检索结果-内蒙古大学图书馆

A new binary coati optimization algorithm for binary optimization problems

NEURAL COMPUTING & APPLICATIONS 2024年第6期36卷 2797-2834页

作者： Yildizdan, Gulnur Bas, Emine Selcuk Univ Kulu Vocat Sch Konya Turkiye Konya Tech Univ Engn & Nat Sci Fac Dept Environm Engn TR-42075 Konya Turkiye

The coati optimization algorithm (COA) is a recently proposed heuristic algorithm. The COA algorithm, which solved the continuous optimization problems in its original paper, has been converted to a binary optimization solution by using transfer functions in this paper. Thus, binary COA (BinCOA) is proposed for the first time in this study. In this study, twenty transfer functions are used (four S-shaped, four V-shaped, four Z-shaped, four U-shaped, and four taper-shaped transfer functions). Thus, twenty variations of BinCOA are obtained, and the effect of each transfer function on BinCOA is examined in detail. The knapsack problem (KP) and uncapacitated facility location problem (UFLP), which are popular binary optimization problems in the literature, are chosen to test the success of BinCOA. In this study, small-, middle-, and large-scale KP and UFLP datasets are selected. Real-world problems are not always low-dimensional. Although a binary algorithm sometimes shows superior success in low dimensions, it cannot maintain the same success in large dimensions. Therefore, the success of BinCOA has been tested and demonstrated not only in low-dimensional binary optimization problems, but also in large-scale optimization problems. The most successful transfer function is T3 for KPs and T20 for UFLPs. This showed that S-shaped and taper-shaped transfer functions obtained better results than others. After determining the most successful transfer function for each problem, the enhanced BinCOA (EBinCOA) is proposed to increase the success of BinCOA. Two methods are used in the development of BinCOA. These are the repair method and the XOR gate method. The repair method repairs unsuitable solutions in the population in a way that competes with other solutions. The XOR gate is one of the most preferred methods in the literature when producing binary solutions and supports diversity. In tests, EBinCOA has achieved better results than BinCOA. The added methods have proven succ

关键词： coati optimization algorithm Transfer functions Knapsack problems UFL problems

来源：评论

学校读者我要写书评

暂无评论

An efficient adaptive-mutated coati optimization algorithm for feature selection and global optimization

引用

ALEXANDRIA ENGINEERING JOURNAL 2023年 85卷 29-48页

作者： Hashim, Fatma A. Houssein, Essam H. Mostafa, Reham R. Hussien, Abdelazim G. Helmy, Fatma Helwan Univ Fac Engn Helwan Egypt Middle East Univ MEU Res Unit Amman 11831 Jordan Minia Univ Fac Comp & Informat Al Minya Egypt Univ Sharjah Res Inst Sci & Engn RISE Sharjah U Arab Emirates Mansoura Univ Fac Comp & Informat Sci Informat Syst Dept Mansoura 35516 Egypt Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Fayoum Univ Fac Sci Faiyum Egypt Misr Int Univ Fac Comp Sci Cairo Egypt

The feature selection (FS) problem has occupied a great interest of scientists lately since the highly dimensional datasets might have many redundant and irrelevant features. FS aims to eliminate such features and select the most important ones that affect classification performance. Metaheuristic algorithms are the best choice to solve this combinatorial problem. Recent researchers invented and adapted new algorithms, hybridized many algorithms, or enhanced existing ones by adding some operators to solve the FS problem. In our paper, we added some operators to the coati optimization algorithm (coatiOA). The first operator is the adaptive s-best mutation operator to enhance the balance between exploration and exploitation. The second operator is the directional mutation rule that opens the way to discover the search space thoroughly. The final enhancement is controlling the search direction toward the global best. We tested the proposed mcoatiOA algorithm in solving) in solving challenging problems from the CEC'20 test suite. mcoatiOA performance was compared with Dandelion Optimizer (DO), African vultures optimization algorithm (AVOA), Artificial gorilla troops optimizer (GTO), whale optimization algorithm (WOA), Fick's Law algorithm (FLA), Particle swarm optimization (PSO), Harris hawks optimization (HHO), and Tunicate swarm algorithm (TSA). According to the average fitness, it can be observed that the proposed method, mcoatiOA, performs better than the other optimization algorithms on 8 test functions. It has lower average standard deviation values compared to the competitive algorithms. Wilcoxon test showed that the results obtained by mcoatiOA are significantly different from those of the other rival algorithms. mcoatiOA has been tested as a feature selection algorithm. Fifteen benchmark datasets of various types were collected from the UCI machine-learning repository. Different evaluation criteria are used to determine the effectiveness of the proposed method.

