Fast stochastic configuration network based on an improved sparrow search algorithm for fire flame recognition

Wu, Hao; Zhang, Aihua; Han, Ying; Nan, Juan; Li, Kun

doi:10.1016/j.knosys.2022.108626

Cited by 47 publications

(12 citation statements)

References 67 publications

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“…This improved algorithm outperformed other algorithms through experimental comparison in terms of accuracy, optimization speed, and stability [16]. Other similar SSA variants and related applications could be found in [17][18][19][20][21][22]. Details of some of the above mentions of SSA improvements are shown in Table 1.…”

Section: Introductionmentioning

confidence: 78%

An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

Zhang

Zhou

et al. 2022

IEEE Access

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Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optimal easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, both the solution accuracy and convergence speed are higher than other algorithms. It further indicates that the ICSSOA has an outstanding ability to jump out of the local optimum.INDEX TERMS Sparrow search algorithm, cubic chaos mapping, adaptive weighting modification, levy flight, reverse learning.

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Section: Introductionmentioning

confidence: 78%

An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

Zhang

Zhou

et al. 2022

IEEE Access

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“…The stochastic configuration network (SCN) is a powerful class of stochastic learning models with a stronger generalization performance than traditional stochastic learning models, as its hidden layer structure can be generated adaptively based on training effects [34]. The basic idea is to start with a smaller network and then gradually add new hidden nodes with random parameters until an acceptable tolerance is reached.…”

Section: Regularised Random Configuration Networkmentioning

confidence: 99%

Fault Diagnosis Method for Power Transformers Based on Improved Golden Jackal Optimization Algorithm and Random Configuration Network

Shi

et al. 2023

IEEE Access

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The problem of the low accuracy of Dissolved Gas Analysis (DGA) in diagnosing transformer faults is addressed by proposing an Improved Golden Jackal Optimization (IGJO) based Stochastic Configuration Network (SCN) method. The method of transformer fault diagnosis based on IGJO optimized SCN is proposed. Firstly, Kernel Principal Component Analysis (KPCA) is used to reduce the dimensionality of the gas data and extract the effective feature quantities. Secondly, the L2 parametric penalty term is introduced into the SCN to improve the generalisation ability of SCN in practical applications. The elite backward learning and golden sine algorithms are incorporated into the golden jackal algorithm, and the IGJO performance is tested using 13 typical test functions, demonstrating that the IGJO has greater stability and merit-seeking capability. The penalty term coefficient C of the SCN is optimised using the IGJO to develop a transformer fault diagnosis model with an Improved Golden Jackal algorithm optimised Random Configuration Network (IGJO-SCN). Finally, the feature quantities extracted by KPCA are used as the input set of the model and the different transformer fault diagnosis models are simulated and validated. The results show that the IGJO-SCN has higher transformer fault diagnosis accuracy compared to other models.

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“…To further test the optimized performance of the CMASSA, including Particle Swarm Optimization (PSO) [ 26 ], Harris Hawks Optimization (HHO) [ 27 ], Multi-Verse Optimizer (MVO) [ 28 ], Improved Sparrow Search Algorithm (ISSA) [ 14 ], Sparrow Fusion with Firefly Algorithm Search Algorithm (ESSA) [ 16 ], the optimization comparison is performed on the benchmark functions in Table 1 .…”

Section: Simulationmentioning

confidence: 99%

“…However, the sparrow algorithm still has some limitations in aspects such as population initialization and location update strategy, resulting in the inconsistency of global search ability and local optimization ability and a weak ability to jump out of local optima. Wu et al [ 14 ] proposed an improved Sparrow Search Algorithm (ISSA) to optimize some parameters in a Fast Random Configuration Network (FSCN) to make it have better classification performance. Liu et al [ 15 ] proposed an improved SSA called CASSA to solve the UAV route planning problem.…”

Section: Introductionmentioning

confidence: 99%

Application of Chaos Mutation Adaptive Sparrow Search Algorithm in Edge Data Compression

Qiu¹,

Li²

2022

Sensors

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In view of the large amount of data collected by an edge server, when compression technology is used for data compression, data classification accuracy is reduced and data loss is large. This paper proposes a data compression algorithm based on the chaotic mutation adaptive sparrow search algorithm (CMASSA). Constructing a new fitness function, CMASSA optimizes the hyperparameters of the Convolutional Auto-Encoder Network (CAEN) on the cloud service center, aiming to obtain the optimal CAEN model. The model is sent to the edge server to compress the data at the lower level of edge computing. The effectiveness of CMASSA performance is tested on ten high-dimensional benchmark functions, and the results show that CMASSA outperforms other comparison algorithms. Subsequently, experiments are compared with other literature on the Multi-class Weather Dataset (MWD). Experiments show that under the premise of ensuring a certain compression ratio, the proposed algorithm not only has better accuracy in classification tasks than other algorithms but also maintains a high degree of data reconstruction.

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Fast stochastic configuration network based on an improved sparrow search algorithm for fire flame recognition

Cited by 47 publications

References 67 publications

An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

Fault Diagnosis Method for Power Transformers Based on Improved Golden Jackal Optimization Algorithm and Random Configuration Network

Application of Chaos Mutation Adaptive Sparrow Search Algorithm in Edge Data Compression

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