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检索条件"主题词=Fireworks Algorithm"

共 265 条记录，以下是71-80 订阅

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Parallel Dynamic Search fireworks algorithm with Linearly Decreased Dimension Number Strategy for Solving Conditional Nonlinear Optimal Perturbation

Parallel Dynamic Search Fireworks Algorithm with Linearly De...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Mu, Bin Zhao, Junhui Yuan, Shijin Yan, Jinghao Tongji Univ Sch Software Engn Shanghai Peoples R China

ISBN: (纸本)9781509061822

Intelligent algorithms have been applied extensively to solving conditional nonlinear optimal perturbation (CNOP), which is considered as an effective approach in the study of weather and climate predictability. Recent studies reveal that single particle intelligent algorithms could solve CNOP efficiently while their results are unstable. Conversely, swarm intelligent algorithms can get similar CNOP to adjoint method stably but still show lower time efficiency. For the purpose of balancing between efficiency and stability, in this paper, we introduce a linearly decreased dimension number strategy for dynamic search fireworks algorithm (dynFWA), called ld-dynFWA, and apply it to solving CNOP. Meanwhile, to accelerate the computation speed, we parallelize the ld-dynFWA with MPI. To demonstrate the validity, we take Zebiak-Cane (ZC) numerical model as a case to study ENSO event. Compared with CTS-SS and MABC, which are the latest research on single particle intelligent algorithm and swarm intelligent algorithm for solving CNOP respectively, experimental results show that the proposed parallel ld-dynFWA can obtain a better CNOP efficiently and stably.

关键词： fireworks algorithm CNOP MPI Zebiak-Cane model

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Solving the MAX-SAT Problem by Binary Enhanced fireworks algorithm 6

Solving the MAX-SAT Problem by Binary Enhanced Fireworks Alg...

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6th International Conference on Innovative Computing Technology (INTECH)

作者： Ali, Hafiz Munsub Lee, Daniel C. Simon Fraser Univ Sch Engn Sci Burnaby BC Canada

ISBN: (纸本)9781509020003

In this paper, we present a binary enhanced firework algorithm (BEFWA) for optimization problems with a search space of binary vectors, and we test this new algorithm for the MAX-SAT problem. The EFWA algorithm is a relatively recent development in swarm intelligence (SI) for continuous optimization, and the explosion amplitude operator in EFWA does not fit for searching a good solution in a discrete binary space. The original ABC algorithm is also not suitable for searching through a binary space, but its adaptation, the discrete ABC (DisABC), for a binary space was recently presented. In the present paper, we employ the similarity-measure-based differential expression from DisABC to design the binary EFWA algorithm to operate in binary space. The MAX-SAT is a well-known modelling framework for various computationally challenging problems, and thus it has many applications. However, the MAX-SAT problem has been proven to be NP-hard. Existing results indicate that evolutionary algorithms (EAs) can be useful for finding goodquality solutions without excessive computational resources. Our experimental results demonstrate that the binary EFWA can be a better choice over DisABC and Genetic algorithm (GA) for various classes of MAX-SAT instances.

关键词： swarm intelligence fireworks algorithm similarity measure MAX-SAT

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An Advanced fireworks algorithm for Distribution Network Reconfigurations

An Advanced Fireworks Algorithm for Distribution Network Rec...

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2017 IEEE Power & Energy Society General Meeting

作者： Mori, Hiroyuki Ikegami, Hiromitsu Meiji Univ Dept Network Design Nakano Ku Tokyo 1618525 Japan

ISBN: (纸本)9781538622124

This paper proposes an advanced metaheuristic method for distribution network reconfigurations. It is useful for minimizing the active network losses by switching on/off sectionalizing switches and shunt capacitors. The mathematical formulation may be expressed as one of combinational optimization problems. In this paper a discrete version of CoFFWA (Cooperative Framework for fireworks algorithm) of advanced metaheuristics is proposed to solve distribution network reconfigurations efficiently. CoFFWA is one of efficient fireworks algorithms that make use of the idea of swarm intelligence through the independent selection method. The proposed method is successfully applied to sample distribution networks.

关键词： network reconfigurations meta-heuristics global optimization fireworks algorithm CoFFWA

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An Empirical Study on Influence of Approximation Approaches on Enhancing fireworks algorithm

An Empirical Study on Influence of Approximation Approaches ...

