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检索条件"主题词=Fireworks algorithm"

共 265 条记录，以下是111-120 订阅

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A Lite fireworks algorithm with Fractal Dimension Constraint for Feature Selection 6

A Lite Fireworks Algorithm with Fractal Dimension Constraint...

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IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)

作者： Zeng, Min Mo, Haimiao Liang, Zhiming Wang, Hua Guangdong Univ Sci & Technol Sch Comp Sci Dongguan Guangdong Peoples R China Hefei Univ Technol Sch Management Hefei Peoples R China Guangzhou Med Univ Affiliated Hosp 5 Dept Pharm Guangzhou Guangdong Peoples R China

ISBN: (纸本)9798350311259

As the use of robotics becomes more widespread, the huge amount of vision data leads to a dramatic increase in data dimensionality. Although deep learning methods can effectively process these high-dimensional vision data. Due to the limitation of computational resources, some special scenarios still rely on traditional machine learning methods. However, these high-dimensional visual data lead to great challenges for traditional machine learning methods. Therefore, we propose a Lite fireworks algorithm with Fractal Dimension constraint for feature selection (LFWA+FD) and use it to solve the feature selection problem driven by robot vision. The "LFWA+FD" focuses on searching the ideal feature subset by simplifying the fireworks algorithm and constraining the dimensionality of selected features by fractal dimensionality, which in turn reduces the approximate features and reduces the noise in the original data to improve the accuracy of the model. The comparative experimental results of two publicly available datasets from UCI show that the proposed method can effectively select a subset of features useful for model inference and remove a large amount of noise noise present in the original data to improve the performance.

关键词： Visual Data Processing Feature Selection Fractal Dimension fireworks algorithm

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Support Vector Machine Optimized by fireworks algorithm for Handwritten Digit Recognition 6th

Support Vector Machine Optimized by Fireworks Algorithm for ...

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6th International Conference on Modelling and Development of Intelligent Systems (MDIS)

作者： Tuba, Eva Hrosik, Romana Capor Alihodzic, Adis Jovanovic, Raka Tuba, Milan Singidunum Univ Belgrade Serbia Univ Dubrovnik Maritime Dept Dubrovnik Croatia Univ Sarajevo Fac Sci Sarajevo Bosnia & Herceg Hamad bin Khalifa Univ Qatar Environm & Energy Res Inst QEERI Doha Qatar

ISBN: (纸本)9783030392376;9783030392369

Handwritten digit recognition is an important subarea in the object recognition research area. Support vector machines represent a very successful recent binary classifier. Basic support vector machines have to be improved in order to deal with real-world problems. The introduction of soft margin for outliers and misclassified samples as well as kernel function for non linearly separably data leads to the hard optimization problem of selecting parameters for these two modifications. Grid search which is often used is rather inefficient. In this paper we propose the use of one of the latest swarm intelligence algorithms, the fireworks algorithm, for the support vector machine parameters tuning. We tested our approach on standard MNIST base of handwritten images and with selected set of simple features we obtained better results compared to other approaches from literature.

关键词： Handwritten digit recognition Machine learning Support vector machine Optimization Swarm intelligence fireworks algorithm

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Image retrieval based on fireworks algorithm optimizing convolutional neural network 4

Image retrieval based on fireworks algorithm optimizing conv...

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4th IEEE International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS)

作者： Wang, Chunzhi Wu, Pan Yan, Lingyu Zhou, Fangyu Cai, Wencheng Hubei Univ Technol Sch Comp Sci Wuhan Hubei Peoples R China

ISBN: (纸本)9781538675878

For the problem of the slow convergence rate of the traditional CNN algorithm using gradient descent algorithm to optimize parameters, a CNN model based on fireworks algorithm optimization is proposed, the parameters that need to be optimized are used as the input to the fireworks algorithm, and the cross-entropy (error) is used as the fitness function to improve the tuning process in the backward propagation, the MNIST handwritten character set is used for simulation experiments of image retrieval. The results show that the convergence speed of the improved algorithm is significantly improved.

