检索结果-内蒙古大学图书馆

Time-optimal memetic whale optimization algorithm for hypersonic vehicle reentry trajectory optimization with no-fly zones

引用

NEURAL COMPUTING & APPLICATIONS 2020年第7期32卷 2735-2749页

作者： Zhang, Huiping Wang, Honglun Li, Na Yu, Yue Su, Zikang Liu, Yiheng Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Aerosp Automat Control Inst Beijing 100854 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beihang Univ Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China Beihang Univ Unmanned Syst Res Inst Beijing 100191 Peoples R China

A novel time-optimal memetic whale optimization algorithm (WOA) integrating the Gauss pseudo-spectral methods (GPM), is proposed in this paper for the hypersonic vehicle entry trajectory optimization problem with no-fly zones. The WOA is featured with the strong global search ability and non-sensitive to the initial values, but also shows poor searching convergence speed around the global optimum. Conversely, GPM may be sensitive to the initial solution and easily trapped in a local optimum, but it also possesses more rapid convergence speed around the optimum and higher searching accuracy. Thus, a memetic optimization algorithm which contains a two-stage approach mechanism is proposed for searching the global optimum. The first searching stage, which is driven by an improved WOA (IWOA), works as an initializer of the entire searching due to its strong global search ability and non-sensitive to the initial values. The local optimum reservation and adaptive amplitude factor updating strategy are established to improve the convergent speed and the global search ability of the WOA. Once the changing of fitness value satisfies the predefined criterion, the next searching stage driven by GPM will take the place of the IWOA to expedite the search process around optimum and to obtain a precise global optimal solution. By this hybrid way, the proposed optimization algorithm may find an optimum more quickly and accurately. Simulation results show the proposed algorithm possesses faster convergence speed, higher accuracy, and stronger robustness for the hypersonic vehicle entry trajectory optimization.

关键词： Trajectory optimization Hypersonic vehicle whale optimization algorithm Gauss pseudo-spectral method

来源：评论

学校读者我要写书评

暂无评论

Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach

引用

NEURAL COMPUTING & APPLICATIONS 2021年第23期33卷 16229-16250页

作者： Too, Jingwei Mafarja, Majdi Mirjalili, Seyedali Univ Teknikal Malaysia Melaka Fac Elect Engn Durian Tunggal 76100 Melaka Malaysia Birzeit Univ Fac Engn & Technol Dept Comp Sci Birzeit Palestine Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea King Abdulaziz Univ Jeddah Saudi Arabia

Selecting a subset of candidate features is one of the important steps in the data mining process. The ultimate goal of feature selection is to select an optimal number of high-quality features that can maximize the performance of the learning algorithm. However, this problem becomes challenging when the number of features increases in a dataset. Hence, advanced optimization techniques are used these days to search for the optimal feature combinations. whale optimization algorithm (WOA) is a recent metaheuristic that has successfully applied to different optimization problems. In this work, we propose a new variant of WOA (SBWOA) based on spatial bounding strategy to play the role of finding the potential features from the high-dimensional feature space. Also, a simplified version of SBWOA is introduced in an attempt to maintain a low computational complexity. The effectiveness of the proposed approach was validated on 16 high-dimensional datasets gathered from Arizona State University, and the results are compared with the other eight state-of-the-art feature selection methods. Among the competitors, SBWOA has achieved the highest accuracy for most datasets such as TOX_171, Colon, and Prostate_GE. The results obtained demonstrate the supremacy of the proposed approaches over the comparison methods.