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

作者： Pei, Yan Zheng, Shaoqiu Tan, Ying Takagi, Hideyuki Kyushu Univ Grad Sch Design Fukuoka 8158540 Japan Peking Univ Sch Elect Engn & Comp Sci Dept Machine Intelligence Beijing Peoples R China Peking Univ Minst Educ Key Lab Machine Percept Beijing 100871 Peoples R China Kyushu Univ Fac Design Fukuoka 8158540 Japan

ISBN: (纸本)9781467317146

This paper presents an empirical study on the influence of approximation approaches on accelerating the fireworks algorithm search by elite strategy. In this study, we use three sampling data methods to approximate fitness landscape, i.e. the best fitness sampling method, the sampling distance near the best fitness individual sampling method and the random sampling method. For each approximation methods, we conduct a series of combinative evaluations with the different sampling method and sampling number for accelerating fireworks algorithm. The experimental evaluations on benchmark functions show that this elite strategy can enhance the fireworks algorithm search capability effectively. We also analyze and discuss the related issues on the influence of approximation model, sampling method, and sampling number on the fireworks algorithm acceleration performance.

关键词： fireworks algorithm Elite Strategy Fitness Landscape Approximation Acceleration

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Optimal Choice of Parameters for fireworks algorithm 4th

Optimal Choice of Parameters for Fireworks Algorithm

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4th International Conference on Eco-Friendly Computing and Communication Systems (ICECCS)

作者： Kumar, Vijay Chhabra, Jitender Kumar Kumar, Dinesh Thapar Univ Comp Sci & Engn Dept Patiala 147004 Punjab India Natl Inst Technol Comp Engn Dept Kurukshetra Haryana India GJUS&T Comp Sci & Engn Dept Hisar Haryana India

The fireworks algorithm (FWA) is a relatively novel metaheuristic algorithm based on simulating the flare-up process of fireworks exploding and illuminating the night sky. The effects of major parameters on FWA were investigated on the unimodal and multimodal benchmark functions. The parameters such as number of fireworks, explosion amplitude, number of explosion sparks and number of iterations, have significant impact on the performance of FWA. The inappropriate selection of these parameters may lead to wrong results. The characteristics of FWA's parameters are depicted and general recommendation for deciding the optimal parameter values is given in this paper. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/)

关键词： fireworks algorithm Optimization Metaheuristic Parameter Selection

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A GPU-based Parallel fireworks algorithm for Optimization 13

A GPU-based Parallel Fireworks Algorithm for Optimization

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15th Genetic and Evolutionary Computation Conference (GECCO)

作者： Ding, Ke Zheng, Shaoqiu Tan, Ying Peking Univ Sch Elect Engn & Comp Sci Dept Machine Intelligence Key Lab Machine Percept MOE Beijing 100871 Peoples R China

ISBN: (纸本)9781450319638

Swarm intelligence algorithms have been widely used to solve difficult real world problems in both academic and engineering domains. Thanks to the inherent parallelism, various parallelized swarm intelligence algorithms have been proposed to speed up the optimization process, especially on the massively parallel processing architecture GPUs. However, conventional swarm intelligence algorithms are usually not designed specifically for the GPU architecture. They neither can fully exploit the tremendous computational power of GPUs nor can extend effectively as the problem scales go large. To address this shortcoming, a novel GPU-based fireworks algorithm (GPU-FWA) is proposed in this paper. In order to fully leverage GPUs' high performance, GPU-FWA modified the original FWA so that it is more suitable for the GPU architecture. An implementation of GPU-FWA on the CUDA platform is presented and then tested on a suite of well-known benchmark optimization problems. We extensively evaluated GPU-FWA and compared it with FWA and PSO, with respect to both running time and solution quality, on a state-of-the-art commodity Fermi GPU. Experimental results demonstrate that GPU-FWA generally outperforms both FWA and PSO, and enjoys a significant speedup as high as 200x, compared to the sequential version of FWA and PSO running on an up-to-date CPU. GPU-FWA also enjoys the advantages of being easy to implement and scalable.