关键词： Feature selection coati optimization algorithm optimization problems

来源：评论

学校读者我要写书评

暂无评论

Improve coati optimization algorithm for solving constrained engineering optimization problems

引用

JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第6期10卷 2223-2250页

作者： Jia, Heming Shi, Shengzhao Wu, Di Rao, Honghua Zhang, Jinrui Abualigah, Laith Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Sanming Univ Sch Educ & Mus Sanming 365004 Peoples R China Al Al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Sunway Univ Malaysia Sch Engn & Technol Petaling Jaya 27500 Malaysia

The coati optimization algorithm (COA) is a meta-heuristic optimization algorithm proposed in 2022. It creates mathematical models according to the habits and social behaviors of coatis: (i) In the group organization of the coatis, half of the coatis climb trees to chase their prey away, while the other half wait beneath to catch it and (ii) coatis avoidance predators behavior, which gives the algorithm strong global exploration ability. However, over the course of our experiment, we uncovered opportunities for enhancing the algorithm's performance. When confronted with intricate optimization problems, certain limitations surfaced. Much like a long-nosed raccoon gradually narrowing its search range as it approaches the optimal solution, COA algorithm exhibited tendencies that could result in reduced convergence speed and the risk of becoming trapped in local optima. In this paper, we propose an improved coati optimization algorithm (ICOA) to enhance the algorithm's efficiency. Through a sound-based search envelopment strategy, coatis can capture prey more quickly and accurately, allowing the algorithm to converge more rapidly. By employing a physical exertion strategy, coatis can have a greater variety of escape options when being chased, thereby enhancing the algorithm's exploratory capabilities and the ability to escape local optima. Finally, the lens opposition-based learning strategy is added to improve the algorithm's global performance. To validate the performance of the ICOA, we conducted tests using the IEEE CEC2014 and IEEE CEC2017 benchmark functions, as well as six engineering problems. Graphical Abstract

关键词： coati optimization algorithm search and encirclement strategy according to sound transmission physical exertion strategy lens opposition-based learning strategy engineering problem

来源：评论

学校读者我要写书评

暂无评论

Enhancing intrusion detection using coati optimization algorithm with deep learning on vehicular Adhoc networks

引用

International Journal of Information Technology (Singapore) 2024年第5期16卷 3009-3018页

作者： Sarathkumar, K. Sudhakar, P. Kanmani, A. Clara Department of Computer Science and Engineering Annamalai University Chidambaram India Computer Science and Engineering PES University Bangalore India

Vehicular ad hoc networks (VANETs) role a vital play in allowing technology for future cooperative intelligent transportation systems (CITSs). Vehicles in VANETs transmit real-time data on their movement, traffic, and road conditions. Nevertheless, VANETs can vulnerable to cyberattacks, which causes serious conditions or road congestion. Intrusion detection systems (IDSs) depends on the cooperation among vehicles to identify intruders are mostly proposed security solutions for VANETs. Evolving a more efficient and effective IDS for VANETs is vital because of these networks’ unique problems and vulnerabilities. This article develops an enhanced intrusion detection using coati optimization algorithm with Deep Learning (ID-COADL) technique on VANET. The major aim of the ID-COADL technique is to categorize various kinds of intrusions that exist in the VANET to accomplish security using hyperparameter tuned deep learning (DL) model. At the primary stage, the ID-COADL technique employs min–max normalization to scale the input data into uniform data. For intrusion detection, the ID-COADL technique applies deep belief network (DBN) model. Finally, the hyperparameter selection of the DBN model can be performed by the use of COA thereby enhancing the intrusion detection rate. To examine the performance of the ID-COADL system, a wide-ranging simulations were carried out. The extensive result analysis highlighted the enhanced performance of the ID-COADL technique in the detection and classification of intrusions. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： coati optimization algorithm Deep learning IDS Intelligent transportation system VANET

