检索结果-内蒙古大学图书馆

Adaptive and blind audio watermarking algorithm based on dither modulation and butterfly optimization algorithm

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第6期20卷 11482-11501页

作者： Wu, Qiuling Huang, Dandan Wei, Jiangchun Chen, Wenhui Jinling Inst Technol Sch Cyber Secur Nanjing 211169 Jiangsu Peoples R China Jiangsu Liyuan Power Engn Co Ltd Nanjing 211100 Jiangsu Peoples R China

How to improve the robustness to resist attacks and how to adaptively match the key parameters of the watermarking algorithm with the performance requirements to achieve the best performance in different applications are two hot issues in the research of audio watermarking algorithms. An adaptive and blind audio watermarking algorithm based on dither modulation and butterfly optimization algorithm (BOA) is proposed. Based on the convolution operation, a stable feature is designed to carry the watermark, which will improve the robustness by means of the stability of this feature to prevent the watermark loss. Blind extraction will be achieved only by comparing the feature value and the quantized value without the original audio. The BOA is used to optimize the key parameters of the algorithm which can be matched with the performance requirements by coding the population and constructing the fitness function. Experimental results confirm that this proposed algorithm can adaptively search for the optimal key parameters that match the performance requirements. Compared with other related algorithms in recent years, it exhibits strong robustness against various signal processing attacks and synchronization attacks.

关键词： audio watermarking butterfly optimization algorithm robustness dither modulation optimal parameter

来源：评论

学校读者我要写书评

暂无评论

Unknown area exploration for robots with energy constraints using a modified butterfly optimization algorithm

引用

SOFT COMPUTING 2023年第7期27卷 3785-3804页

作者： Bendahmane, Amine Tlemsani, Redouane Univ Sci & Technol Oran Mohamed Boudiaf Comp Sci Dept Signal Image Parole SIMPA Lab BP 1505 Bir El Djir 31000 Oran Algeria

butterfly optimization algorithm (BOA) is a recent metaheuristic that has been used in several optimization problems. In this paper, we propose a new version of the algorithm (xBOA) based on the crossover operator and compare its results to the original BOA and 3 other variants recently introduced in the literature. We also proposed a framework for solving the unknown area exploration problem with energy constraints using metaheuristics in both single- and multi-robot scenarios. This framework allowed us to benchmark the performances of different metaheuristics for the robotics exploration problem. We conducted several experiments to validate this framework and used it to compare the effectiveness of xBOA with well-known metaheuristics used in the literature through 5 evaluation criteria. Although BOA and xBOA are not optimal in all these criteria, we found that BOA can be a good alternative to many metaheuristics in terms of the exploration time, while xBOA is more robust to local optima;has better fitness convergence;and achieves better exploration rates than the original BOA and its other variants.

关键词： Robotics Exploration butterfly optimization algorithm Crossover operator Metaheuristics Multi-robot systems

来源：评论

学校读者我要写书评

暂无评论

Exploiting parallel graphics processing units to improve association rule mining in transactional databases using butterfly optimization algorithm

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2021年第4期24卷 3767-3778页

作者： Zoraghchian, Ali Abbas Sohrabi, Mohammad Karim Yaghmaee, Farzin Islamic Azad Univ Dept Comp Engn Semnan Branch Semnan Iran Semnan Univ Dept Elect & Comp Engn Semnan Iran

Extracting association rules from huge amounts of data is an essential method in data mining that can provide valuable knowledge from datasets for various applications. In addition to a variety of traditional and parallel association rule mining (ARM) methods, some studies have also been represented in the literature to extract association rules from datasets using multi-core processors combining GPU-CPU (graphics processing unit-central processing unit) and FPGA-CPU (field programmable gate array-central processing unit). These parallel methods have been utilized to speed up the process of ARM, including its major phase, frequent itemset mining (FIM). The use of multiple GPUs for ARM and FIM has been usually in the cluster form, and the simultaneous use of multiple GPUs on a single system has not been extensively used to extract the set of association rules. Due to the huge volume of big data, finding more efficient and faster ARM and FIM methods is still of interest to researchers. In this paper, the butterfly optimization algorithm (BOA), which has an acceptable accuracy and speed in solving optimization problems, is used for ARM. In this study, an efficient platform including a single CPU and three parallel GPUs are employed to parallelize ARM using BOA. The main feature of the proposed method is the use of parallel GPUs on a single computer, to speed up the process, and the use of the CPU as a synchronizer. Since the GPUs reduce the runtime by executing similar duplicate structures, the proposed model speeds up the mining process and prevents computing overload on the CPU. All three phases of the algorithm are implemented on the parallel graphics cards to increase the performance. The evaluation of the proposed method and its comparison with the BSO- and GBSO-Miner methods show its better performance in terms of accuracy and execution time.

