检索结果-内蒙古大学图书馆

2nd International Conference on Design Tools and Methods in Industrial Engineering (ADM)

作者： Marannano, Giuseppe Ricotta, Vito Univ Palermo Engn Dept Viale Sci I-90128 Palermo Italy

ISBN: (纸本)9783030912345;9783030912338

In the mid-1980s, several metaheuristic methods began to be developed for solving a very large class of computational problems with the aim of obtaining more robust and efficient procedures. Among them, many metaheuristic methods use bio-inspired intelligent algorithms. In recent years, these methods are becoming increasingly important and they can be used in various subject areas for solving complex problems. firefly algorithm is a nature-inspired optimization algorithm proposed by Yang to solve multimodal optimization problems. In particular, the method is inspired by the nature of fireflies to emit a light signal to attract other individuals of this species. In this work, a numerical study for solving a structural problem using the firefly algorithm as optimization method is conducted. In particular, the implementation of the firefly algorithm in several input files realized in the ANSYS Parametric Design Language has allowed the definition of the optimal stacking sequence and the laminate thickness of a composite gear housing used to enclose the components of a mechanical reducer.

关键词： firefly algorithm Structural optimization Finite element analysis ANSYS

来源：评论

学校读者我要写书评

暂无评论

firefly algorithm Approach For Rational Bezier Border Reconstruction of Skin Lesions from Macroscopic Medical Images 13

Firefly Algorithm Approach For Rational Bezier Border Recons...

引用

13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) / International Workshop on Applied Artificial Intelligence (AI Maldives)

作者： Galvez, Akemi Iglesias, Andres Ugail, Hassan You, Lihua Haron, Habibollah Habib, Zulfiqar Toho Univ Fac Sci Dept Informat Sci Funabashi Chiba Japan Univ Cantabria Dept Appl Math & Comp Sci Santander Spain Univ Bradford Fac Engn & Informat Media Design & Technol Bradford W Yorkshire England Bournemouth Univ Natl Ctr Comp Animat Media Sch Talbot Campus Poole Dorset England Univ Teknol Malaysia Fac Comp Dept Comp Sci Skudai Malaysia COMSATS Univ Islamabad Dept Comp Sci Ctr Res VIG Robot Lahore Pakistan

ISBN: (纸本)9781728127415

Image segmentation is a fundamental step for image processing of medical images. One of the most important tasks in this step is border reconstruction, which consists of constructing a border curve separating the organ or tissue of interest from the image background. This problem can be formulated as an optimization problem, where the border curve is computed through data fitting procedures from a collection of data points assumed to lie on the boundary of the object under analysis. However, standard mathematical optimization techniques do not provide satisfactory solutions to this problem. Some recent papers have applied evolutionary computation techniques to tackle this issue. Such works are only focused on the polynomial case, ignoring the more powerful (but also more difficult) case of rational curves. In this paper, we address this problem with rational Bezier curves by applying the firefly algorithm, a popular bio-inspired swarm intelligence technique for optimization. Experimental results on medical images of melanomas show that this method performs well and can be successfully applied to this problem.

关键词： medical images image segmentation border reconstruction skin lesions swarm intelligence firefly algorithm rational curves Bezier curves

来源：评论

学校读者我要写书评

暂无评论

firefly algorithm Optimized Extreme Learning Machine for Hyperspectral Image Classification 23

Firefly Algorithm Optimized Extreme Learning Machine for Hyp...

引用

23rd International Conference on Geoinformatics (Geoinformatics)

作者： Su, Hongjun Cai, Yue Jiangsu Key Lab Resources & Environm Informat Eng Xuzhou Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing 210098 Jiangsu Peoples R China

ISBN: (纸本)9781467376631

A firefly algorithm (FA) based parameter optimization method for extreme learning machine (ELM) for hyperspectral image classification is proposed. The parameters of regularization coefficient and Gaussian kernel are optimized by firefly algorithm in this method. The experimental results show that the FA-based optimization algorithm can provide the better performance of extreme learning machines for hyperspectral image classification, and it outperforms the popular particle swarm optimization (PSO), genetic algorithm (GA) method.