关键词： fireworks algorithm convolutional neural network gradient descent image retrieval

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Adaptive fireworks algorithm based on simulated annealing 13

Adaptive fireworks algorithm based on simulated annealing

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13th International Conference on Computational Intelligence and Security (CIS)

作者： Ye, Wenwen Wen, Jiechang Guangdong Univ Technol Sch Appl Math Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538648223

This paper proposes an adaptive fireworks algorithm based on simulated annealing to solve global optimization problems. It normalizes the individuals before the explosion operation and then restores them after the explosion operation. The subpopulations are produced simultaneously by the explosion operation and the mutation operation, and then a new way to produce the explosion amplitude by introducing the simulated annealing is used to adjust the explosion amplitude adaptively along with the evolution. Experimental results on eight benchmark functions with different shift values show that the proposed algorithm has higher accuracy and faster convergence rate than the other five compared algorithms.

关键词： fireworks algorithm Normalization and reduction Simulated annealing

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Enhanced fireworks algorithm for Dynamic Deployment of Wireless Sensor Networks 2

Enhanced Fireworks Algorithm for Dynamic Deployment of Wirel...

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2nd International Conference on Frontiers of Sensors Technologies (ICFST)

作者： Liu, Xiaolei Zhang, Xiaosong Zhu, Qingxin Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Ctr Cyber Secur Chengdu Sichuan Peoples R China

ISBN: (纸本)9781509048601

The optimization for deployment of wireless sensor networks is helpful to improve the sensor coverage and the reliability of system. In this work, an enhanced fireworks algorithm is applied to the dynamic deployment of mobile sensor networks. Compared with the existing mainstream swarm intelligence algorithms, this proposed approach has the advantages of good convergence rate, fast convergence and better coverage, which is closer to the theoretical coverage value. Experimental results show that the enhanced fireworks algorithm is able to get the optimal deployment quickly and effectively in the actual scene.

关键词： component fireworks algorithm wireless sensor networks dynamic deployment

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Elite-Leading fireworks algorithm 8th

Elite-Leading Fireworks Algorithm

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8th International Conference on Swarm Intelligence (ICSI)

作者： Zhao, Xinchao Li, Rui Zuo, Xingquan Tan, Ying Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

ISBN: (纸本)9783319618241;9783319618234

fireworks algorithm (FWA) is effective to solve optimization problems as a swarm intelligence algorithm. In this paper, the elite-leading fireworks algorithm (ELFWA) is proposed based on dynamic search in fireworks algorithm (dynFWA), which is an important improvement of FWA. In dynFWA firework is separated to two group: core-firework (CF) and non-core fireworks (non-CFs). This paper takes some beneficial information from non-CFs to reinforce the local search effect of CF. Random reinitialization and elite-leading operator are used to maintain the diversity of the non-CFs, which play an important role in global search. Based on the CEC2015 benchmark functions suite, ELFWA has a very competitive performance when comparing with state-of-the-art fireworks algorithms, such as dynFWA, dynFWACM and eddynFWA.

关键词： fireworks algorithm Elite-leading operator Random reinitialization

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Optimization of Formation Configuration of Space-Based Radar Networking Based on Combined Chaotic fireworks algorithm 1

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International Conference on Autonomous Unmanned Systems (ICAUS)

作者： Yuan, Kai Jin, Hongbin Li, Hao Early Warning Acad Wuhan 430019 Peoples R China

ISBN: (数字)9789819904792

ISBN: (纸本)9789819904815;9789819904792;9789819904785

Aiming at the problems of slow convergence speed and easy to fall into local optimum in the traditional fireworks algorithm, this paper combines the chaotic mapping strategy with the adaptive explosion radius, and proposes a combined chaotic fireworks algorithm (Combinatorial Chaos fireworks algorithm, CCFWA). The algorithm is applied to the optimal formation configuration of space-based radar. Compared with the traditional chaotic mapping strategy, the combined chaotic system can distribute the initial value more evenly in the solution space, and the explosion radius can be adaptively changed according to the fitness, which ensures the search accuracy of the fireworks algorithm in the local search, and satisfies the Diversity of global search. The experimental results show that the optimal formation configuration of the space-based radar calculated by the combined chaotic fireworks algorithm is better than the calculation value of the traditional fireworks algorithm.

关键词： fireworks algorithm Chaotic mapping Space-based radar Optimal configuration

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Neural Network Training Model for Weather Forecasting Using fireworks algorithm 20

Neural Network Training Model for Weather Forecasting Using ...