关键词： Association rule mining Frequent itemset mining butterfly optimization algorithm Multiprocessor systems

来源：评论

学校读者我要写书评

暂无评论

A modified butterfly optimization algorithm for mechanical design optimization problems

引用

JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2018年第1期40卷 1-17页

作者： Arora, Sankalap Singh, Satvir Yetilmezsoy, Kaan DAV Univ Dept Comp Sci & Engn Jalandhar Punjab India IK Gujral Punjab Tech Univ Dept Elect & Commun Engn Jalandhar Punjab India Yildiz Tech Univ Fac Civil Engn Dept Environm Engn TR-34220 Istanbul Turkey

This paper presents a modified butterfly optimization algorithm (MBOA) for solving mechanical design optimization problems. The modification is focused on an additional intensive exploitation phase which provides more chance to solutions to improve itself. The performance of the proposed algorithm is validated on fifteen benchmark test functions and three engineering design problems which have different natures of objective functions, constraints and decision variables. The experimental results are analyzed in comparison with those reported in the literature. The results indicate that the MBOA provides very competitive results in comparison with other existing optimization algorithms.

关键词： butterfly optimization algorithm Intensive exploitation Benchmark functions Engineering design problems

来源：评论

学校读者我要写书评

暂无评论

Solving power system economic emission dispatch problem under complex constraints via dimension differential learn butterfly optimization algorithm with FDC-based

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2024年 197卷

作者： He, Kai Zhang, Yong Wang, Yukun Zhou, Ronghe Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China

The economic emission dispatch (EED) aims to minimize the fuel and pollutant emission costs of generator units under various complex constraints. Optimizing the EED problem is of crucial importance for alleviating the current energy and environmental pressures. In this work, nearly all known complex constraints in the EED problem, including the valve-point effect, transmission line power loss, prohibited operating zones, and ramp- rate limits, are taken into account, and an enhanced version of butterfly optimization algorithm (FDCDLBOA) is proposed to solve it. First, a new adaptive fragrance is employed to optimize the instability caused by target differences and improve the convergence performance. Second, the proposed dimension differential learning strategy evolves the position of individuals with the help of superior dimensional information in the population, and this extensive learning exchange can balance global and local search, maintain diversity, and get rid of local optima. Third, the Fitness-Distance-Constraint (FDC) guide selection method is employed for the first time to handle the complex constraints of EED problems, enhancing the ability of individuals to bypass the infeasible search areas. After evaluating the proposed FDCDLBOA on CEC 2022 test suite, it is applied to solve 8 EED cases, encompassing small-, medium- and large-scale systems. Notably, the 280-generator case is the first large-scale test to exceed 200 generators. Compared with 9 representative algorithms, FDCDLBOA performs outstandingly in terms of robustness, improvement index (IF), mean constraint violation (MV), feasibility rate (FR) and Quade multiple comparison, among which IF, MV, FR, and Quade are all employed for evaluating the EED problem for the first time. The presented results confirm that the proposed method effectively enhances the robustness of high-quality solutions and the ability to handle complex constraints, demonstrating strong competitiveness and potential in solvi

关键词： Economic emission dispatch Complex constraints Large-scale generator unit butterfly optimization algorithm Dimension differential learning FDC-based guide selection

来源：评论

学校读者我要写书评

暂无评论

A hybridization of butterfly optimization algorithm and harmony search for fuzzy modelling in phishing attack detection

引用

NEURAL COMPUTING & APPLICATIONS 2023年第7期35卷 5501-5512页

作者： Nordin, Noor Syahirah Ismail, Mohd Arfian Univ Malaysia Pahang Fac Comp Pekan 26300 Malaysia

Fuzzy system is one of the most used systems in the decision-making and classification method as it is easy to understand because the way this system works is closer to how humans think. It is a system that uses human experts to hold the membership values to make decisions. However, it is hard to determine the fuzzy parameter manually in a complex problem, and the process of generating the parameter is called fuzzy modelling. Therefore, an optimization method is needed to solve this issue, and one of the best methods to be applied is butterfly optimization algorithm. In this paper, BOA was improvised by combining this algorithm with Harmony Search (HS) in order to achieve optimal results in fuzzy modelling. The advantages of both algorithms are used to balance the exploration and exploitation in the searching process. Two datasets from UCI machine learning were used: Website Phishing Dataset and Phishing Websites Dataset. As a result, the average accuracy for WPD and PWD was 98.69% and 98.80%, respectively. In conclusion, the proposed method shows promising and effective results compared to other methods.