来源：评论

学校读者我要写书评

暂无评论

High voltage shunt reactor acoustic signal denoising based on the combination of VMD parameters optimized by coati optimization algorithm and wavelet threshold

引用

MEASUREMENT 2024年 224卷

作者： Lei, Wu Wang, Guo Wan, Baoquan Min, Yongzhi Wu, Jianming Li, Baopeng Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou 730070 Peoples R China China Elect Power Res Inst State Key Lab Power Grid Environm Protect Wuhan 430074 Peoples R China

The goal of this research is the application of variational mode decomposition (VMD) in the denoising process of acoustic signals from high-voltage shunt reactors. This paper propose a unique joint denoising method that combines improved VMD and wavelet threshold. The coati optimization algorithm (COA) was introduced to optimally find the optimal combination of VMD decomposition parameters. The optimized and improved VMD was then used to decompose the original noisy reactor acoustic signal. The correlation coefficient method was introduced to remove uncorrelated intrinsic mode functions (IMFs). The wavelet threshold denoising algorithm was applied for secondary noise reduction on the extracted IMFs. The denoised IMFs were subsequently superimposed and reconstructed to obtain the denoised acoustic signal. Comprehensive comparison and analysis of these methods were conducted through simulation experiments and practical tests. The results indicate that COA-VMD-Wavelet Threshold-significantly reduces the reconstructed signal errors with high practicality and effectiveness.

关键词： High -voltage shunt reactor Acoustic signal denoising coati optimization algorithm Variational mode decomposition Wavelet threshold

来源：评论

学校读者我要写书评

暂无评论

Dynamic coati optimization algorithm for Biomedical Classification Tasks

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期164卷 107237-107237页

作者： Houssein, Essam H. Samee, Nagwan Abdel Mahmoud, Noha F. Hussain, Kashif Minia Univ Fac Comp & Informat Al Minya Egypt Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Hlth & Rehabil Sci Coll Rehabil Sci Dept POB 84428 Riyadh 11671 Saudi Arabia Solent Univ Dept Sci & Engn East Pk Terrace Southampton SO14 0YN England

Medical datasets are primarily made up of numerous pointless and redundant elements in a collection of patient records. None of these characteristics are necessary for a medical decision-making process. Conversely, a large amount of data leads to increased dimensionality and decreased classifier performance in terms of machine learning. Numerous approaches have recently been put out to address this issue, and the results indicate that feature selection can be a successful remedy. To meet the various needs of input patterns, medical diagnostic tasks typically involve learning a suitable categorization model. The k-Nearest Neighbors algorithm (kNN) classifier's classification performance is typically decreased by the input variables' abundance of irrelevant features. To simplify the kNN classifier, essential attributes of the input variables have been searched using the feature selection approach. This paper presents the coati optimization algorithm (DCOA) in a dynamic form as a feature selection technique where each iteration of the optimization process involves the introduction of a different feature. We enhance the exploration and exploitation capability of DCOA by employing dynamic opposing candidate solutions. The most impressive feature of DCOA is that it does not require any preparatory parameter fine-tuning to the most popular metaheuristic algorithms. The CEC'22 test suite and nine medical datasets with various dimension sizes were used to evaluate the performance of the original COA and the proposed dynamic version. The statistical results were validated using the Bonferroni-Dunn test and Kendall's W test and showed the superiority of DCOA over seven well-known metaheuristic algorithms with an overall accuracy of 89.7%, a feature selection of 24%, a sensitivity of 93.35% a specificity of 96.81%, and a precision of 93.90%.

