检索结果-内蒙古大学图书馆

Exhaust emission reduction of a SI engine using acetone-gasoline fuel blends: Modeling, prediction, and whale optimization algorithm

引用

ENERGY REPORTS 2023年 9卷 77-86页

作者： Alahmer, Hussein Alahmer, Ali Alkhazaleh, Razan Alrbai, Mohammad Al Balqa Appl Univ Fac Artificial Intelligence Dept Automated Syst Al Salt 19117 Jordan Auburn Univ Dept Ind & Syst Engn Auburn AL 36849 USA Tafila Tech Univ Fac Engn Dept Mech Engn POB 179 Tafila 66110 Jordan Univ Jordan Sch Engn Dept Mech Engn Amman 11942 Jordan

Acetone-gasoline fuel is one potential favorable alternative option to regular gasoline fuel. This work attempts to mitigate pollution while simultaneously improving SI engine performance with acetone (AC) proportion in fuel mixture and varying engine speed. Acetone-gasoline alternative fuel was experimentally prepared by mixing 5-10 vol.% of acetone into ordinary gasoline. The experiments were performed in a spark ignition (SI) single-cylinder, four-stroke at variable engine speed. The engine performance was measured by an eddy current dynamometer (ECD), while the exhaust engine was measured by a gas analyzer connected to the gasoline engine. The proposed strategy, the integrated combined polynomial regression with whale optimization algorithm (WOA), was implemented. The results showed that the acetone blending of 10 % with gasoline (AC10: 10 vol. % acetone + 90 vol. % gasoline) decreases carbon monoxide (CO), unburned hydrocarbon (UHC), and nitrogen oxides (NOx), emissions by 26.3%, 30.3%, and 6.6%, respectively. Furthermore, the AC10 exhibited better engine brake power (BP) than pure gasoline, with an improvement of 4.39%. According to the optimization results, the BP was enhanced by AC10 to record 2.426 kW at 2887 rpm. In addition, engine emissions reduced to the lowest levels of 142.849 ppm, 0.426%, and 1088.178 ppm, in terms of UHC, CO, and NOx, respectively. The experiment results show that the WOA optimizer can accurately predict the optimal point in SI engine performance and emissions. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Acetone-gasoline blend SI engine Engine performance Emission control whale optimization algorithm Polynomial regression

来源：评论

学校读者我要写书评

暂无评论

Seismic Response Prediction and Velocity Model Building Inversion by the whale optimization algorithm

引用

PURE AND APPLIED GEOPHYSICS 2023年第6期180卷 2087-2109页

作者： Routa, Alok Kumar Mohanty, Priya Ranjan Indian Inst Technol Indian Sch Mines Dept Appl Geophys Dhanbad 826004 India

The whale optimization algorithm (WOA) is a popular swarm intelligence algorithm that is based on the bubble-net hunting strategy used by humpback whales. The objective of this study is to assess the utilization of the WOA to perform the inversion of seismic data. The primary focus of this optimization is to first target an objective function for inversion that leads to minimizing the RMS error between field observed data and whale predicted data. The algorithm maintains a balance between exploration and exploitation phases that results in a better solution to the desired problem. The application of this technique to 12 benchmark models and validation through a true Vp model helps to reconstruct P-wave velocity and true model parameter estimation. The performance of the algorithm is compared with the gray wolf optimization (GWO) algorithm. The experimental results show that the final model after a reasonable number of iterations is able to provide optimum solutions within 10 different randomly generated search populations. As a consequence, it can be stated that the approach has higher reliability to reveal the P-wave imaging with a good convergence rate and minimum uncertainty over any complex geology.

关键词： Inversion Meta-heuristic algorithm RMS error Velocity model whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

An Improved whale optimization algorithm for Feature Selection

引用

Computers, Materials & Continua 2020年第1期62卷 337-354页

作者： Wenyan Guo Ting Liu Fang Dai Peng Xu School of Science Xi’an University of TechnologyXi’an710054China

