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Recognition of mixture control chart patterns based on fusion feature reduction and fireworks algorithm-optimized MSVM

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PATTERN ANALYSIS AND APPLICATIONS 2020年第1期23卷 15-26页

作者： Zhang, Min Yuan, Yi Wang, Ruiqi Cheng, Wenming Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China

Unnatural control chart patterns (CCPs) can be associated with the quality problems of the production process. It is quite critical to detect and identify these patterns effectively based on process data. Various machine learning techniques to CCPs recognition have been studied on the process only suffer from basic CCPs of unnatural patterns. Practical production process data may be the combination of two or more basic patterns simultaneously in reality. This paper proposes a mixture CCPs recognition method based on fusion feature reduction (FFR) and fireworks algorithm-optimized multiclass support vector machine (MSVM). FFR algorithm consists of three main sub-networks: statistical and shape features, features fusion and kernel principal component analysis feature dimensionality reduction, which make the features more effective. In MSVM classifier algorithm, the kernel function parameters play a very significant role in mixture CCPs recognition accuracy. Therefore, fireworks algorithm is proposed to select the two-dimensional parameters of the classifier. The results of the proposed algorithm are benchmarked with popular genetic algorithm and particle swarm optimization methods. Simulation results demonstrate that the proposed method can gain the higher recognition accuracy and significantly reduce the running time.

关键词： Control chart patterns recognition Multiclass support vector machines Fusion feature reduction fireworks algorithm Parameters optimization

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A Novel Hybrid algorithm Based on Grey Wolf Optimizer and fireworks algorithm

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SENSORS 2020年第7期20卷 2147页

作者： Yue, Zhihang Zhang, Sen Xiao, Wendong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Key Lab Knowledge Automat Ind Proc Sch Automat & Elect Engn Minist Educ Beijing 100083 Peoples R China

Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA.

关键词： Grey Wolf Optimizer fireworks algorithm hybrid algorithm exploitation and exploration

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Enhancing fireworks algorithm in Local Adaptation and Global Collaboration 12th

Enhancing Fireworks Algorithm in Local Adaptation and Global...

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12th International Conference on Swarm Intelligence (ICSI)

作者： Li, Yifeng 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)9783030787424;9783030787431

In order to improve the performance of fireworks algorithm, this paper carries out a comprehensive enhancement for its framework. Locally, the basic explosion operator is replaced by an efficient adaptation method in CMA-ES. Globally, the explosion range of all fireworks is effectively collaborated by search space partition. On the one hand, the proposed algorithm can quickly adapt to local landscape and improve the local exploitation efficiency significantly. On the other hand, it can collaborate the search ranges of multiple fireworks to form a seamless and non-overlapping partition of the search space, thereby ensuring the global search ability. Since the proposed framework evaluates one batch of a fixed large number of solutions in each iteration, it also achieves better computational efficiency in modern parallel hardware. The proposed algorithm is tested on the CEC 2020 benchmark functions with three different dimensions. The experimental results prove that those strategies improve fireworks algorithm significantly.

关键词： fireworks algorithm Swarm intelligence Optimization Collaboration

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Dynamic Search Hybrid fireworks algorithm 20th

Dynamic Search Hybrid Fireworks Algorithm

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20th International Conference on Intelligent Computing (ICIC)

作者： Feng, Kedi Li, Jun Yu, Senwu Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Peoples R China

ISBN: (纸本)9789819755776;9789819755783

In order to enhance the local exploitation and global search capability of the fireworks algorithm, this paper proposes a dynamic search hybrid fireworks algorithm (DHFWA). Firstly, the algorithm utilizes the adaptive changes in fireworks to update the explosion amplitude of the fireworks. When the adaptive value improves, the explosion amplitude is increased to enhance the algorithm's global search ability. Conversely, reducing the explosion amplitude improves the algorithm's local utilization ability;then, replace the Gaussian mutation operator with a bootstrap mutation operator to enhance the information exchange between sparks generated by the same fireworks;finally, using differential evolution instead of the original selection strategy enhances the information exchange between fireworks and improves the convergence performance of the algorithm. Tests were conducted on the CEC2017 benchmark suite, and the experimental results show that DHFWA significantly outperforms previous fireworks algorithms.

关键词： fireworks algorithm Dynamic Search Guided Sparks Differential Variation CEC2017

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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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Non-dominated Sorting Based fireworks algorithm for Multi-objective Optimization 13th

Non-dominated Sorting Based Fireworks Algorithm for Multi-ob...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Li, Mingze Tan, Ying Peking Univ Sch Artificial Intelligence Inst Artificial Intelligence Key Lab Machine Percept MOE Beijing Peoples R China

ISBN: (纸本)9783031096778;9783031096761

Multi-objective optimization is one of the most important problem in the mathematical optimization. Some researchers have already proposed several multi-objective fireworks algorithms, of which S-metric based multi-objective fireworks algorithm (S-MOFWA) is the most representative work. S-MOFWA takes the hypervolume as the evaluation criterion of external archive updating, which is easy to implement but ignores the landscape information of the population. In this paper, a novel multi-objective fireworks algorithm named non-dominated sorting based fireworks algorithm (NSFWA) is proposed. The proposed algorithm updates the external archive with the selection operator based on the fast non-dominated sorting approach, which is specially designed for the spark generation characteristic of FWA to improve the diversity. A multi-objective guided mutation operator is also designed to enhance the efficiency of population information utilization and improve the search capability of the algorithm. Experimental results on the benchmarks demonstrate that NSFWA outperforms other multi-objective swarm intelligence algorithms of S-MOFWA, NSGA-II and SPEA2.

