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检索条件"主题词=fireworks algorithm"

共 266 条记录，以下是81-90 订阅

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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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Accelerating fireworks algorithm with Weight-Based Guiding Sparks 10th

Accelerating Fireworks Algorithm with Weight-Based Guiding S...

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10th International Conference on Swarm Intelligence (ICSI)

作者： Li, Yuhao Yu, Jun Takagi, Hideyuki Tan, Ying Kyushu Univ Grad Sch Design Fukuoka 8158540 Japan Japan Soc Promot Sci Tokyo Japan Kyushu Univ Fac Design Fukuoka 8158540 Japan Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

ISBN: (纸本)9783030263683;9783030263690

We introduce two strategies into the guided fireworks algorithm (GFWA) to further improve its performance by generating one or more weight-based guiding spark individual(s) for each firework individual. The first strategy assigns different weights to spark individuals under each firework individual according to their fitness and then calculates one or more guiding vector(s) to guide the firework individual to evolve into potential directions. The second strategy decides the number of weight-based guiding spark individuals dynamically based on the evolution of a firework individual, i.e. if a firework individual does not evolve and survive in the next generation, then the second strategy reduces the number of spark individuals generated around the firework individual and generates the same reduced number of weight-based guiding spark individuals additionally. We design a controlled experiment to evaluate the performance of our proposal using CEC 2013 benchmark functions with five different dimensions. The experiment results confirm that the proposed strategies can provide effective guidance information to improve the GFWA performance significantly, and its acceleration effect for higher dimensional tasks is more obvious.

关键词： fireworks algorithm Meta-heuristic algorithm Weight-based guiding sparks Acceleration

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Wireless Sensor Network Coverage Problem Using Modified fireworks algorithm 12

Wireless Sensor Network Coverage Problem Using Modified Fire...

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12th International Wireless Communications & Mobile Computing Conference (IWCMC)

作者： Tuba, Eva Tuba, Milan Simian, Dana Univ Belgrade Fac Math Belgrade Serbia John Naisbitt Univ Fac Comp Sci Belgrade Serbia Lucian Blaga Univ Fac Sci Sibiu Romania

ISBN: (纸本)9781509003044

Wireless sensor networks are emerging technology with increasing number of applications, and consequently an active research area. One of the problems pertinent to wireless sensor networks is the coverage problem with number of definitions, depending on the assumed conditions. In this paper we consider hard optimization area coverage problem with the goal of finding optimal sensor nodes positions that maximize probabilistic coverage of the area of interest. For such type of optimization problem swarm intelligence stochastic metaheuristics have been successfully used. In this paper we propose a modified enhanced fireworks algorithm for wireless sensor network coverage problem and compare it with other approaches from literature, where our algorithm proved to be very robust and better, considering all conducted tests.

关键词： Wireless sensor networks Coverage problem fireworks algorithm Swarm intelligence Metaheuristic optimization algorithms

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Image Registration using fireworks algorithm and Chaotic Sequences 11

Image Registration using Fireworks Algorithm and Chaotic Seq...

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11th International Symposium on Advanced Topics in Electrical Engineering (ATEE)

作者： Bejinariu, Silviu-Ioan Rotariu, Cristian Costin, Hariton Luca, Ramona Romanian Acad Iasi Branch Inst Comp Sci Iasi Romania Grigore T Popa Univ Med & Pharm Fac Med Bioengn Iasi Romania

ISBN: (纸本)9781479975143

The fireworks algorithm is a relatively new optimization meta-heuristic which has good performances for global optimization problems. For some resources consuming problems, as image registration is, the optimization algorithm's performances are important, especially for real time computing. In this paper, the usage of chaos enhanced fireworks algorithm for features based image registration is proposed. During evolution, the chaotic sequences are used to compute the position of the newly generated regular sparks. The proposed procedure is applied for biomedical image registration and the performances obtained by using ten different chaotic sequences are compared to those obtained by using the standard algorithm

关键词： chaos map optimization fireworks algorithm image registration

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Node Localization in Ad Hoc Wireless Sensor Networks Using fireworks algorithm 5

Node Localization in Ad Hoc Wireless Sensor Networks Using F...

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5th International Conference on Multimedia Computing and Systems (ICMCS)

作者： Tuba, Eva Tuba, Milan Beko, Marko Univ Belgrade Fac Math Belgrade Serbia John Naisbitt Univ Fac Comp Sci Belgrade Serbia Univ Lusofona Humanidades & Tecnol Dept Comp Engn Lisbon Portugal

ISBN: (纸本)9781509051465

Wireless sensor networks represent a class of ad hoc networks for which the localization problem is emphasized since the collected data are usually not usable without location information. Range based localization techniques estimate locations of unknown nodes using information about distance from the anchor nodes, often estimating that distance by radio signal strength. Such distance estimation contains noise and determining node locations from such data is a hard optimization problem for which swarm intelligence stochastic population based algorithms have been used successfully. In this paper we propose localization algorithm based on recent fireworks swarm intelligence metaheuristic. We tested our approach using standard benchmark data and compared it to other algorithms from literature. Our fireworks based localization algorithm proved to be superior considering most performance indicators that included average localization error with different number of radio signal strength measurements, different number of nodes, different number of anchors and different radio signal strength noise levels.

关键词： wireless sensor networks localization problem fireworks algorithm swarm intelligence nature inspired algorithms

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Using Population Migration and Mutation to Improve Loser-Out Tournament-Based fireworks algorithm 12th

Using Population Migration and Mutation to Improve Loser-Out...