whale optimization algorithm(WOA)is a new population-based meta-heuristic *** uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s *** has merit in terms of simple calculation and high computational accuracy,but its convergence speed is slow and it is easy to fall into the local optimal *** order to overcome the shortcomings,this paper integrates adaptive neighborhood and hybrid mutation strategies into whale optimization algorithms,designs the average distance from itself to other whales as an adaptive neighborhood radius,and chooses to learn from the optimal solution in the neighborhood instead of random learning *** hybrid mutation strategy is used to enhance the ability of algorithm to jump out of the local optimal solution.A new whale optimization algorithm(HMNWOA)is *** proposed algorithm inherits the global search capability of the original algorithm,enhances the exploitation ability,improves the quality of the population,and thus improves the convergence speed of the algorithm.A feature selection algorithm based on binary HMNWOA is *** standard datasets from UCI repository test the validity of the proposed algorithm for feature *** experimental results show that HMNWOA is very competitive compared to the other six popular feature selection methods in improving the classification accuracy and reducing the number of features,and ensures that HMNWOA has strong search ability in the search feature space.

关键词： whale optimization algorithm Filter and Wrapper model K-nearest neighbor method Adaptive neighborhood hybrid mutation

来源：评论

学校读者我要写书评

暂无评论

Nature-inspired approach: An enhanced whale optimization algorithm for global optimization

引用

MATHEMATICS AND COMPUTERS IN SIMULATION 2021年 185卷 17-46页

作者： Yan, Zheping Zhang, Jinzhong Zeng, Jia Tang, Jialing Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

The whale optimization algorithm is based on the bubble-net attacking behavior of humpback whales and simulates encircling prey, bubble-net attacking and searching for prey to obtain the global optimal solution. However, the basic whale optimization algorithm has the disadvantage of search stagnation, easily falls into a local optimum, has slow convergence speed and has low calculation accuracy. The Levy flight strategy is beneficial for expanding the search range and prevents the algorithm from falling into a local optimum, which enhances the global search ability. The ranking-based mutation operator can increase the selection probability and accelerate the convergence speed to enhance the local search ability. To overcome these shortcomings and avoid premature convergence, the Levy flight strategy and the ranking-based mutation operator are added to the whale optimization algorithm. In this paper, an enhanced whale optimization algorithm is proposed, which realizes complementary advantages to balance exploration and exploitation. Eighteen benchmark test functions and five structural engineering design problems are used to verify the robustness and overall optimization performance of the enhanced whale optimization algorithm. The experimental results show that the enhanced whale optimization algorithm is an effective and feasible method that has a fast convergence speed, high calculation accuracy, strong robustness and stability (C) 2020 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： whale optimization algorithm Levy flight strategy Ranking-based mutation operator Benchmark test functions Structural engineering design

来源：评论

学校读者我要写书评

暂无评论

Optimizing connection weights in neural networks using the whale optimization algorithm

引用

SOFT COMPUTING 2018年第1期22卷 1-15页

作者： Aljarah, Ibrahim Faris, Hossam Mirjalili, Seyedali Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia

The learning process of artificial neural networks is considered as one of the most difficult challenges in machine learning and has attracted many researchers recently. The main difficulty of training a neural network is the nonlinear nature and the unknown best set of main controlling parameters (weights and biases). The main disadvantages of the conventional training algorithms are local optima stagnation and slow convergence speed. This makes stochastic optimization algorithm reliable alternative to alleviate these drawbacks. This work proposes a new training algorithm based on the recently proposed whale optimization algorithm (WOA). It has been proved that this algorithm is able to solve a wide range of optimization problems and outperform the current algorithms. This motivated our attempts to benchmark its performance in training feedforward neural networks. For the first time in the literature, a set of 20 datasets with different levels of difficulty are chosen to test the proposed WOA-based trainer. The results are verified by comparisons with back-propagation algorithm and six evolutionary techniques. The qualitative and quantitative results prove that the proposed trainer is able to outperform the current algorithms on the majority of datasets in terms of both local optima avoidance and convergence speed.

关键词： optimization whale optimization algorithm WOA Multilayer perceptron MLP Training neural network Evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Transformer fault diagnosis method based on improved whale optimization algorithm to optimize support vector machine

引用

ENERGY REPORTS 2021年 7卷 856-866页

作者： Fan, Qingchuan Yu, Fei Xuan, Min Naval Univ Engn Sch Elect Engn Wuhan 430033 Peoples R China