关键词： Machine learning Feature selection k-nearest neighbor Medical diagnostic coati optimization algorithm Dynamic opposite

来源：评论

学校读者我要写书评

暂无评论

Cauchy Mutation Chaotic coati optimization algorithm

引用

ENGINEERING LETTERS 2024年第6期32卷 1114-1131页

作者： Song, Yu-Wei Sun, Wei-Zhong Wang, Jie-Sheng Qi, Yu-Liang Liu, Xun Gao, Yuan-Zheng Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan 114051 Peoples R China

coati optimization algorithm (COA) is a novel heuristic algorithm that simulates the intricate behavioral repertoire of coatis manifestations during their pursuit of iguanas as weil as their evasive tactics against predators. Aiming to elevate the convergence characteristics regarding speed and precision of the original algorithmic process, and addressing the issue of susceptibility to local optima, an enhanced coati optimization algorithm is suggested based on Cauchy mutation and chaotic maps. Firstly, the Cauchy mutation is embedded in the process of coatis hunting for iguanas. Subsequently, each of ten chaotic maps was incorporated into the probing phase of the COA, generating ten unique enhanced versions. This augments the algorithm's precision in optimization, bolsters the equilibrium between exploration and exploitation and enhances its itinerancy and non-redundancy. The enhanced optimization algorithm exhibiting the aggregated optimal performance is selected from types of different enhanced COA and used for subsequent comparisons with other algorithms for function optimization and engineering optimization. Thirty benchmark functions from the CEC-BC-2022 datasets were incorporated for evaluation on performance metrics of the improved COA with Cauchy mutation and ten types of chaotic maps, and then the performance of the Chebyshev map enhanced COA and other swarm intelligent algorithms are compared for optimization purposes. Finally, optimization was performed on four distinct engineering design problems. The outcomes of the simulation experiment evidence that the advanced Cauchy mutation chaotic COA yields satisfactory outcomes in addressing both function optimization and engineering optimization. The algorithm exhibits superiority in balancing exploration and exploitation during the iterative procedure of optimization, thereby enhancing convergence precision.

关键词： coati optimization algorithm Cauchy mutation Chaotic maps Function optimization Engineering optimization

来源：评论

学校读者我要写书评

暂无评论

Transferable deep learning with coati optimization algorithm based mitotic nuclei segmentation and classification model

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-21页

作者： Alshardan, Amal Ahmad, Nazir Ben Miled, Achraf Alshuhail, Asma Alzahrani, Yazeed Mahmud, Ahmed Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Comp Sci Abha Saudi Arabia Northern Border Univ Coll Sci Dept Comp Sci Ar Ar 73213 Saudi Arabia King Faisal Univ Coll Comp Sci & Informat Technol Dept Informat Syst Al Hufuf Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawasir Dept Comp Engn Al Kharj Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11835 Egypt

Image processing and pattern recognition methods have recently been extensively implemented in histopathological images (HIs). These computer-aided techniques are aimed at detecting the attentive biological markers for assisting the final cancer grading. Mitotic count (MC) is a significant cancer detection and grading parameter. Conventionally, a pathologist examines the biopsy image physically by employing higher-power microscopy. The MC cells have been marked physically at every analysis, and total MC must be utilized as a major aspect for the cancer ranking and considered as the initiative of cancers. Numerous pattern recognition algorithms for cell-sized objects in HIs depend upon segmentation to assess features. The correct description of the segmentation has been difficult, and feature outcomes can be highly complex to the segmentation. The MC cells are an essential element in many cancer grading methods. Extraction of the MC cell from the HI is a highly challenging assignment. This manuscript proposes the coati optimization algorithm with Deep Learning-Driven Mitotic Nuclei Segmentation and Classification (COADL-MNSC) technique. The major aim of the COADL-MNSC technique is to utilize the DL model to segment and classify the mitotic nuclei (MN). In the preliminary stage, the COADL-MNSC approach implements median filtering (MF) for pre-processing. Besides, the COADL-MNSC approach utilizes the Hybrid Attention Fusion U-Net (HAU-UNet) model to segment the MN. Moreover, the capsule network (CapsNet) model is employed for the feature extraction method, and its hyperparameters are adjusted by utilizing the COA model. At last, the classification procedure is performed using the bidirectional long short-term memory (BiLSTM) model. Extensive simulations are performed under the MN image dataset to exhibit the excellent performance of the COADL-MNSC methodology. The experimental validation of the COADL-MNSC methodology portrayed a superior accuracy value of 98.89% over e