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12th International Conference on Swarm Intelligence (ICSI)

作者： Hong, PengCheng Zhang, JunQi Tongji Univ Dept Comp Sci & Technol Shanghai Peoples R China Minist Educ Key Lab Embedded Syst & Serv Comp Shanghai Peoples R China

ISBN: (纸本)9783030787424;9783030787431

The fireworks algorithm (FWA) is a newly proposed swarm intelligence algorithm inspired by the phenomena of fireworks explosion and has solved many real-world optimization problems successfully. A loser-out tournament-based fireworks algorithm (LoTFWA) is a new baseline in the development of FWA due to its outstanding independent framework and competition mechanism for multimodal optimization. Under this framework, each firework calculates its expected fitness improvement compared with the best fitness to determine whether to be reinitialized. Although LoTFWA achieves the best performance among the variants of FWA, it lacks of comprehensive consideration of the fireworks' cooperation and hence weakens the algorithm's power. This paper improves the cooperation of fireworks in LoTFWA based on the idea of population migration and mutation in biogeography-based optimization (BBO). The proposed mechanism not only promotes fireworks' exploration ability but also enhances their exploitation ability greatly. Experimental results show that the proposed algorithm attains superior performance than the state-of-the-art fireworks algorithm in both unimodal and multimodal functions.

关键词： Biogeography-based optimization Evolutionary algorithms fireworks algorithm Swarm intelligence Population migration

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Dynamically Adjusted Genetic and fireworks algorithm for Deployment Optimization of Indoor Positioning Signal Sources 5

Dynamically Adjusted Genetic and Fireworks Algorithm for Dep...

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5th International Conference on Robotics and Automation Sciences (ICRAS)

作者： Dong, Zhe He, Shuangjiang Zhao, Huijuan Wen, Shiqi Zhao, Jianhui Wuhan Fenghuo Informat Integrat Technol Co Ltd Wuhan Hubei Peoples R China Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China

ISBN: (纸本)9780738133119

The performance of indoor positioning systems is affected by spatial deployment of signal sources, thus we presented a combined method of adjusted Genetic algorithm (AGA) and fireworks algorithm (FWA) to provide the optimal deployment solution. The threshold value of fitness function is dynamically adjusted in GA, which reduces the probability of falling into local optimum and decreases the number of trivial solutions. With fireworks of fine chromosomes, the number of explosion sparks and radius of all explosion sparks are set as explosion factors. Through explosion and random generation, supplemented individuals are produced to increase population diversity and guarantee individual quality. Population evolves to the next generation by crossover and mutation, and the optimal result from evolutions is one solution of deployment with a number of signal sources and their locations. Based on a set of experiments, the AGA&FWA algorithm has been proved with good performance of convergence ability and computing expense. AGA&FWA has the best searching ability compared with the existing optimization methods, considering only the positioning error, or together with the cost of iBeacons. Our proposed AGA&FWA approach thus can provide the optimal signal sources' deployment for indoor positioning.

关键词： Genetic algorithm fireworks algorithm Indoor positioning iBeacons

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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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An Improved Patch Matching algorithm Based on fireworks algorithm 11

An Improved Patch Matching Algorithm Based on Fireworks Algo...

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11th International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Pattern Recognition and Computer Vision

作者： Shi, Huili Zhong, Sheng Yan, Luxin Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781510636385

With the development of technology, especially the rapid development of hand-held devices, it is more convenient to obtain video sequences, but the video quality still suffers from some issues, such as unwanted camera shakes and jitter. To address the issues, video stabilization techniques have been developed to obtain high quality and stable videos. Considering computational complexity and real-time requirements, patch matching, has become an important method for motion estimation and video stabilization. It transforms the video stabilization task into a minimum optimization problem. In this paper, we propose a novel patch matching method integrated with fireworks algorithm([1]) for motion search, which is a novel swarm intelligence optimization algorithm. Inspired by the fireworks explode in the air, the established mathematical model can be formulated as a parallel explosive search method by introducing random factors and selection strategies, and thus developed into a global probability search method for solving the optimal solution of complex optimization problems. It has excellent performance and high efficiency in solving complex optimization problems. Experimental results show that the improved patch matching method based on fireworks algorithm has achieved better results, compared with the ones with traditional motion search algorithms.

关键词： Patch matching motion estimation fireworks algorithm three-step search

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Image Segmentation using Clustering with fireworks algorithm 11

Image Segmentation using Clustering with Fireworks Algorithm

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11th International Conference on Intelligent Systems and Control (ISCO)

作者： Misra, Priya Ranjan Si, Tapas Bankura Unnayani Inst Engn Dept Comp Sci & Engn Bankura W Bengal India

ISBN: (纸本)9781509027170

This paper presents a hard clustering technique using fireworks algorithm with adaptive transfer function (FWA-ATF) for image segmentation. The fireworks algorithm (FWA) is a recently developed new Swarm Intelligence (SI) algorithm for function optimization. This algorithm simulates the process of fireworks explosion in the night sky. The main characteristic of FWA is the good balance between exploration and exploitation during the search process. The exploitation is done using good fireworks whereas the bad fireworks are responsible for exploration. FWA shows its efficiency and effectiveness in numerical function optimization over other SI algorithm like particle swarm optimization (PSO). FWA-ATF is a modified version of basic FWA and in this work, it is used in hard clustering technique to segment the image. FWA-ATF is used to find the optimal cluster centroids corresponding to different regions in the image. The proposed clustering technique is applied to segment four benchmark images and the well-known cluster validity indexDunn's Index is used to measure the performance of the proposed clustering technique quantitatively. The performance of the proposed method is compared with clustering using K-means, PSO and basic FWA. The experimental results demonstrates that the proposed clustering technique with FWA-ATF performs better than other methods in segmentation for most of the images.

关键词： fireworks algorithm adaptive transfer function optimization clustering segmentation Dunn's Index

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