关键词： whale optimization algorithm Feature selection Data mining Classification High Dimensional Data optimization Benchmark WOA Swarm intelligence Evolutionary

来源：评论

学校读者我要写书评

暂无评论

MCWOA Scheduler:Modified Chimp-whale optimization algorithm for Task Scheduling in Cloud Computing

引用

Computers, Materials & Continua 2024年第2期78卷 2593-2616页

作者： Chirag Chandrashekar Pradeep Krishnadoss Vijayakumar Kedalu Poornachary Balasundaram Ananthakrishnan School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Center for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of TechnologyChennai600127India

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as *** has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud ***,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing *** proposed Modified Chimp-whale optimization algorithm called Modified Chimp-whale optimization algorithm(MCWOA),combines elements of the Chimp optimization algorithm(COA)and the whale optimization algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution ***,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating *** study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam *** outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection *** excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage *** efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and *** simulated data indicates that the new MCWOA outpaces other methods across all *** study also references the whale optimization algorithm(WOA),Chimp algorithm(CA),Ant Lion Optimizer(ALO),Genetic algorithm(GA)and Grey Wolf Optimizer(GWO).

关键词： Cloud computing scheduling chimp optimization algorithm whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Q-learning whale optimization algorithm for test suite generation with constraints support

引用

NEURAL COMPUTING & APPLICATIONS 2023年第34期35卷 24069-24090页

作者： Hassan, Ali Abdullah Abdullah, Salwani Zamli, Kamal Z. Razali, Rozilawati Univ Kebangsaan Malaysia Fac Informat Sci & Technol Bangi 43600 Selangor Malaysia Univ Malaysia Pahang Al Sultan Abdullah Fac Comp Pekan 26600 Pahang Malaysia Univ Airlangga Fac Sci & Technol Campus JI Dr H Soekamo C Surabaya 60115 Indonesia

This paper introduces a new variant of a metaheuristic algorithm based on the whale optimization algorithm (WOA), the Q-learning algorithm and the Exponential Monte Carlo Acceptance Probability called (QWOA-EMC). Unlike WOA, QWOA-EMC permits just-in-time adaptive selection of its operators (i.e., between shrinking mechanism, spiral shape mechanism, and random generation) based on their historical performances as well as exploits the Monte Carlo Acceptance probability to further strengthen its exploration capabilities by allowing a poor performing operator to be reselected with probability in the early part of the iteration. Experimental results for constraints combinatorial test generation demonstrate that the proposed QWOA-EMC outperforms WOA and performs competitively against other metaheuristic algorithms.

关键词： Combinatorial testing Constrained software testing Meta-heuristic Test case generation whale optimization algorithm Reinforcement learning Q-learning algorithm

来源：评论

学校读者我要写书评

暂无评论

New binary whale optimization algorithm for discrete optimization problems

引用

ENGINEERING optimization 2020年第6期52卷 945-959页

作者： Hussien, Abdelazim G. Hassanien, Aboul Ella Houssein, Essam H. Amin, Mohamed Azar, Ahmad Taher Fayoum Univ Fac Sci Faiyum Egypt Cairo Univ Fac Comp & Artificial Intelligence Cairo Egypt Sci Res Grp Egypt SRGE Cairo Cairo Egypt Minia Univ Fac Comp & Informat Al Minya Egypt Menoufia Univ Fac Sci Menoufia Egypt Prince Sultan Univ Coll Engn Riyadh Saudi Arabia Benha Univ Fac Comp & Artificial Intelligence Banha Egypt

The whale optimization algorithm (WOA) is an intelligence-based technique that simulates the hunting behaviour of humpback whales in nature. In this article, an adaptation of the original version of the WOA is made for handling binary optimization problems. For this purpose, two transfer functions (S-shaped and V-shaped) are presented to map a continuous search space to a binary one. To illustrate the functionality and performance of the proposed binary whale optimization algorithm (bWOA), its results when applied on twenty-two benchmark functions, three engineering optimization problems and a real-world travelling salesman problem are found. Furthermore, the proposed bWOA is compared with five well-known metaheuristic algorithms. The experimental results show its superiority in comparison with other state-of-the-art metaheuristics in terms of accuracy and speed. Finally, Wilcoxon's rank-sum non-parametric statistical test is carried out at the 5% significance level to judge whether the results of the proposed algorithm differ from those of the other comparison algorithms in a statistically significant way.

