检索结果-内蒙古大学图书馆

whale optimization based Deep Residual Learning Network for Early Rice Disease Prediction in IoT

EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2023年第6期10卷

作者： Lakshmi, M. Sri Kashyap, K. Jayadwaja Khan, S. Mohammed Fazal Reddy, N. Jaya Satya Vratha Achari, V. Bharath Kumar G Pullaiah Coll Engn & Technol Dept CSE Kurnool India G Pullaiah Coll Engn & Technol Kurnool India

Disease detection on a farm requires laborious and time-consuming observation of individual plants, which is made more difficult when the farm is large and many different plants are farmed. To address these problems, cutting-edge technologies, AI, and Deep Learning (DL) are employed to provide more accurate illness predictions. When it comes to smart farming and precision agriculture, IoT opens up exciting new possibilities. To a certain extent, the goal-mouth of "smart farming" is to upsurge productivity and efficiency in agricultural processes. Smart farming is an approach to agriculture in which Internet of Things devices are interconnected and new technologies are used to optimize existing methods. Utilizing Internet of Things (IoT) devices, smart farming aids in more informed decision making. In many parts of the world, rice is the staple diet. This means that early detection of rice plant diseases using automated techniques and IoT devices is essential. Growing rice yields and profits may be helped along by DL model creation and deployment in agriculture. Here we introduce DRL, a deep residual learning framework that has been trained using photos of rice leaves to recognize one of four classes. The suggested model is called WO-DRL, and the hyper-parameter tuning procedure of DRL is executed with the help of the whale optimization algorithm. The outcomes demonstrate the efficacy of our suggested approach in directing the WO-DRL model to learn important characteristics. The findings of this study will pave the way for the agriculture sector to more quickly diagnose and treat plant diseases using AI.

关键词： Internet of Things whale optimization algorithm Metaheuristic Deep Residual Learning Framework Rice Plant Disease Smart farming Precision agriculture

来源：评论

学校读者我要写书评

暂无评论

An improved learning automata based multi-objective whale optimization approach for multi-objective portfolio optimization in financial markets

引用

EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期224卷

作者： Morteza, Hakimeh Jameii, Seyed Mahdi Sohrabi, Mohammad Karim Islamic Azad Univ Dept Comp Engn Shahr E Qods Branch Tehran Iran Islamic Azad Univ Dept Comp Engn Semnan Branch Semnan Iran

Exploiting meta-heuristic algorithms is common and popular in financial research areas, because of their ability to efficiently discover the large search spaces in the stock portfolio optimization problem. Increasing the speed of convergence and accuracy in stock selection and creating a portfolio with the highest rate of return and the lowest rate of risk in investment is of particular importance. This paper aims to present a whale optimization -based multi-objective approach to optimize the portfolio in the financial markets. The proposed optimization approach, called PMP-WOA (periodic multi-population whale optimization approach), creates the Pareto fronts of portfolios at an appropriate convergence time and proper accuracy to achieve higher return and liquidity and lower risk as its main goals. The dynamic use of multiple sub-populations and the exchange of information between them have a great impact on increasing the accuracy of solutions and proximity to the real Pareto front. Moreover, controlling the parameters of the algorithm using learning automata leads to the selection of the appropriate search path and speeds up the convergence of the approach. Furthermore, PMP-WOA creates an archive of non-dominated solutions that increases the variety of solutions, which is one of the important issues in creating the Pareto front. In order to evaluate PMP-WOA, it is first used with four multi-objective meta-heuristic algorithms to optimize a number of ZDT mathematical test functions, and then with 6 existing meta-heuristic algorithms for portfolio optimization. The obtained results show the lower generational distance that means higher accuracy, higher number of Pareto solutions, and higher value of maximum spread, and lower value of spacing, which together indicate higher diversity of the proposed solution in the optimization of the ZDT test functions and also in the portfolio optimization compared to the other existing methods. The higher convergence speed of PMP-

关键词： Portfolio optimization whale optimization algorithm Periodic multi -population Learning automata

来源：评论

学校读者我要写书评

暂无评论

An Improved Sparrow Search algorithm for Location optimization of Logistics Distribution Centers

引用

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2023年第9期32卷 2350150-2350150页

作者： Ou, Yaqin Yu, Lei Yan, Ailing Chaohu Coll Sch Business Adm Chaohu 238024 Peoples R China

In modern society where business development is booming, it is of great importance to investigate location optimization of logistics distribution centers. In order to achieve greater efficiency of distribution system, save the distribution costs and the construction cost of logistics distribution center, good point set method and decreasing nonlinear inertia weight are proposed to improve sparrow search algorithm (SSA) to solve the mathematical model for logistics distribution center location. First, in order to prevent SSA falling in local optimal, while improving the convergence speed and efficiency of SSA, good point set method and decreasing nonlinear inertia weight are applied to improve the SSA. The test results of eight benchmark functions show that the fitness value and convergence speed of the proposed ISSA are smaller and faster. Second, compared with SSA, WOSA and PSO, the ISSA has the lowest total cost for solving the mathematical model of logistics distribution center.