关键词： firefly algorithm extreme learning machine hyperspectral imaging classification

来源：评论

学校读者我要写书评

暂无评论

firefly algorithm with Proportional Adjustment Strategy 1

引用

5th International WISE Workshop on Data Quality and Trust in Big Data (QUAT)

作者： Wang, Jing Liu, Guiyuan Song, William Wei Jiangxi Univ Finance & Econ Sch Software & Internet Things Engn Nanchang Jiangxi Peoples R China Dalarna Univ Borlange Sweden

ISBN: (数字)9783030191436

ISBN: (纸本)9783030191436;9783030191429

firefly algorithm is a new heuristic intelligent optimization algorithm and has excellent performance in many optimization problems. However, in the face of some multimodal and high-dimensional problems, the algorithm is easy to fall into the local optimum. In order to avoid this phenomenon, this paper proposed an improved firefly algorithm with proportional adjustment strategy for alpha and beta. Thirteen well-known benchmark functions are used to verify the performance of our proposed algorithm, the computational results show that our proposed algorithm is more efficient than many other FA algorithms.

关键词： firefly algorithm Meta-heuristic algorithm Intelligent optimization algorithm Global optimization

来源：评论

学校读者我要写书评

暂无评论

firefly algorithm for the RBF Network Design

Firefly Algorithm for the RBF Network Design

引用

IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications (INISTA)

作者： Czarnowski, Ireneusz Jedrzejowicz, Piotr Gdynia Maritime Univ Dept Informat Syst Morska 83 PL-81225 Gdynia Poland

ISBN: (纸本)9781538651506

The paper focuses on using the firefly algorithm for designing the radial-basis neural networks. The firefly algorithm belongs to a family of the global optimization tools. The firefly algorithm is a nature-inspired and one of the most powerful algorithms for solving the NP-hard optimization problems. In the paper the firefly algorithm is used as a tool for designing of the RBF network, including estimation of its output weights and transfer function parameters. The details of the implementation are discussed. Computational experiment has been carried-out with a view to investigate effectiveness of the discussed implementation. Experiment results have confirmed usefulness of the proposed approach. Conclusions include suggestions for future research.

关键词： RBF network firefly algorithm optimization

来源：评论

学校读者我要写书评

暂无评论

firefly algorithm with Attractiveness Matrix Enhancement for Shortest Route Alternative Solution 2

Firefly Algorithm with Attractiveness Matrix Enhancement for...

引用

2nd International Conference on Computer Communications Control Technology

作者： Ali, Nadhirah Othman, Mohd Azlishah Misran, Mohamad Harris Nor, Mohd Khanapiah Sulaiman, Hamzah Asyrani Univ Tekn Malaysia Melaka Fac Elect & Comp Engn Ctr Telecommun Res & Innovat CeTRI Durian Tunggal 76100 Melaka Malaysia

ISBN: (纸本)9781479979523

Finding the shortest path or route is a problem that have been studies by years. Many algorithm have been used to solve this problem one of it is firefly algorithm. Thus this paper done some modified for the light absorption coefficient and used the attractiveness matrix as the guidance to choose the next node till all the nodes is visited only once and return back to the beginning.

关键词： firefly algorithm shortest path modification

来源：评论

学校读者我要写书评

暂无评论

firefly algorithm in Biomedical and Health Care: Advances, Issues and Challenges

引用

SN Computer Science 2020年第6期1卷 1-36页

作者： Nayak, Janmenjoy Naik, Bighnaraj Dinesh, Paidi Vakula, Kanithi Dash, Pandit Byomakesha Department of Computer Science and Engineering Aditya Institute of Technology and Management (AITAM) K Kotturu Andhra Pradesh Tekkali 532201 India Department of Computer Application Veer Surendra Sai University of Technology Odisha Burla 768018 India Department of Computer Science and Engineering Sri Sivani College of Engineering Andhra Pradesh Srikakulam 532402 India

Since the past decades, most of the nature inspired optimization algorithms (NIOA) have been developed and become admired due to their effectiveness for resolving a variety of complex problems of dissimilar domain. firefly algorithm (FA) is well-known, yet efficient nature inspired swarm intelligence (SI) based metaheuristic algorithm. Since from its initiation, FA has become well-liked between the researchers due to its competence and turn out to be an interesting technique for the practitioners as well as researchers for solving the problems of numerous fields of research such as classifications, clustering, neural networks, biomedical engineering, healthcare as well as other research domain. Moreover, there is an outstanding track record of FA in solving biomedical engineering (BME) and healthcare (HC) problems. Abundant complexities have been worked out with the assist of FA and its variants. By taking these particulars into concern, in this paper, a first ever in-depth analysis has been addressed on the variants, importance, applications as well as enhancements of FA in BME as well as HC. The major intention behind this investigative work is to motivate the researchers to improve and innovate new solutions for multifaceted problems of healthcare and biomedical engineering using FA. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Biomedical engineering and healthcare firefly algorithm Metaheuristics Nature-inspired algorithm Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

