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检索条件"主题词=fireworks algorithm"

共 265 条记录，以下是91-100 订阅

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A Metabolic Pathway Design Method Based on Surrogate-Assisted fireworks algorithm 15th

A Metabolic Pathway Design Method Based on Surrogate-Assiste...

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15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Zhao, Xin Cui, Shuxin Zhang, Tao Cao, Yahui Yang, Ming Liu, Weijie Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

ISBN: (纸本)9789819771806;9789819771813

In recent years, synthetic biology has emerged as a transformative field combining biology and information technology principles to forge novel approaches in medicine, agriculture, and the chemical industry. Metabolic pathway design is a critical branch of synthetic biology that enables more efficient and cost-effective production of target compounds. Gibbs free energy is a crucial criterion for assessing the feasibility of a metabolic pathway. Therefore, we propose a metabolic pathway design method named FWAPathDesign, based on a surrogate-assisted fireworks algorithm (FWA), which can design efficient metabolic pathways. This paper uses pyruvate and vanillin as target compounds to design metabolic pathways in the experimental part. Throughout the iterative process of the algorithm, FWAPathDesign can not only find the classical metabolic pathways but also design metabolic pathways with lower Gibbs free energy. Our comprehensive experiments validate the effectiveness of FWAPathDesign and confirm its potential to impact the field of metabolic pathway design significantly.

关键词： Metabolic Pathway Design Gibbs Free Energy Surrogate Model fireworks algorithm

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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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Two-Stage Dual-Archive fireworks algorithm for Multimodal Multi-Objective Optimization 2022

Two-Stage Dual-Archive Fireworks Algorithm for Multimodal Mu...

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6th International Conference on Machine Learning and Soft Computing (ICMLSC)

作者： Chen, Yushu Zhang, Kai Shen, Chaonan Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781450387477

In recent years, multi-objective optimization has attracted a lot of attention in the field of high performance computing. In this paper, two-stage dual-archive fireworks algorithm (TSDA_MMOFWA) is proposed to solve the multimodal multi-objective optimization problems (MMOPs). The first stage of the algorithm uses a dual-archive genetic operator evolution strategy. Each firework performs a relatively independent decision space search guided by global information to find a large number of Pareto optimal solution set. The second stage of the algorithm distributes the solution set uniformly over multiple approximate Pareto optimal sets by means of an adaptive firework explosion strategy. The performance of the algorithm is evaluated on 16 benchmark MMOPs. The experimental results show that the proposed TSDA_MMOFWA possesses a competitive performance compared to the state-of-the-art comparison algorithms.

关键词： fireworks algorithm multimodal multi-objective optimization multimodal multi-objective evolutionary algorithm

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Scheduling Strategy of Space-based Satellite Based on fireworks algorithm under Cloud Computing 19

Scheduling Strategy of Space-based Satellite Based on Firewo...

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4th International Conference on Big Data and Computing (ICBDC)

作者： Tian, Guilin Liu, Changyun Jiang, Haobo Air Force Engn Univ Grad Sch Xian 710064 Shaanxi Peoples R China Air Force Engn Univ Air & Missile Def Coll Xian 710064 Shaanxi Peoples R China

ISBN: (纸本)9781450362788

The scheduling of space-based satellite missions in cloud computing environment is a hotspot of current research. For the task completion time and the balance of virtual machine resource load, the cloud task scheduling fireworks algorithm is improved. According to the performance of virtual machine resources in the cloud environment, virtual The concept of relative fitness of the machine;the random mutation operation of the standard fireworks algorithm is improved to the targeted mutation operation, so that the virtual machine can obtain greater variation of the virtual machine resources with greater adaptability, and accelerate the convergence of the algorithm. Simulation experiments show that this algorithm improves the load balancing of virtual machine resources while reducing the task completion time. It is an effective cloud task scheduling algorithm. The task scheduling of space-based satellite in cloud computing environment is a hot spot of research at present. Aiming at the balance of task completion time and virtual machine resource load, the fireworks algorithm of cloud task scheduling is improved. According to the performance of virtual machine resources in cloud environment, the concept of relative fitness of virtual machine is introduced. The random mutation operation of standard fireworks algorithm is improved to a targeted mutation operation. The simulation results show that the proposed algorithm can reduce the task completion time and improve the load balance of virtual machine resources. It is an effective cloud task scheduling algorithm.

关键词： Cloud computing fireworks algorithm task scheduling space-based satellite

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From Resampling to Non-resampling: A fireworks algorithm-Based Framework for Solving Noisy Optimization Problems 8th

From Resampling to Non-resampling: A Fireworks Algorithm-Bas...

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8th International Conference on Swarm Intelligence (ICSI)

作者： Zhang, JunQi Zhu, ShanWen Zhou, MengChu Tongji Univ Dept Comp Sci & Technol Shanghai Peoples R China Minist Educ Key Lab Embedded Syst & Serv Comp Shanghai Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

ISBN: (纸本)9783319618241;9783319618234

Many resampling methods and non-resampling ones have been proposed to deal with noisy optimization problems. The former provides accurate fitness but demands more computational resources while the latter increases the diversity but may mislead the swarm. This paper proposes a fireworks algorithm (FWA) based framework to solve noisy optimization problems. It can gradually change its strategy from resampling to non-resampling during the evolutionary process. Experiments on CEC2015 benchmark functions with noises show that the algorithms based on the proposed framework outperform their original versions as well as their resampling versions.