关键词： Sparrow search algorithm whale optimization algorithm logistics distribution center good set point

来源：评论

学校读者我要写书评

暂无评论

Novel hybrid models by coupling support vector regression (SVR) with meta-heuristic algorithms (WOA and GWO) for flood susceptibility mapping

引用

NATURAL HAZARDS 2022年第2期114卷 1247-1283页

作者： Rezaie, Fatemeh Panahi, Mahdi Bateni, Sayed M. Jun, Changhyun Neale, Christopher M. U. Lee, Saro Korea Inst Geosci & Mineral Resources KIGAM Geosci Data Ctr 124 Gwahak Ro Daejeon 34132 South Korea Korea Univ Sci & Technol Dept Geophys Explorat 217 Gajeong Ro Daejeon 34113 South Korea Kangwon Natl Univ Div Sci Educ 1 Gangwondaehak Gil Chuncheon Si 24341 Gangwon Do South Korea Univ Hawaii Manoa Dept Civil & Environm Engn Honolulu HI 96822 USA Univ Hawaii Manoa Water Resources Res Ctr Honolulu HI 96822 USA Chung Ang Univ Coll Engn Dept Civil & Environm Engn Seoul 06974 South Korea Univ Nebraska Daugherty Water Food Global Inst Lincoln NE USA

Schools as social bases and children's centers are among the most vulnerable areas to flooding. Flood susceptibility mapping is very important for flood preparedness and adopting preventive plans for reducing the school vulnerability to flooding. To achieve this, there is a need for the models that can be used in vast areas with high predictive accuracy. This study aims to develop the innovative hybrid models by coupling the support vector regression (SVR), statistical approaches, and two meta-heuristic algorithms, whale optimization algorithms (WOA) as well as grey wolf optimizer (GWO). According to the proposed methodology, a hybrid feature of SVR and frequency ratio (FR-SVR) is optimized by applying the GWO and WOA optimization algorithms to generate the maps related to flood susceptibility. The method was utilized for the Ardabil Province located in southwestern Caspian Sea precincts of which faced devastating floods. The GIS database including 147 ground control locations of flooded zones and nine factors which influence flood were utilized to learn and ascertain the validity of the models. Three statistical metrics namely, mean absolute error (MAE), root mean square error (RMSE), and the area under the receiver operating characteristic curve (AUC) were computed for the developed models in order to estimate prophetically. The results indicated that the meta-optimized FR-SVR-GWO as well as FR-SVR-WOA models exceeded the FR-SVR and FR models in training (RMSEFR-SVR-WOA = 0.2016, RMSEFR-SVR-GWO = 0.1885, AUC(FR-SVR-WOA) = 0.87, AUC(FR-SVR-GWO) = 0.88) and validation (RMSEFR-SVR-WOA = 0.2025, RMSEFR-SVR-GWO = 0.1986, AUC(FR-SVR-WOA) = 0.87, AUC(FR-SVR-GWO) = 0.87) phases. The FR-SVR-WOA and FR-SVR-GWO models were very competitive regarding AUC and RMSE values, but the FR-SVR-WOA model reproduced greater flood susceptibility rates and was considered for identifying vulnerability of schools to flood events. To this end, number of schools, number of students in each s

关键词： Flood susceptibility map Grey wolf optimizer whale optimization algorithm SVR Frequency ratio Iran

来源：评论

学校读者我要写书评

暂无评论

Machine learning and particle swarm inspired success history based adaptive multi-objective differential evolution for optimization of heaving buoy point absorber

引用

OCEAN ENGINEERING 2023年第1期284卷

作者： Saveca, John Sun, Yanxia Wang, Zenghui Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa Univ South Africa Dept Elect Engn ZA-1710 Johannesburg South Africa

Success-History-Based Parameter Adaptation Multi-Objective Differential Evolution (SHAMODE) has shown excellent performance recently compared to other multi-objective evolutionary algorithms (MOEAs) and therefore caught the interest of researchers lately. The SHAMODE framework employs historical memory of control parameters, which stores set values of control parameters that have performed well in the past generation and produces new set values of the parameter by directly sampling the parameter space close to one of these stored pairs. Since the algorithm's performance depends on the problem to be optimized, this paper proposes an enhanced version of SHAMODE that is compatible with optimizing the multi-objective wave energy converter problem. The improved version of SHAMODE is achieved by incorporating competing machine learning algo-rithms, Particle Swarm optimization, and a modified bubble-net attacking method formula of the whale opti-mization algorithm from its original form. Simulations and comparisons are used to verify the proposed method's performance based on some famous multi-objective problem (MOP) benchmark functions. The results reveal that the proposed algorithm performs better than other algorithms that have been recently developed. In addition, the suggested technique is used to optimize the multi-objective wave energy converter model in order to maximize the annual production of electric energy while minimizing the per-unit energy cost.