A Global Best-guided firefly algorithm for Engineering Problems

引用

Journal of Bionic Engineering 2023年第5期20卷 2359-2388页

作者： Mohsen Zare Mojtaba Ghasemi Amir Zahedi Keyvan Golalipour Soleiman Kadkhoda Mohammadi Seyedali Mirjalili Laith Abualigah Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahrom***FrasIran Department of Electronics and Electrical Engineering Shiraz University of TechnologyShiraz***Iran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehran***Iran Department of Computer Engineering Sari BranchIslamic Azad UniversitySari***Iran Department of Electrical Engineering Urmia BranchIslamic Azad UniversityUrmia571696896Iran Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722South Korea Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan School of Computer Sciences Universiti Sains Malaysia11800George TownPulau PinangMalaysia University Research and Innovation Center Obuda University1034BudapestHungary School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Applied science research center Applied science private universityAmman11931Jordan

The firefly algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when *** article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement *** proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best ***,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main ***,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original *** GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha ***,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced *** all cases,GbFA provides the optimal result compared to other *** that the source code of the GbFA algorithm is publicly available at https://***/projects/gbfa.

关键词： firefly algorithm New movement vector Global best-guided firefly algorithm Global optimization Engineering design

来源：评论

学校读者我要写书评

暂无评论

A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation

引用

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2018年第24期56卷 7354-7374页

作者： Zhu, Lixia Zhang, Zeqiang Wang, Yi Southwest Jiaotong Univ Sch Mech Engn Chengdu Sichuan Peoples R China Auburn Univ Dept Math & Comp Sci Montgomery AL 36117 USA

The safety hazards existing in the process of disassembling waste products pose potential harms to the physical and mental health of the workers. In this article, these hazards involved in the disassembly operations are evaluated and taken into consideration in a disassembly line balancing problem. A multi-objective mathematical model is constructed to minimise the number of workstations, maximise the smoothing rate and minimise the average maximum hazard involved in the disassembly line. Subsequently, a Pareto firefly algorithm is proposed to solve the problem. The random key encoding method based on the smallest position rule is used to adapt the firefly algorithm to tackle the discrete optimisation problem of the disassembly line balancing. To avoid the search being trapped in a local optimum, a random perturbation strategy based on a swap operation is performed on the non-inferior solutions. The validity of the proposed algorithm is tested by comparing with two other algorithms in the existing literature using a 25-task phone disassembly case. Finally, the proposed algorithm is applied to solve a refrigerator disassembly line problem based on the field investigation and a comparison of the proposed Pareto firefly algorithm with another multi-objective firefly algorithm in the existing literature is performed to further identify the superior performance of the proposed Pareto firefly algorithm, and eight Pareto optimal solutions are obtained for decision makers to make a decision.

关键词： hazard evaluation disassembly line balancing problem firefly algorithm multi-objective optimisation Pareto optimal solution set

来源：评论

学校读者我要写书评

暂无评论

firefly algorithm Using Cluster Information for Superior Solution Set Search

Firefly Algorithm Using Cluster Information for Superior Sol...

引用

IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Wang, Hongran Tamura, Kenichi Tsuchiya, Junichi Yasuda, Keiichiro Tokyo Metropolitan Univ Dept Elect & Elect Engn 1-1 Minamiosawa Hachioji Tokyo 1920397 Japan

ISBN: (纸本)9781538616451

We analyzed the search characteristics of firefly algorithm (FA), which has a fundamental nature of a Superior Solution Set Search Problem, previously defined in our previous study for single-objective optimization problems. In this study, we proposed a new FA method based on the former problem. This method, which employs cluster information by K-means clustering, is tested for performance by fundamental numerical experiments.

关键词： Metaheuristics Single-Objective Optimization Superior Solution Set Search Problem Cluster K-means clustering firefly 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：