

Application of Improved whale optimization algorithm in Parameter Identification of Hydraulic Turbine at No-Load

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第12期45卷 9913-9924页

作者： Tian, Tian Zhao, Wei Zhen, Wenxi Liu, Changyu Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China State Grid Gansu Elect Power Res Inst Lanzhou 730070 Gansu Peoples R China

The accuracy of hydraulic turbine model has a direct influence on controller design and system stability. This paper presents an improved whale optimization algorithm (IWOA) and its application in parameter identification of hydraulic turbine at no-load. In IWOA, two strategies including the increase in global exploration probability and combination of immune operator are introduced to avoid local optimum. Besides, in the identification process, the adaptive modification method is developed to solve the estimated parameter range uncertainty problem. Finally, in the example of Unit 4 in Huanglongtan Hydropower Plant, China, the results of different methods are compared. Considering the indicators of cost, iteration and total computation time, the results show that IWOA has faster convergence and higher precision than WOA.

关键词： Hydraulic turbine governing system Hydroelectric generating unit No-load Parameter identification whale optimization algorithm Measured data

来源：评论

学校读者我要写书评

暂无评论

Modified whale optimization algorithm for Infinitive Impulse Response System Identification

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第3期45卷 2163-2176页

作者： Luo, Qifang Ling, Ying Zhou, Yongquan Guangxi Univ Nationalities Coll Informat Sci Engn Nanning Peoples R China Key Labs Guangxi High Schools Complex Syst Computat Intelligence Nanning Guangxi Peoples R China

System identification based on infinite impulse response (IIR) models has received much attention because it is used in a variety of real-world applications. However, an IIR model might have a multimodal error surface. To attain an ideal identification, an efficient and robust method is necessary. In this study, a modified whale optimization algorithm (WOA) with a ranking-based mutation operator, called the RWOA, is presented to solve the IIR system identification problem. The RWOA integrates a ranking-based mutation operator into the basic WOA to enhance performance by speeding up the convergence rate and then enhances the exploitation capability. The experimental results of actual and reduced-order identification for a standard system using our proposed RWOA were superior to those of five state-of-the-art algorithms (including the basic WOA), in terms of improving the quality and stability of the results, in most cases, and significantly speeding up convergence.

关键词： Ranking-based mutation operator whale optimization algorithm RWOA IIR system System identification

来源：评论

学校读者我要写书评

暂无评论

Chaotic whale optimization algorithm for optimal solution of combined heat and power economic dispatch problem incorporating wind

引用

RENEWABLE ENERGY FOCUS 2020年 35卷 56-71页

作者： Paul, Chandan Roy, Provas Kumar Mukherjee, V. Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India Kalyani Govt Engn Coll Dept Elect Engn Kalyani W Bengal India Indian Inst Technol Indian Sch Mines Dept Elect Engn Dhanbad Bihar India

In this article, a new practical approach is implemented to analyze combined heat and power economic dispatch (CHPED) and wind based CHPED (CHPEDW) problems with a chaotic based whale optimization algorithm (WOA). The conflict objectives of this problem are used to minimize fuel costs as well as emission. The proposedWOAworks on the searching behavior of humpback whales, which helps to satisfy the problem's objective functions. The chaotic nature has been integrated with WOA to enhance the convergence speed of the problem. The presence of valve point loading, prohibited operating zone, and uncertainty of wind has expanded the problem nonlinearity. It has been tested on two different nonlinear realistic power areas to judge the satisfactory results of the proposed algorithm. The superiority of the proposed algorithm is judged by comparing it with presently developed some other meta-heuristic optimization techniques.

关键词： Chaotic whale optimization algorithm Combined heat and power economic dispatch whale optimization algorithm Wind based combined heat and power economic dispatch.

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：