关键词： Success-history based parameter adaptation multi-objective differential evolution Artificial neural network Q-learning Wave energy converter Point absorber whale optimization algorithm Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

A Bio-Inspired Cluster optimization Schema for Efficient Routing in Vehicular Ad Hoc Networks (VANETs)

引用

ENERGIES 2023年第3期16卷 1456页

作者： Husnain, Ghassan Anwar, Shahzad Sikander, Gulbadan Ali, Armughan Lim, Sangsoon Iqra Natl Univ Dept Comp Sci Peshawar 25100 Pakistan Univ Engn & Technol Dept Mechatron Engn Peshawar 25100 Pakistan Univ Engn & Technol Natl Ctr Artificial Intelligence NCAI Intelligent Informat Proc Lab Peshawar 25000 Pakistan COMSATS Univ Islamabad Attock Campus Islamabad 43600 Pakistan Sungkyul Univ Dept Comp Engn Anyang 14097 South Korea

Vehicular ad hoc networks (VANETs) are vital to many Intelligent Transportation System (ITS)-enabled technologies, including efficient traffic control, media applications, and encrypted financial transactions. Due to an increase in traffic, vehicular network topology is constantly changing, and sparse vehicle distribution (on highways) hinders network scalability. Thus, there is a challenge for all vehicles (in the network) to maintain a stable route, which would increase network instability. Concerning IoT-based network transportation, this study proposes a bio-inspired, cluster-based algorithm for routing, i.e., the intelligent, probability-based, and nature-inspired whale optimization algorithm (p-WOA), which produces cluster formation in vehicular communication. Various parameters, such as communication range, number of nodes, velocity, and route along the highway were considered, and their probaabilities were incorporated into the fitness function, hence resulting in randomness reduction. Results were compared to existing methods such as Ant Lion Optimizer (ALO) and Grey Wolf optimization (GWO), demonstrating that the developed p-WOA technique produces an optimal number of cluster heads (CH). The results achieved by calculating the Packet Delivery Ratio (PDR), average throughput, and latency demonstrate the superiority of the proposed method over other well-established methodologies (ALO and GWO). This study confirms statistically that VANETs employing ITS applications optimize their clusters by a factor of 75, which has the twin benefits of decreasing communication costs and routing overhead and extending the life of the cluster as a whole.

关键词： bio-inspired algorithms clustering vehicular networks whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A novel combined forecasting system based on advanced optimization algorithm-A study on optimal interval prediction of wind speed

引用

ENERGY 2023年 264卷

作者： Li, Jingrui Wang, Jiyang Li, Zhiwu Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

Wind speed forecasting is becoming increasingly crucial for an environmentally friendly and sustainable economy because of the renewability and benefits of wind energy. Currently, many scholars have proposed various approaches to forecasting wind speed;however, due to the limitations of point prediction and drawbacks of traditional individual methods, it is challenging to acquire satisfactory results. Compared with the previous articles, this paper combines a data preprocessing strategy by fuzzification, interval estimation, and advanced optimization methods to enhance the accuracy of wind speed forecasting. Moreover, the weight coefficient allocated by a multi-objective algorithm is proved to reach Pareto optimal solution in theory. Based on the comparative experiments and discussion of the performance of the developed combined forecasting system and other control groups, it is revealed that the combined system not only outperforms for predicting wind speed with higher accuracy and stability but also enables a valid assessment of uncertainty than the former studies. It can be convinced that the developed predicting system is an appropriate and efficient tool for further practical applications in energy systems.

关键词： Wind speed forecasting Data preprocessing technique Combined system whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Multi-objective optimization for improving machining benefit based on WOA-BBPN and a Deep Double Q-Network

引用

APPLIED SOFT COMPUTING 2023年 142卷

作者： Lu, Juan Chen, Zhiheng Liao, Xiaoping Chen, Chaoyi Ouyang, Haibin Li, Steven Beibu Gulf Univ Guangxi Key Lab Ocean Engn Equipment & Technol Qinzhou 535011 Peoples R China Guangxi Univ Guangxi Key Lab Mfg Syst & Adv Mfg Technol Nanning 535004 Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China RMIT Univ Grad Sch Business & Law Melbourne Vic 3000 Australia Dongfeng Liuzhou Motor Co Ltd Liuzhou Peoples R China

