A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation

Yang, Xiaoxu; Liu, Jie; Liu, Yi; Xu, Peng; Yu, Ling; Zhu, Lei; Chen, Huayue

doi:10.3390/app112311192

Cited by 27 publications

(9 citation statements)

References 40 publications

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“…In the work of Yang [ 55 ], another adaptive SSA (CWTSSA) based on chaotic mapping mechanism, adaptive weighting strategy, and adaptive t-distribution mutation was proposed for global optimization. In CWTSSA, the chaotic mapping mechanism was used to construct the initial population in order to enhance population diversity.…”

Section: Recent Variants Of Ssamentioning

confidence: 99%

“…However, the other paths in the search space are not well explored. Several suggestions have been proposed to improve the diversity of the SSA such as chaotic mapping [ 27 , 55 , 56 ], inertia weight and cauchy-gaussian mutation [ 58 ], sin chaos search schema ([ 31 ]), lévy flight [ 31 , 59 ], reverse learning [ 11 ], opposition based learning [ 28 , 29 , 65 ], and random walk [ 23 , 70 ].…”

Section: Critical Analysis Of Ssamentioning

confidence: 99%

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Recent Versions and Applications of Sparrow Search Algorithm

Awadallah

Al-Betar

Doush

et al. 2023

Arch Computat Methods Eng

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This paper reviews the latest versions and applications of sparrow search algorithm (SSA). It is a recent swarm-based algorithm proposed in 2020 rapidly grew due to its simple and optimistic features. SSA is inspired by the sparrow living style of foraging and the anti-predation behavior of sparrows. Since its establishment, it has been utilized for a plethora of optimization problems in different research topics, such as mechanical engineering, electrical engineering, civil engineering, power systems, industrial engineering, image processing, networking, environment, robotics, planing and scheduling, and healthcare. Initially, the growth of SSA and its theoretical features are highlighted in terms of the number of published articles, citations, topics covered, etc. After that, the different extended versions of SSA are reviewed, where the main variations of SSA are produced to avoid premature convergence and to boost the diversity aspects. These extended versions are modifications and hybridization summarized with more focus on the motivations behind establishing these versions. Multi-objective SSA is also presented as another version to deal with Multi-objective optimization problems. The critical analysis of the main research gaps in the convergence behaviour of SSA is discussed. Finally, the conclusion and the possible future expansions are recommended based on the research works accomplished in the literature.

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Section: Recent Variants Of Ssamentioning

confidence: 99%

Section: Critical Analysis Of Ssamentioning

confidence: 99%

Recent Versions and Applications of Sparrow Search Algorithm

Awadallah

Al-Betar

Doush

et al. 2023

Arch Computat Methods Eng

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“…In order to improve these shortcomings, a multistrategy fusion mechanism is designed to improve the SSA. In here, the sine chaotic mapping strategy, dynamic adaptive weight strategy and t distribution mutation strategy are introduced to make up for the shortcomings of SSA (CWTSSA) to improve the global search and local development abilities of the SSA [55].…”

Section: Cwtssamentioning

confidence: 99%

Bearing fault diagnosis using transfer learning and optimized deep belief network

Zhao

Yang

Chan³

et al. 2022

Meas. Sci. Technol.

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Bearing is an important component in mechanical equipment. Its main function is to support the rotating mechanical body and reduce the friction coefficient and axial load. In the actual operating environment, the bearings are affected by complex working conditions and other factors. Therefore, it is very difficult to effectively obtain data that meets the conditions of independent and identical distribution of training data and test data, which result in unsatisfactory fault diagnosis results. As a transfer learning method, joint distribution adaptive (JDA) can effectively solve the learning problem of inconsistent distribution of training data and test data. In this paper, a new bearing fault diagnosis method based on JDA and deep belief network (DBN) with improved sparrow search algorithm (CWTSSA), namely JACADN is proposed. In the JACADN, the JDA is employed to carry out feature transfer between the source domain samples and target domain samples, that is, the source domain samples and target domain samples are mapped into the same feature space by the kernel function. Then the maximum mean difference is used as the metric to reduce the joint distribution difference between the samples in the two domains. Aiming at the parameter selection of the DBN, an improved sparrow search algorithm (CWTSSA) with global optimization ability is used to optimize the parameters of the DBN in order to construct an optimized DBN model. The obtained source domain samples and target domain samples are divided into training set and test set, which are input the optimized DBN to construct a bearing fault diagnosis model for improving the diagnosis accuracy. The effectiveness of the proposed method is verified by vibration data of QPZZ-II rotating machinery. The experimental results show that the proposed JACADN method can effectively improve the fault diagnosis accuracy of rolling bearings under variable operating conditions.

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“…Followers follow the explorer with the best fitness value to find food to gain their own energy reserves and increase their fitness value [ 23 , 24 ]. Some followers may also constantly monitor the explorer, compete for food, and alert when they are aware of the danger and move quickly to safe areas to get a better location, while sparrows in the middle of the population walk randomly close to other sparrows, known as anti-predatory behavior [ 25 , 26 ]. At the same time, if the alert value is greater than the security threshold, the explorer needs to take all followers out of the danger zone.…”

Section: Sparrow Search Algorithmmentioning

confidence: 99%

Membrane Fouling Prediction Based on Tent-SSA-BP

et al. 2022

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In view of the difficulty in obtaining the membrane bioreactor (MBR) membrane flux in real time, considering the disadvantage of the back propagation (BP) network in predicting MBR membrane flux, such as the local minimum value and poor generalization ability of the model, this article introduces tent chaotic mapping in the standard sparrow search algorithm (SSA), which improves the uniformity of population distribution and the searching ability of the algorithm (used to optimize the key parameters of the BP network). The tent sparrow search algorithm back propagation network (Tent-SSA-BP) membrane fouling prediction model is established to achieve accurate prediction of membrane flux; compared to the BP, genetic algorithm back propagation network (GA-BP), particle swarm optimization back propagation network (PSO-BP), sparrow search algorithm extreme learning machine(SSA-ELM), sparrow search algorithm back propagation network (SSA-BP), and Tent particle swarm optimization back propagation network (Tent–PSO-BP) models, it has unique advantages. Compared with the BP model before improvement, the improved soft sensing model reduces MAPE by 96.76%, RMSE by 99.78% and MAE by 95.61%. The prediction accuracy of the algorithm proposed in this article reaches 97.4%, which is much higher than the 48.52% of BP. It is also higher than other prediction models, and the prediction accuracy has been greatly improved, which has some engineering reference value.

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A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation

Cited by 27 publications

References 40 publications

Recent Versions and Applications of Sparrow Search Algorithm

Recent Versions and Applications of Sparrow Search Algorithm

Bearing fault diagnosis using transfer learning and optimized deep belief network

Membrane Fouling Prediction Based on Tent-SSA-BP

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