关键词： Fuzzy butterfly optimization algorithm Harmony search Phishing detection

来源：评论

学校读者我要写书评

暂无评论

An opposition-based butterfly optimization algorithm with adaptive elite mutation in solving complex high-dimensional optimization problems

引用

MATHEMATICS AND COMPUTERS IN SIMULATION 2023年 204卷 498-528页

作者： Li, Yu Yu, Xiaomei Liu, Jingsen Henan Univ Inst Management Sci & Engn Kaifeng 475004 Peoples R China Henan Univ Sch Business Kaifeng 475004 Peoples R China Henan Univ Inst Intelligent Network Syst Kaifeng 475004 Peoples R China Henan Univ Software Sch Kaifeng 475004 Peoples R China

To solve complex high-dimensional optimization problems, an opposition-based butterfly optimization algorithm with adaptive elite mutation (OBOAEM) is proposed. In the initial stage, the opposition-based learning mechanism is introduced to increase the diversity of the initial population and improve the probability of finding the optimal value. In order to balance the process of global search and local search, the segmental adjustment factor is used to improve the optimization accuracy of the algorithm. In the final stage of the algorithm, the elite mutation strategy is adopted to prevent precocity of the algorithm. In this paper, 23 benchmark functions are selected to test OBOAEM and original algorithm BOA in low dimension, and 16 benchmark functions are introduced to test OBOAEM and eight intelligent optimization algorithms in high dimensions 100, 500 and 1000. In addition, the simulation experiments of 30 CEC2014 complex deformation functions reveal the OBOAEM has a good effect in solving complex optimization problems, which are compared with six state-of-art algorithms. Friedman test is used forstatistical analysis, showing that OBOAEM has better optimization performance for complex high-dimensional problems. Finally, OBOAEM is applied to engineering design problems, and it is proved that OBOAEM is competitive in solving real-world problems. (c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： butterfly optimization algorithm Opposition-based learning mechanism Elite mutation strategy Complex high-dimensional problems CEC2014 Engineering problems

来源：评论

学校读者我要写书评

暂无评论

BUILDING ENERGY optimization USING butterfly optimization algorithm

引用

THERMAL SCIENCE 2022年第5期26卷 3975-3986页

作者： Ghalambaz, Mehdi Yengejeh, Reza Jalilzadeh Davami, Amir Hossein Islamic Azad Univ Dept Environm Engn Ahvaz Branch Ahvaz Iran Islamic Azad Univ Dept Environm Management Ahvaz Branch HSE Ahvaz Iran

The butterfly optimization algorithm (BOA) is a novel meta-heuristic optimization algorithm, inspired by the intelligence foraging performance of butterflies. The aim of the current research is to minimize the energy consumption of an office building in Seattle using BOA. A heat transfer model of the building was modeled in EnergyPluse software and annual energy demand of the building was computed. A two-way coupling was established between EnergyPluse and BOA. The Energy-Pluse takes into account the non-linear interaction of design variables and com-putes the energy demand of the building. Then the computed amount of energy de-mand would be transferred to the BOA, where the optimization algorithm decides about changing the design variables. Then, a new set of design variables would be transferred to EnergyPluse for a new simulation. Through the dynamic interaction of BOA and EnergyPluse, a building with minimum energy demand was designed. The impact of the number of butterflies on the performance of the optimization algorithm was also investigated. It was found that using 50 butterflies would lead to the best optimization performance. A comparison between the present method and literature optimization methods was made, which showed that BOA with 15 butterflies or higher could adequately avoid local minimums and reach the best minimum with a reasonable computation effort.