An improved whale algorithm optimized support vector machine (SVM) for fault diagnosis of oil-immersed transformers is proposed. Firstly, based on the dissolved gas analysis (DGA) in oil, with no coding ratio method is utilized to extract oil can express the state of the transformer in 9 dimensional vector, will be the vector normalization and USES kernel principal component analysis (KPCA) for feature extraction, and reduce the dimension of feature vector, to avoid the overlapping between the information at the same time to speed up the speed of 5 s;Secondly, the selection, crossover and mutation operations of genetic algorithm (GA) were introduced into whale optimization algorithm (WOA), and the penalty factor and kernel parameters of SVM were further iterated. Finally, the DGA data of 627 cases of oil-immersed transformers were collected and input into GWOA-SVM, GA-SVM, PSO-SVM and WOA-SVM models, and the diagnosis results were 94.05%, 78.56%, 82.28% and 86.37%, respectively. At the same time, the stability test experiment was carried out with 10% fault data, and the diagnostic accuracy of GWOA-SVM model only decreased by about 5%. The results show that the model established in this paper has the characteristics of high diagnostic accuracy and strong stability. (C) 2021 The Author(s). Published by Elsevier

关键词： Dissolved gas analysis Electric transformer Fault diagnosis Support vector machine whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Multiobjective whale optimization algorithm-based feature selection for intelligent systems

引用

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第11期37卷 9037-9054页

作者： Riyahi, Milad Rafsanjani, Marjan K. Gupta, Brij B. Alhalabi, Wadee Shahid Bahonar Univ Kerman Fac Math & Comp Dept Comp Sci Kerman Iran Asia Univ Dept Comp Sci & Informat Engn Taichung 413 Taiwan Lebanese Amer Univ Beirut Lebanon UPES Ctr Interdisciplinary Res Dehra Dun Uttarakhand India Skyline Univ Coll Res & Innovat Dept Sharjah U Arab Emirates Univ Miami Dept Elect & Comp Engn Coral Gables FL 33124 USA

With regard to large dimensions of contemporary data sets and restricted computational time of intelligent systems, reducing the dimensions of data sets is necessary. Feature selection is a practical way to remove a set of redundant, irrelevant, and noisy features. In this way, the speed of decision-making procedure will be increased while the accuracy of decisions will be retained. To this end, numerous attentions have been attracted to the topic and consequently, extensive range of methods has been proposed. Regarding the goals of the feature selection concept, the proposed algorithms in this field must be fast and accurate. Therefore, this paper proposes a light meanwhile accurate algorithm to fulfill the mentioned goals. The presented algorithm takes the speed advantage of whale optimization algorithm (WOA) to propose a novel feature selection method for intelligent systems. Moreover, to reach the goal of accuracy, the proposed strategy considers three important fitness objectives, namely, the number of selected features, the accuracy of classification, and information gain. The proposed scheme considers an accurate multiobjective fitness function instead of manipulating the basic algorithm. The reason is that improving the basic algorithms, WOA in our case, may lead to loading more computational complexity. Also, to make the proposed algorithm as light as possible, this paper considers K-nearest neighbor algorithm as the main classifier. The proposed light feature selection algorithm is run on different data sets. Experimental results prove that this algorithm is able to reduce the number of features meanwhile it retains, and in some cases even increases, the accuracy of classification.

关键词： feature selection information gain intelligent systems K-nearest neighbor whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Energy-aware disjoint dominating sets-based whale optimization algorithm for data collection in WSNs

引用

JOURNAL OF SUPERCOMPUTING 2023年第4期79卷 4318-4350页

作者： Elsway, Ahmed A. Khedr, Ahmed M. Alfawaz, Oruba Osamy, Walid Benha Univ Fac Comp & Artificial Intelligence Comp Sci Dept Banha Egypt Univ Sharjah Comp Sci Dept Sharjah 27272 U Arab Emirates Zagazig Univ Fac Sci Zagazig Egypt Univ Sharjah Res Inst Sci & Engn Sharjah U Arab Emirates