关键词： Swarm Intelligence fireworks algorithm Parallel Computing GPU computing CUDA

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Performance Analysis of the fireworks algorithm Versions 12th

Performance Analysis of the Fireworks Algorithm Versions

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12th International Conference on Swarm Intelligence (ICSI)

作者： Tuba, Ira Strumberger, Ivana Tuba, Eva Bacanin, Nebojsa Tuba, Milan Singidunum Univ Danijelova 32 Belgrade 11000 Serbia

ISBN: (纸本)9783030787424;9783030787431

In the last decades, swarm intelligence algorithms have become a powerful tool for solving hard optimization problems. Nowadays numerous algorithms are proved to be good for different problems. With the overwhelming number of algorithms, it became hard for a common user to choose an appropriate method for solving a certain problem. To provide guidelines, it is necessary to classify optimization metaheuristics according to their capabilities. Deep statistical comparison represents a novel method for comparing and analyzing optimization algorithms. In this paper, the deep statistical comparison method was used for comparing different versions of the widely used fireworks algorithm. The fireworks algorithm was developed and improved in the last ten year, and this paper provides a theoretical analysis of five different versions, a cooperative framework for FWA, bare bones FWA, guided FWA, loser-out tournament based FWA, and dynamic search FWA. Based on the obtained results, the loser-out tournament based FWA has the best performance in the term of the solution quality, while the dynamic search FWA is the best in term of the solutions distribution in the search space.

关键词： Swarm intelligence fireworks algorithm Deep statistical comparison Performance analysis

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Financial Early Warning of Listed Companies Based on fireworks algorithm Optimized Back-Propagation Neural Network 10

Financial Early Warning of Listed Companies Based on Firewor...

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10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

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

ISBN: (纸本)9781728140681

Aiming at the problems of slow convergence speed, easy to fall into local optimum and poor robustness based on BP neural network (Back-Propagation), a fireworks algorithm is proposed as a pre-device of neural network model to optimize the initial value and threshold of the network input, and then The error is corrected by the BP neural network algorithm to obtain an optimal value. This method improves the prediction accuracy of the network. The empirical research shows that the accuracy of the financial distress prediction of the optimization model is higher than that of the traditional BP neural network model.

关键词： financial risk warning BP neural network fireworks algorithm

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Research on Optimized Reconfiguration of Distribution Network Based on Improved fireworks algorithm 5

Research on Optimized Reconfiguration of Distribution Networ...

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5th Asia Conference on Power and Electrical Engineering (ACPEE)

作者： Shi, Yangtao Yan, Yuehao Liu, Yi Zhengzhou Univ Sch Elect Engn Zhengzhou Peoples R China State Grid Henan Elect Power Co Zhengzhou Power Supply Co Zhengzhou Peoples R China State Grid Henan Elect Power Co Maintenance Co Zhengzhou Peoples R China

ISBN: (数字)9781728152813

ISBN: (纸本)9781728152813

Aiming at the problem of low operating efficiency of traditional fireworks algorithm in distribution network optimization and reconstruction, some improved strategies are proposed. The algorithm takes the minimum loss of distribution network as the fitness function, the mapping rules of the traditional fireworks algorithm are improved and optimized, and the sparks are double mapped, which improves the optimization speed of the algorithm. By improving the operation, the population diversity of the evolutionary algorithm is improved, so the accuracy and speed of the algorithm are improved. Through the simulation analysis of the IEEE33 node system, it is proved that the improved fireworks algorithm can get the optimal result of distribution network structure, and can adapt to the optimal reconfiguration of the distribution network. Through the example simulation of Zhengzhou distribution network, the practical application of the algorithm is verified.

关键词： distribution network reconfiguration fireworks algorithm mapping rules minimum network loss

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A New Hybrid algorithm based on PSO and fireworks algorithm for Optimal Design of Metasurface Absorber in RF Energy Harvesting 21

A New Hybrid Algorithm based on PSO and Fireworks Algorithm ...

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IEEE 21st Biennial Conference on Electromagnetic Field Computation (IEEE CEFC)

作者： Chen, Na Yang, Shiyou An, Siguang Zhejiang Univ Coll Elect Engn Hangzhou Peoples R China China Jiliang Univ Dept Elect Engn Hangzhou Peoples R China

ISBN: (纸本)9798350348958;9798350348965

To tackle the nonlinear, multimodal, and computationally expensive challenges inherent in the optimal design of RF energy harvesters, an innovative hybrid algorithm combining particle swarm optimization and fireworks algorithm is proposed. In the proposed hybrid algorithm, 1) a stretching-and-ejection mechanism is developed to balance exploration and exploitation searches, 2) a self-adaptive inertia weight is introduced to enhance the convergence rate, 3) an elite-preservation strategy is employed to guarantee a global convergence. Also, a new topology of a wideband and dual-polarized metasurface absorber for RF energy harvesting applications is introduced. The feasibilities of the presented work in RFEH applications and design optimizations are validated using a case study.

关键词： design optimization fireworks algorithm hybrid algorithm metasurfaces particle swarm optimization

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