关键词： Mitotic nuclei coati optimization algorithm Median filtering Capsule network HAU-UNet

来源：评论

学校读者我要写书评

暂无评论

EACH-COA: An Energy-Aware Cluster Head Selection for the Internet of Things Using the coati optimization algorithm

引用

INFORMATION 2023年第11期14卷 601页

作者： Somula, Ramasubbareddy Cho, Yongyun Mohanta, Bhabendu Kumar Sunchon Natl Univ Dept Informat & Commun Engn Suncheon Si 57922 South Korea Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram 522302 Andhra Pradesh India

In recent years, the Internet of Things (IoT) has transformed human life by improving quality of life and revolutionizing all business sectors. The sensor nodes in IoT are interconnected to ensure data transfer to the sink node over the network. Owing to limited battery power, the energy in the nodes is conserved with the help of the clustering technique in IoT. Cluster head (CH) selection is essential for extending network lifetime and throughput in clustering. In recent years, many existing optimization algorithms have been adapted to select the optimal CH to improve energy usage in network nodes. Hence, improper CH selection approaches require more extended convergence and drain sensor batteries quickly. To solve this problem, this paper proposed a coati optimization algorithm (EACH-COA) to improve network longevity and throughput by evaluating the fitness function over the residual energy (RER) and distance constraints. The proposed EACH-COA simulation was conducted in MATLAB 2019a. The potency of the EACH-COA approach was compared with those of the energy-efficient rabbit optimization algorithm (EECHS-ARO), improved sparrow optimization technique (EECHS-ISSADE), and hybrid sea lion algorithm (PDU-SLno). The proposed EACH-COA improved the network lifetime by 8-15% and throughput by 5-10%.

关键词： Internet of Things (IoT) cluster head optimization technique network lifetime coati optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Path planning and engineering problems of 3D UAV based on adaptive coati optimization algorithm

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-53页

作者： Jia, Chuan He, Ling Liu, Dan Fu, Shengwei Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China

In response to the challenges faced by the coati optimization algorithm (COA), including imbalance between exploration and exploitation, slow convergence speed, susceptibility to local optima, and low convergence accuracy, this paper introduces an enhanced variant termed the Adaptive coati optimization algorithm (ACOA). ACOA achieves a balanced exploration-exploitation trade-off through refined exploration strategies and developmental methodologies. It integrates chaos mapping to enhance randomness and global search capabilities and incorporates a dynamic antagonistic learning approach employing random protons to mitigate premature convergence, thereby enhancing algorithmic robustness. Additionally, to prevent entrapment in local optima, ACOA introduces an Adaptive Levy Flight strategy to maintain population diversity, thereby improving convergence accuracy. Furthermore, underperforming individuals are eliminated using a cosine disturbance-based differential evolution strategy to enhance the overall quality of the population. The efficacy of ACOA is assessed across four dimensions: population diversity, exploration-exploitation balance, convergence characteristics, and diverse strategy variations. Ablation experiments further validate the effectiveness of individual strategy modules. Experimental results on CEC-2017 and CEC-2022 benchmarks, along with Wilcoxon rank-sum tests, demonstrate superior performance of ACOA compared to COA and other state-of-the-art optimization algorithms. Finally, ACOA's applicability and superiority are reaffirmed through experimentation on five real-world engineering challenges and a complex urban three-dimensional unmanned aerial vehicle (UAV) path planning problem.

关键词： coati optimization algorithm Dynamic antagonistic learning Exploration strategies and development Global search capability UAV path planning

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：