关键词： whale optimization algorithm binary whale optimization algorithm optimization problems discrete search space metaheuristic algorithms

来源：评论

学校读者我要写书评

暂无评论

Automated Pavement Crack Detection Using Deep Feature Selection and whale optimization algorithm

引用

Computers, Materials & Continua 2023年第10期77卷 63-77页

作者： Shorouq Alshawabkeh Li Wu Daojun Dong Yao Cheng Liping Li Mohammad Alanaqreh Faculty of Engineering China University of GeosciencesWuhan430074China

Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance *** advancements in deep learning(DL)techniques have shown promising results in detecting pavement cracks;however,the selection of relevant features for classification remains *** this study,we propose a new approach for pavement crack detection that integrates deep learning for feature extraction,the whale optimization algorithm(WOA)for feature selection,and random forest(RF)for *** performance of the models was evaluated using accuracy,recall,precision,F1 score,and area under the receiver operating characteristic curve(AUC).Our findings reveal that Model 2,which incorporates RF into the ResNet-18 architecture,outperforms baseline Model 1 across all evaluation ***,our proposed model,which combines ResNet-18 with both WOA and RF,achieves significantly higher accuracy,recall,precision,and F1 score compared to the other two *** results underscore the effectiveness of integrating RF and WOA into ResNet-18 for pavement crack detection *** applied the proposed approach to a dataset of pavement images,achieving an accuracy of 97.16%and an AUC of *** results demonstrate that the proposed approach surpasses existing methods for pavement crack detection,offering a promising solution for the automatic identification of pavement *** leveraging this approach,potential safety hazards can be identified more effectively,enabling timely repairs and maintenance ***,the findings of this study also emphasize the potential of integrating RF and WOA with deep learning for pavement crack detection,providing road authorities with the necessary tools to make informed decisions regarding road infrastructure maintenance.

关键词： Pavement crack detection deep learning feature selection whale optimization algorithm civil engineering

来源：评论

学校读者我要写书评

暂无评论

A novel community detection method based on whale optimization algorithm with evolutionary population

引用

APPLIED INTELLIGENCE 2020年第8期50卷 2503-2522页

作者： Feng, Yunfei Chen, Hongmei Li, Tianrui Luo, Chuan Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China

Community detection is the process of detecting communities in complex networks. Communities are important structures that can help us further study the properties of complex networks. In recent years, swarm intelligence algorithms have been applied to community detection and have achieved remarkable results. However, these existing algorithms have limited search ability and easily fall into the problem of local optima. In this paper, we propose a new community detection approach based on an improved whale optimization algorithm (WOA). The WOA is applied to a discrete symbol space in solving the community detection problem, therefore topology structure-based search strategies, adjustment and mergence policies, and evolutionary population method are designed to improve the efficiency and effectiveness of the method. Then, a whale optimization algorithm with evolutionary population for community detection (EP-WOCD) is proposed. Extensive experiments are conducted to compare the EP-WOCD with other state-of-the-art algorithms on both artificial and real-world social networks. Experimental results show that the EP-WOCD is effective and stable.

关键词： Complex network Community detection whale optimization algorithm Evolutionary population

来源：评论

学校读者我要写书评

暂无评论

A novel approach in exergy optimization of a WWER1000 nuclear power plant using whale optimization algorithm

引用

ANNALS OF NUCLEAR ENERGY 2020年 145卷 107540-107540页

作者： Ebrahimgol, H. Aghaie, M. Zolfaghari, A. Naserbegi, A. Shahid Beheshti Univ Engn Dept GC POB *** Tehran Iran