关键词： fireworks algorithm Noisy environment Resampling Non-resampling

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GEFWA: Gradient-Enhanced fireworks algorithm for Optimizing Convolutional Neural Networks 14th

GEFWA: Gradient-Enhanced Fireworks Algorithm for Optimizing ...

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14th International Conference on Swarm Intelligence (ICSI)

作者： Chen, Maiyue Tan, Ying Peking Univ Sch Intelligence Sci & Technol Beijing Peoples R China Peking Univ Key Lab Machine Perceptron MOE Beijing Peoples R China Peking Univ Inst Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9783031366215;9783031366222

The efficacy of evolutionary and swarm intelligence-based black-box optimization algorithms in machine learning has increased their usage, but concerns have been raised about their low sample efficiency owing to their reliance on sampling. Consequently, improving the sample efficiency of conventional black-box optimization algorithms while retaining their strengths is crucial. To this end, we propose a new algorithm called Gradient Enhanced fireworks algorithm (GEFWA) that incorporates first-order gradient information into the population-based fireworks algorithm (FWA). We enhance the explosion operator with the gradient-enhanced explosion (GEE) and take advantage of attraction-based cooperation (ABC) for firework collaboration. Experimental results illustrate that GEFWA outperforms traditional first-order stochastic gradient descent-based optimization methods such as Adm and SGD when it comes to optimizing convolutional neural networks. These results demonstrate the potential of integrating gradient information into the FWA framework for addressing large-scale machine learning problems.

关键词： fireworks algorithm Deep learning Convolutional neural network Swarm intelligence

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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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Multi-dimensional Short-term Load Forecasting Based on XGBoost and fireworks algorithm 18

Multi-dimensional Short-term Load Forecasting Based on XGBoo...

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18th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

作者： Suo, Guilong Song, Liangcai Dou, Yanmei Cui, Zhiyong State Grid Henan Elect Power Co Hebi Power Supply Co Hebi Henan Peoples R China

ISBN: (纸本)9781728128658

Nowadays, most load forecasting of power system only takes advantage of sequential characteristics of the load itself. In fact, meteorological conditions, population and geographical factors will also have a significant impact on short-term load, thus reducing the accuracy of load forecasting. In this paper, we introduce XGBoost into load forecasting, making full use of its parallelism, anti-overfitting, second-order Taylor expansion and other characteristics, and modeling with multi-dimensional factors. The results show that the XGBoost model considering only sequential characteristics works fine in predicting the trend of short-term load, while the multi-dimensional model combined with temperature, humidity and rainfall factors has a significant improvement in prediction accuracy.

关键词： XGBoost load forecasting fireworks algorithm

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Transmission Line Icing Short-term Forecasting Based on improved Time Series Analysis by fireworks algorithm

Transmission Line Icing Short-term Forecasting Based on impr...

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International Conference on Condition Monitoring and Diagnosis (CMD)

作者： Huang, Xin-bo Li, Hong-bo Zhu, Yong-can Wang, Yu-xin Zheng, Xin-xin Wang, Yi-ge Xian Polytech Univ Coll Elect & Informat Xian Peoples R China Xidian Univ Sch Electromech Engn Xian Peoples R China

ISBN: (纸本)9781509033980

Icing on transmission line is a major risk factor for the safety of the power system. Therefore, it is necessary to research on online monitoring system and prediction model of icing. At present, there are many kinds of the icing prediction models are based on micro meteorological conditions, but the errors of the prediction are caused by the time-varying characteristics of the meteorological conditions and the error accumulation. On account of icing growth is a time accumulation process, this paper presents a new transmission line icing short-term forecast model based on improved time series analysis of fireworks algorithm, and by using icing accumulation, the error caused by meteorological parameters can be reduced to a minimum level. Finally, the model is verified and analyzed by simulating transmission line icing on the simulation platform for power system micro climate simulating, the short-term forecasting average absolute error of the model is 1.34%. Moreover, using the icing field monitoring data on Guizhou Grid, the mean error of this prediction model is 2.67%, which is much better than the conventional models.

关键词： transmission line icing forecasting time Series Analysis fireworks algorithm

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An Effective Guided fireworks algorithm for Solving UCAV Path Planning Problem 9th

An Effective Guided Fireworks Algorithm for Solving UCAV Pat...

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9th International Conference on Numerical Methods and Applications (NMA)

作者： Alihodzic, Adis Hasic, Damir Selmanovic, Elmedin Univ Sarajevo Dept Math Zmaja Bosne 33-35 Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9783030106928;9783030106911

The use of the unmanned aerial vehicles is rapidly growing in the ever more extensive range of applications where the military is the oldest ones. One of the fundamental problems in the unmanned combat aerial vehicles (UCAV) control is the path planning problem that refers to optimization of the flight route subject to various constraints inside the battlefield environments. Since the number of control points is high as well as the number of radars, the traditional methods could not produce acceptable results when tackling this problem. In this paper, we propose an adjustment of the recent guided fireworks algorithm from the class of swarm intelligence algorithms for locating the optimal path by unmanned combat aerial vehicle taking into consideration fuel consumption and safety degree. For experimental purposes, we compared it with eight different methods from the literature. Based on the experimental results, it can be concluded that our proposed approach is robust, exhibits better performance in almost all cases.

关键词： Unmanned combat aerial vehicle Path planning Swarm intelligence Metaheuristics fireworks algorithm

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