关键词： fireworks algorithm Multi-objective optimization Swarm intelligence Non-dominated sorting based fireworks algorithm

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Signal Control of Urban Traffic Network Based on Multi-Agent Architecture and fireworks algorithm

Signal Control of Urban Traffic Network Based on Multi-Agent...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Qiao, Zhimin Ke, Liangjun Wang, Xiaoqiang Lu, Xin Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9781728121536

The application of multi-agent technology in urban traffic network control makes the traffic signal control has ability of adaptive adjustment. With the changing of traffic flow in the road, it can adjust the important parameters such as the offset, green ratio and public cycle of signal lights in real time, which can effectively reduce traffic congestion and improve the vehicle capacity of the urban traffic network. In this paper, a three level multi-agent control framework is used. The objective functions are the optimization model of green ratio delay, public cycle and offset delay. fireworks algorithm is used to solve the modeling optimization problem. The simulation results show that the adaptive traffic network signal control can significantly reduce the total delay time of the traffic network and improve the road utilization rate with the continuous change of traffic flow. Compared with the traditional traffic signal timing control, the adaptive traffic network signal control has great advantages and overcomes the disadvantages of traditional traffic signal control. At the same time, the fireworks algorithm shows significant performance in solving the optimizing model of the traffic network.

关键词： multi-agent signal control fireworks algorithm traffic network

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Multi-level Image Thresholding based on Improved fireworks algorithm 13

Multi-level Image Thresholding based on Improved Fireworks A...

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Ma, Miao Zheng, Weige Wu, Jie Yang, Kaifang Guo, Min Minist Educ Key Lab Modern Teaching Technol Xian Shaanxi Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9781538621653

Aiming at achieving the optimal multi-level thresholding quickly and effectively for the image segmentation, this paper proposes an improved fireworks algorithm based image segmentation method. The proposed method transforms the multi-level thresholding problem into a multivariate combinational optimization problem and then improved the fireworks algorithm for a better image segmentation. Meanwhile, the Otsu method is adopted as the fitness function, and the global search and the local search in the improved fireworks algorithm method is utilized to achieve multi-level thresholding concurrently and efficiently. The experimental results show that the improved fireworks algorithm based image segmentation method can significantly improve the segmentation efficiency comparing with other swarm intelligence algorithm based image segmentation methods.

关键词： multi-level image thresholding fireworks algorithm image segmentation the Otsu method

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Economic Dispatch Optimization for Microgrid Based on fireworks algorithm with Momentum 13th

Economic Dispatch Optimization for Microgrid Based on Firewo...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Li, Mingze Tan, Ying Peking Univ Sch Artificial Intelligence Inst Artificial Intelligence Key Lab Machine Percept MOE Beijing Peoples R China

ISBN: (纸本)9783031096778;9783031096761

As an efficient organization form of distributed energy resources with high permeability, microgrid (MG) is recognized as a promising technology with the promotion of various clean renewable sources. Due to uncertainties of renewable sources and load demands, optimizing the dispatch of controllable units in microgrid to reduce economic cost has become a critical issue. In this paper, an economic dispatch optimization model for microgrid including distributed generation and storage is established with the considering of inherent links between intervals, which aims to minimize the economic and environmental costs. In order to solve the optimization problem, a novel swarm intelligence algorithm called fireworks algorithm with momentum (FWAM) is also proposed. In the algorithm, the momentum mechanism is introduced into the mutation strategy, and the generation of the guiding spark is modified with the historical information to improve the searching capability. Finally, in order to verify the rationality and effectiveness of the proposed model and algorithm, a microgrid system is simulated with open data. The simulation results demonstrate FWAM lowers the economic cost of the microgrid system more effectively compared with other swarm intelligence algorithms such as GFWA and CMA-ES.

关键词： fireworks algorithm Swarm intelligence Microgrid Smart grid Economic dispatch

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Research on Radiator Structure Optimization Using fireworks algorithm Based on Elite Opposition-Based Learning 40

Research on Radiator Structure Optimization Using Fireworks ...

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40th Chinese Control Conference (CCC)

作者： He, Xiuzhu Wu, Yong Li, Jiange Wuhan Univ Technol Sch Automat Wuhan Peoples R China

ISBN: (纸本)9789881563804

Aiming at the shortcomings of the fireworks algorithm (FWA) that it is easy to mature prematurely and has low accuracy and weak global search ability, a novel improved FWA whose solution space search strategy based on Elite Opposition-Based Learning (EOBLFWA) is proposed. The optimization results of 4 sets of standard functions show that EOBLFWA has higher convergence speed and convergence accuracy for numerical optimization. In this paper, the proposed algorithm is used to optimize the structural parameters of radiator. The result shows that the temperature of the heat source on the surface of the optimized radiator is lowered by 4.8 degree compared with the original model, and the calculation results are verified by the ICEPAK software.

关键词： fireworks algorithm Elite Opposition-Based Learning Radiator structure ANSYS ICEPAK

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