关键词： building optimization problems butterfly optimization algorithm building energy demand optimum building design

来源：评论

学校读者我要写书评

暂无评论

An enhanced adaptive butterfly optimization algorithm rigorously verified on engineering problems and implemented to ISAR image motion compensation

引用

ENGINEERING COMPUTATIONS 2020年第9期37卷 3543-3566页

作者： Ustun, Deniz Tarsus Univ Fac Engn Dept Comp Engn Tarsus Turkey

Purpose This study aims to evolve an enhanced butterfly optimization algorithm (BOA) with respect to convergence and accuracy performance for numerous benchmark functions, rigorous constrained engineering design problems and an inverse synthetic aperture radar (ISAR) image motion compensation. Design/methodology/approach Adaptive BOA (ABOA) is thus developed by incorporating spatial dispersal strategy to the global search and inserting the fittest solution to the local search, and hence its exploration and exploitation abilities are improved. Findings The accuracy and convergence performance of ABOA are well verified via exhaustive comparisons with BOA and its existing variants such as improved BOA (IBOA), modified BOA (MBOA) and BOA with Levy flight (BOAL) in terms of various precise metrics through 15 classical and 12 conference on evolutionary computation (CEC)-2017 benchmark functions. ABOA has outstanding accuracy and stability performance better than BOA, IBOA, MBOA and BOAL for most of the benchmarks. The design optimization performance of ABOA is also evaluated for three constrained engineering problems such as welded beam design, spring design and gear train design and the results are compared with those of BOA, MBOA and BOA with chaos. ABOA, therefore, optimizes engineering designs with the most optimal variables. Furthermore, a validation is performed through translational motion compensation (TMC) of the ISAR image for an aircraft, which includes blurriness. In TMC, the motion parameters such as velocity and acceleration of target are optimally predicted by the optimization algorithms. The TMC results are elaborately compared with BOA, IBOA, MBOA and BOAL between each other in view of images, motion parameter and numerical image measuring metrics. Originality/value The outperforming results reflect the optimization and design successes of ABOA which is enhanced by establishing better global and local search abilities over BOA and its existing variants.

关键词： Engineering design butterfly optimization algorithm ISAR imaging Enhanced algorithm Benchmark functions CEC functions Motion compensation

来源：评论

学校读者我要写书评

暂无评论

Dynamic butterfly optimization algorithm for Feature Selection

引用

IEEE ACCESS 2020年 8卷 194303-194314页

作者： Tubishat, Mohammad Alswaitti, Mohammed Mirjalili, Seyedali Al-Garadi, Mohammed Ali Alrashdan, Ma'en Tayseer Rana, Toqir A. Asia Pacific Univ Technol & Innovat Sch Technol & Comp Kuala Lumpur 57000 Malaysia Xiamen Univ Malaysia Sch Elect & Comp Engn ICT Bandar Sunsuria 43900 Malaysia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Fortitude Valley Qld 4006 Australia Univ Calif San Diego Dept Radiol La Jolla CA 92093 USA Univ Lahore Dept Comp Sci & IT Lahore 54590 Pakistan

Feature selection represents an essential pre-processing step for a wide range of Machine Learning approaches. Datasets typically contain irrelevant features that may negatively affect the classifier performance. A feature selector can reduce the number of these features and maximise the classifier accuracy. This paper proposes a Dynamic butterfly optimization algorithm (DBOA) as an improved variant to butterfly optimization algorithm (BOA) for feature selection problems. BOA represents one of the most recently proposed optimization algorithms. BOA has demonstrated its ability to solve different types of problems with competitive results compared to other optimization algorithms. However, the original BOA algorithm has problems when optimising high-dimensional problems. Such issues include stagnation into local optima and lacking solutions diversity during the optimization process. To alleviate these weaknesses of the original BOA, two significant improvements are introduced in the original BOA: the development of a Local Search algorithm Based on Mutation (LSAM) operator to avoid local optima problem and the use of LSAM to improve BOA solutions diversity. To demonstrate the efficiency and superiority of the proposed DBOA algorithm, 20 benchmark datasets from the UCI repository are employed. The classification accuracy, the fitness values, the number of selected features, the statistical results, and convergence curves are reported for DBOA and its competing algorithms. These results demonstrate that DBOA significantly outperforms the comparative algorithms on the majority of the used performance metrics.

关键词： optimization Feature extraction Heuristic algorithms Classification algorithms Benchmark testing Machine learning algorithms Transfer functions butterfly optimization algorithm feature selection local search algorithm based on mutation

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：