One of the major challenges in realizing a reliable wireless sensor network (WSN) that can survive under the emerging applications is the constrained energy of the sensors. Hence, extending the lifetime of WSN is a major concern, which directly impacts the performance of various WSN-based applications. In this regard, various methods have been developed that either investigate the energy consumption or lifetime enhancement of WSN. A promising method to conserve the energy of the sensors is to use sleep-awake scheduling by choosing disjoint groups of nodes called dominating set (DS). By distributing the data collection duties among these DSs, one DS handles these tasks for a specified period of time before being replaced by another group, extending the lifespan of the network. This problem becomes challenging in WSN with heterogeneous energy. Despite the success of the algorithms in determining the DS, none of the existing methods consider the node's energy while creation or selection of DS. This motivates us to utilize the DSs concept to control and maintain sleep/awake schedule of WSN nodes with heterogeneous energy. Toward this goal, we propose an energy-aware algorithm known as proposed initializer for whale optimization algorithm-based operator (PI-WOA-BO) to construct disjoint DSs that work as collector nodes for data gathering in each round and extend the total WSN lifetime. An energy-aware fitness function is introduced for selecting the best DSs that can maximize the WSN lifetime. Simulation results reveal that PI-WOA-BO exhibits enhanced performance over baseline techniques under various metrics including energy, stability, reliability and lifetime of WSN. PI-WOA-BO outperforms FUZZY-DS-ACO, CDS-FOR, BEE-VBC and CDS-LEACH by (17.4%, 40.1%, 31.1% and 53.6%), (7.7%, 33.5%, 23.4% and 48.5%) and (7.9%, 33.5%, 22.9% and 47.8%) in terms of First, Half and Last node dies, respectively.

关键词： whale optimization algorithm Dominating set Energy consumption Network lifetime Heterogeneous wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Optimally Designed PID Controller for a DC-DC Buck Converter via a Hybrid whale optimization algorithm with Simulated Annealing

ELECTRICA

引用

ELECTRICA 2020年第1期20卷 19-27页

作者： Hekimoglu, Baran Ekinci, Serdar Batman Univ Dept Elect & Elect Engn Fac Engn & Architecture Batman Turkey Batman Univ Dept Comp Engn Fac Engn & Architecture Batman Turkey

A new design approach Is presented in this study for tuning of proportional-integral-derivative (PID) controller parameters in a DC-DC buck converter utilizing a hybrid whale optimization algorithm (WOA) with simulated annealing (SA), namely the WOASAT algorithm, which uses a tournament selection mechanism. The proposed algorithm's efficacy ensures that the optimum PID controller parameters are tuned quickly and that the quality of tuning is high. A time domain performance index is utilized to validate the proposed WOASAT based PID controller's performance. In addition, from the comparative results of statistical analysis, frequency response analysis, transient response analysis, disturbance rejection analysis, and performance indices analysis, the proposed WOASAT-PID controller was found to be more efficient than the SAPID controller and WOA-PID controller in enhancing the buck converter's transient response.

关键词： Hybridization whale optimization algorithm simulated annealing tournament selection PID controller parameter tuning buck converter output voltage regulation

来源：评论

学校读者我要写书评

暂无评论

Energy-aware whale optimization algorithm for real-time task scheduling in multiprocessor systems

引用

APPLIED SOFT COMPUTING 2020年 93卷 106349-106349页

作者： Abdel-Basset, Mohamed El-Shahat, Doaa Deb, Kalyanmoy Abouhawwash, Mohamed Zagazig Univ Fac Comp & Informat Zagazig 44519 Egypt Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt

The growth of Multiprocessing Systems (MPS) has become a necessity for dealing with complex tasks and speeding up their execution. Increasing the number of processing cores on a single chip produces a vast processing power, but the biggest obstacle is the energy generated from these cores. The traditional techniques guarantee to get the optimal schedule, but they are costly in terms of time and memory storage. In this paper, we propose an Improved whale algorithm (IWA) to allocate the dependent tasks in MPS with two objectives minimizing the energy consumption and the makespan. The processing cores are assumed to support Dynamic Voltage and Frequency Scaling (DVFS) as an effective technique to reduce energy. The allocation of tasks in MPS is an NP-hard problem. Inadequate scheduling of tasks can result in consuming energy. Also, the failure to complete the tasks before their predetermined deadlines is a critical issue for real-time applications. We consider three different sources of energy coming from the communication, idle, and active states of the processing cores. We use an initialization procedure to produce a population of candidate schedules that respects the precedence among tasks. In IWA, we employ two different discretization methods to map the continuous values into discrete ones. Two specialized crossover operations are adopted to boost the quality of the candidate schedules while respecting the dependencies among the tasks. IWA implements the Load Balancing Improvement (LBI) strategy to alleviate the load of tasks from heavy cores. LBI plays a vital role in decreasing the static energy consumption. We compare IWA with the other algorithms, and the results show the superiority of IWA. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multiprocessor systems Task scheduling Energy DVFS whale 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：