Regression prediction of material grinding particle size based on improved sparrow search algorithm to optimize BP neural network

Zhang, Shida; Zhang, Jingyu; Wang, Zehua; Li, Quanhu

doi:10.1109/isceic53685.2021.00052

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…Tang et al developed a hierarchy and Brownian motion strategy to improve information communication between individuals; maintaining sparrow diversity involves upgrading improved sparrow placements [37]. Zhang et al introduced a Corsi variation strategy for solving the optimum local problem using a tent mapping initialization of the population [38], effectively enhancing the algorithm's search capability. A chaotic mapping method was used by Chen et al to determine the location of sparrow populations, and the Lévy flight and random wandering strategies were introduced [39], which effortfully optimized algorithm efficiency and exploration capabilities.…”

Section: Improving the Search Mechanism Of The Algorithmmentioning

confidence: 99%

A Multi-Strategy Crazy Sparrow Search Algorithm for the Global Optimization Problem

Jiang,

Wang,

Guo

et al. 2023

Electronics

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A multi-strategy crazy sparrow search algorithm (LTMSSA) for logic-tent hybrid chaotic maps is given in the research to address the issues of poor population diversity, slow convergence, and easily falling into the local optimum of the sparrow search algorithm (SSA). Firstly, the LTMSSA employs an elite chaotic backward learning strategy and an improved discoverer-follower ratio factor to improve the population’s quality and diversity. Secondly, the LTMSSA updates the positions of discoverers and followers by the crazy operator and the Lévy flight strategy to expand the selection range of target following individuals. Finally, during the algorithm’s optimization search, the LTMSSA introduces the tent hybrid and Corsi variable perturbation strategies to improve the population’s ability to jump out of the local optimum. Different types and dimensions of test functions are used as performance benchmark functions to test the performance of the LTMSSA with SSA variants and other algorithms. The simulation results show that the LTMSSA can jump out of the optimal local solution, converge faster, and have higher accuracy. Its overall performance is better than the other seven algorithms, and the LTMSSA can find smaller optimal values than other algorithms in the welded beam and reducer designs. The results confirm that the LTMSSA is an effective aid for computationally complex practical tasks, provides high-quality solutions, and outperforms other algorithms.

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Section: Improving the Search Mechanism Of The Algorithmmentioning

confidence: 99%

A Multi-Strategy Crazy Sparrow Search Algorithm for the Global Optimization Problem

Jiang,

Wang,

Guo

et al. 2023

Electronics

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show abstract

“…At present, the widely used chaotic map is Logistic chaotic map, but some scholars have proved that Tent map has better ergodicity, uniformity and faster iteration speed than Logistic map ( Zhang et al, 2021 ). Therefore, Tent mapping is quoted to BCO in this article, which is named improved BCO and denotes as IBCO.…”

Section: Related Workmentioning

confidence: 99%

Strip thickness prediction method based on improved border collie optimizing LSTM

Sun

Lin²,

Zhou

et al. 2022

PeerJ Computer Science

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Background The thickness accuracy of strip is an important indicator to measure the quality of strip, and the control of the thickness accuracy of strip is the key for the high-quality strip products in the rolling industry. Methods A thickness prediction method of strip based on Long Short-Term Memory (LSTM) optimized by improved border collie optimization (IBCO) algorithm is proposed. First, chaotic mapping and dynamic weighting strategy are introduced into IBCO to overcome the shortcomings of uneven initial population distribution and inaccurate optimization states of some individuals in Border Collie Optimization (BCO). Second, Long Short-Term Memory (LSTM) which can effectively deal with time series data and alleviate long-term dependencies is adopted. What’s more, IBCO is utilized to optimize parameters to mitigate the influence of hyperparameters such as the number of hidden neurons and learning rate on the prediction accuracy of LSTM, so IBCO-LSTM is established. Results The experiments are carried out on the measured strip data, which proves the excellent prediction performance of IBCO-LSTM. The experiments are carried out on the actual strip data, which prove that IBCO-LSTM has excellent capability of prediction.

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“…In order to obtain larger data volume, better data utilization and higher proxy accuracy, Li et al [34] proposed a new evolutionary algorithm framework. For the problem of complex material grinding factors and difficulty in accurately predicting yield particle size, reference [35] introduced a chaotic initialization population to promote the global search ability. At the same time, the Cauchy mutation strategy was introduced to solve the local optimal problem, effectively improving the algorithm's search ability.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Modeling Method for Aluminum Smelting Process Based on a Hybrid Strategy-Based Sparrow Search Algorithm

et al. 2022

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In the production of aluminum, the regenerative aluminum smelting process is an important part for energy efficiency and product quality. Aluminum liquid temperature is a significant variable in the aluminum smelting process, and it is costly to measure timely because it requires protective temperature sensor. To handle this problem, a kind of modeling framework which combine a mechanism model with multi-scale kernel technique is proposed. First, the mechanism model is built for the aluminum liquid temperature by the energy conservation law and heat transfer mechanism. Since the mechanism model is based on some assumptions, it often results in unknown variables. Thus, the multi-scale kernel technique is used to obtain the unknown variables. Finally, a hybrid temperature prediction model is built by combining the multi-scale kernel and the mechanism model. The parameter identification of the hybrid model is described as an optimization problem, and a hybrid strategy-based sparrow search algorithm (HSSA) is proposed to solve this problem. The experiment results show that HSSA has higher convergence accuracy and stronger global search ability than the original sparrow search algorithm (SSA), and the proposed hybrid model can correctly estimate the aluminum liquid temperature.

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Regression prediction of material grinding particle size based on improved sparrow search algorithm to optimize BP neural network

Cited by 7 publications

References 4 publications

A Multi-Strategy Crazy Sparrow Search Algorithm for the Global Optimization Problem

A Multi-Strategy Crazy Sparrow Search Algorithm for the Global Optimization Problem

Strip thickness prediction method based on improved border collie optimizing LSTM

A Hybrid Modeling Method for Aluminum Smelting Process Based on a Hybrid Strategy-Based Sparrow Search Algorithm

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