To scientifically and reasonably set and adjust the machining parameters, and improve the machining benefit of the batch machining process including machining quality, performance and efficiency, this paper proposes a low cost and high accurate multi-objective optimization framework by integrating intelligent technologies from three perspectives of physical experiment design, objective function construction and multi-objective optimization. In this framework, multilayer design (MLD) which is a computer experiment design method with low redundancy, good uniformity and filling is applied to the machining physics experiment design to reduce the experiment cost. whale optimization algorithm (WOA) optimized backpropagation neural network (BBPN) is utilized to construct the optimization objective functions to improve the accuracy of functions. And Deep Double Q-Network (DDQN) is employed to achieve multi-objective optimization of machining quality, efficiency, and performance. The effectiveness of MLD, WOA-BBPN and DDQN in multi-objective optimization of machining process are verified by comparisons of common modeling and multi-objective optimization algorithms based on the milling data. These techniques provide a good guidance for the selection of machining parameters to improve the machining benefit and demonstrates some advantages. The research of this paper also offers a good example of applying intelligent technology in machining.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Backpropagation Neural Network Deep double Q-Network Machining benefit Multilayer design whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

whale optimization with random contraction and Rosenbrock method for COVID-19 disease prediction

引用

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第0期83卷 000-000页

作者： Zhang, Meilin Wu, Qianxi Chen, Huiling Heidari, Ali Asghar Cai, Zhennao Li, Jiaren Abdelrahim, Elsaid Md. Mansour, Romany F. Wenzhou Univ Inst Big Data & Informat Technol Wenzhou 325000 Peoples R China Wenzhou Peoples Hosp Wenzhou 325099 Zhejiang Peoples R China Northern Border Univ Fac Sci Ar Ar Saudi Arabia Tanta Univ Fac Sci Tanta Egypt New Valley Univ Fac Sci Dept Math El Kharga 72511 Egypt

Coronavirus Disease 2019 (COVID-19), instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has hugely impacted global public health. To identify and intervene in critically ill patients early, this paper proposes an efficient, intelligent prediction model based on the machine learning approach, which com-bines the improved whale optimization algorithm (RRWOA) with the k-nearest neighbor (KNN) classifier. In order to improve the problem that WOA is prone to fall into local optimum, an improved version named RRWOA is proposed based on the random contraction strategy (RCS) and the Rosenbrock method. To verify the capability of the proposed algorithm, RRWOA is tested against nine classical metaheuristics, nine advanced metaheuristics, and seven well-known WOA variants based on 30 IEEE CEC2014 competition functions, respectively. The experimental results in mean, standard deviation, the Friedman test, and the Wilcoxon signed-rank test are considered, proving that RRWOA won first place on 18, 24, and 25 test functions, respectively. In addition, a binary version of the algorithm, called BRRWOA, is developed for feature selection problems. An efficient prediction model based on BRRWOA and KNN classifier is proposed and compared with seven existing binary metaheuristics based on 15 datasets of UCI repositories. The experimental results show that the proposed al-gorithm obtains the smallest fitness value in eleven datasets and can solve combinatorial optimization problems, indicating that it still performs well in discrete cases. More importantly, the model was compared with five other algorithms on the COVID-19 dataset. The experiment outcomes demonstrate that the model offers a scientific framework to support clinical diagnostic decision-making. Therefore, RRWOA is an effectively improved opti-mizer with efficient value.

关键词： whale optimization algorithm Random contraction strategy Rosenbrock method Swarm intelligence Feature selection COVID-19 WOA

来源：评论

学校读者我要写书评

暂无评论

GM(1,1) Model Based on Parallel Quantum whale algorithm and Its Application 19th

GM(1,1) Model Based on Parallel Quantum Whale Algorithm and ...

引用

19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Huang, Huajuan Huang, Shixian Wei, Xiuxi Zhou, Yongquan Guangxi Minzu Univ Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi Minzu Univ Coll Elect Informat Nanning 530006 Peoples R China

ISBN: (纸本)9789819947416;9789819947423

Aiming at the problems of thinning and perforation leakage caused by corrosion in atmospheric distillation unit of a refinery, GM (1,1) model was used to predict the corrosion trend at the elbow of inlet distribution pipe of top heat exchanger of atmospheric distillation tower. Firstly, the exponential function is introduced to transform the original data of the traditional GM (1,1) model to improve the smoothness of the original sequence. Then, the background weights of the improved GM (1,1) model (EFGM (1,1)) are optimized by the proposed parallel quantum whale optimization algorithm (QWOA) to provide the optimal background weights for the model, so as to improve the prediction accuracy of the model. Finally, the QWOA-EFGM (1,1) model is established. The established new model is used to predict the corrosion at the elbow of the inlet distributor of the overhead heat exchanger of atmospheric distillation column. The prediction results of the actual example show that the QWOA-EFGM (1,1) model proposed in this paper has higher prediction accuracy and better stability than the traditional GM (1,1) model and EFGM (1,1) model.

关键词： whale optimization algorithm Parallel quantum theory GM(1,1) model Distillation apparatus Corrosion prediction

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：