Nowadays, the scarcity of energy resources and the intensive demand of energy leads the researchers and scientists to find the efficient ways to save the energy and decrease the exergy loss. In this study, a new exergy optimization framework for a typical WWER1000 nuclear power plant (Bushehr NPP) is suggested. It is aimed to optimize the total efficiency of the power plant by decrease the exergy destruction using the first and second laws of thermodynamics. First, the main parameters of Bushehr NPP (BNPP) stream lines and exergy destruction of components are determined and the accuracy of the model is assessed. According to the analysis result, the reactor pressure vessel is the most inefficient equipment in the NPP and the steam generators are the next. Then, the exergy optimization of the steam cycle is evaluated using WOA (whale optimization algorithm). To investigate the performance of the WOA, four general fitness functions are determined and compared with GA and PSO and in next exergy optimization is compared. The total exergy destruction for Bushehr power plant is decreased from 1968.9MWth to 1914.7MWth and the overall thermal efficiency of the BNPP increases from 33.66% to 36.42% using WOA. The results demonstrate that the new algorithm WOA has a proper efficiency to implement in this optimization problem. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： WWER1000 optimization whale optimization algorithm Exergy destruction

来源：评论

学校读者我要写书评

暂无评论

Cosine adapted modified whale optimization algorithm for control of switched reluctance motor

引用

COMPUTATIONAL INTELLIGENCE 2022年第3期38卷 978-1017页

作者： Saha, Nutan Panda, Sidhartha VSSUT Dept Elect Engn Burla 768018 Odisha India

whale optimization algorithm (WOA) imitates social conduct of humpback whales which is inspired by bubble net hunting strategy of humpback whales. In the present study, Cosine adapted modified whale optimization algorithm (CamWOA) which is a modified version of WOA, has been proposed where cosine function is incorporated for the selection of control parameter "d" which governs the position of whales during optimization process. Also, correction factors are employed to modify the movement of search agents during the search process. These changes provide a proper balance between exploration and exploitation phases in CamWOA technique. The performance of CamWOA is analyzed by testing on a set of benchmark functions and compared with other state-of-the-art algorithms. It is observed that CamWOA outperforms other state-of-the-art metaheuristic algorithms in majority of benchmark functions. The efficiency of CamWOA is also evaluated by solving a multiobjective engineering problem pertaining to control of switched reluctance motor. The simulation results confirm that CamWOA yields very promising and competitive results compared to that of WOA and other metaheuristic optimization algorithms.

关键词： cosine adapted modified whale optimization algorithm proportional integral controller switch reluctance motor torque ripple whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2020年第43-44期79卷 32169-32194页

作者： Rahnema, Nouria Gharehchopogh, Farhad Soleimanian Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran

Data clustering is one of the branches of unsupervised learning and it is a process whereby the samples are divided into categories whose members are similar to each other. The K-means algorithm is a simple and fast clustering technique, but it has many initial problems, for example, it depends heavily on the initial value for better clustering. Moreover, it is susceptible to outliers and unbalanced clusters. The artificial bee colony (ABC) algorithm is one of the meta-heuristic algorithms that is used nowadays to solve many optimization problems including clustering and the fundamental problem of this algorithm is exploration and late convergence. In this paper, to solve the problem of exploration and late convergence in ABC are used Random Memory (RM) and Elite Memory (EM) called ABCWOA algorithm. RM in the ABCWOA algorithm has used the search stage for the bait in the whale optimization algorithm (WOA) and EM is also used to increase convergence. In addition, we control the use of EM dynamically. Finally, the proposed method was implemented on ten standard datasets from the UCI Machine Learning Database for evaluation. Moreover, it was compared in terms of statistical criteria and analysis of variance (ANOVA) test with basic ABC and WOA, vortex search (VS) algorithm, butterfly optimization algorithm (BOA), crow search (CS) algorithm, and cuckoo search algorithm (CSA). The simulation results showed that the degree of convergence maintained its performance by increasing the number of repetitions of the proposed method, but the ABC algorithm has shown poor performance by increasing the repetition of performance. ANOVA results also confirmed that the ABCWOA algorithm has a positive effect on the population and it contains less noise than other comparative algorithms. The ABCWOA algorithm show that the ABCWOA algorithm performs better than other meta-heuristic algorithms.

关键词： Artificial bee colony algorithm whale optimization algorithm Data clustering optimization

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：