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20th International Computer Science and Engineering Conference (ICSEC)

作者： Suksri, Saktaya Kimpan, Warangkhana King Mongkuts Inst Technol Ladkrabang Fac Sci Dept Comp Sci Bangkok Thailand

ISBN: (纸本)9781509044207

Weather forecasting is the application of science and technology in order to predict the weather conditions. It is important for agricultural and industrial sectors. Models of Artificial Neural Networks with supervised learning paradigm are suitable for weather forecasting in complexity atmosphere. Training algorithm is required for providing weight and bias values to the model. This research proposed a weather forecasting method using Artificial Neural Networks trained by fireworks algorithm. fireworks algorithm is a recently developed Swarm Intelligence algorithm for optimization. The main objective of the method is to predict daily mean temperature based on various measured parameters gained from the Meteorological Station, located in Bangkok. The experimental results indicate that the proposed method is advantageous for weather forecasting.

关键词： weather forecasting fireworks algorithm artificial neural network numerical weather prediction swarm intelligence

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JPEG Quantization Table Optimization by Guided fireworks algorithm 18th

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18th International Workshop on Combinatorial Image Analysis (IWCIA)

作者： Tuba, Eva Tuba, Milan Simian, Dana Jovanovic, Raka Univ Belgrade Fac Math Belgrade Serbia John Naisbitt Univ Grad Sch Comp Sci Belgrade Serbia Lucian Blaga Univ Fac Sci Sibiu Romania Hamad Bin Khalifa Univ Qatar Environm & Energy Res Inst Doha Qatar

ISBN: (纸本)9783319591087;9783319591070

Digital images are very useful and ubiquitous, however there is a problem with their storage because of their large size and memory requirement. JPEG lossy compression algorithm is prevailing standard that solves that problem. It facilitates different levels of compression (and the corresponding quality) by using recommended quantization tables. It is possible to optimize these tables for better image quality at the same level of compression. This presents a hard combinatorial optimization problem for which stochastic metaheuristics proved to be efficient. In this paper we propose an adjustment of the recent guided fireworks algorithm from the class of swarm intelligence algorithms for quantization table optimization. We tested the proposed approach on standard benchmark images and compared results with other approaches from literature. By using various image similarity metrics our approach proved to be more successful.

关键词： Image processing JPEG algorithm Quantization tables fireworks algorithm Swarm intelligence

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An Energy-Efficient Task Scheduling Strategy Based on Improved fireworks algorithm in Heterogeneous Cloud 23

An Energy-Efficient Task Scheduling Strategy Based on Improv...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhao, Hui Meng, Fanxin Feng, Nanzhi Wang, Quan Wan, Bo Wang, Jing School of Computer Science and Technology Xidian University Xi'an China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi'an China

ISBN: (纸本)9781665494571

With the development of cloud computing, energy consumption is getting bigger and bigger, and energy optimization has become one of the research focuses. Task scheduling is an efficient way to reduce the energy consumption of cloud. Most previous works ignored the heterogeneity of physical machines (PMs) configured with different CPUs and GPUs, which may result in high energy consumption in heterogeneous cloud. In this paper, an energy-efficient Task Scheduling Strategy based on Improved fireworks algorithm (TSS-IFWA) is proposed to reduce the energy consumption of the CPU/GPU heterogeneous cloud. First, a task scheduling model is constructed, in which the energy consumption of CPUs and GPUs are separately consid-ered. Then we obtain the task scheduling optimization problem of minimizing the energy consumption of heterogeneous cloud. Second, as traditional fireworks algorithm consumes massive computing resource and has a poor convergence performance, we improve a new calculation method of explosion amplitude of the fireworks algorithm. We also optimize the mutation strategy. Third, we utilize the improved fireworks algorithm to solve the task scheduling optimization problem. Specifically, the corre-sponding coding and decoding schemes of the fireworks algorithm are designed to initialize the fireworks individuals. We then obtain task scheduling results. Finally, TSS- IFWA is compared with traditional fireworks algorithm (FWA), round-robin algorithm (RR), and genetic algorithm (GA). The experimental results show that TSS- IFWA has a good performance in reducing energy consumption of heterogeneous cloud. © 2021 IEEE.

关键词： Cloud computing fireworks algorithm Energy consumption Processor scheduling Scheduling Energy efficiency Explosions

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