Grey Wolf algorithm based on S-function and particle swarm optimization

Liu, ChenYang; Wang, Yongli

doi:10.1088/1742-6596/1453/1/012021

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…where n x is the individual of the xth iteration of population n; n new x is a new individual produced by n x after mutation operation; r 4 , r 5 , r 6 are random numbers between [0, 1]; X stands for iteration x; x max represents the maximum number of iterations of immune cloning operation; and η is a clonal variation parameter. It can be seen from formula (11) that the number of iterations is negatively correlated with the clone variation parameter η. η is close to 1 at the beginning, with a wide range of variation. At this point, a global range search is performed to ensure population diversity.…”

Section: Immune Clone Selection Operationmentioning

confidence: 99%

“…As for the research on risk assessment with supply chain financial business as a whole, scholars choose the assessment indicators of supply chain financial risk from the aspects of core enterprises, financing enterprises, financing projects, and supply chain operation [7]. At the same time, the methods of supply chain financial risk assessment mainly include objective methods, support vector machine (SVM), fuzzy comprehensive evaluation method, game model, entropy weight TOPSIS model [8][9][10][11]. Due to the different methods, indicators and samples used by scholars in the research of supply chain financial risk, there are some differences in the composition and evaluation results of supply chain financial risk.…”

Section: Introductionmentioning

confidence: 99%

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A K-Means Clustering Algorithm for Early Warning of Financial Risks in Agricultural Industry

Li¹,

Duan²

2022

Security and Communication Networks

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As the primary industry, agricultural industry is the basis of guaranteeing people’s basic life and national economic development. Agricultural industrial finance/financial is a weak link in the financial system, which seriously hinders the emergence of agricultural scale effect and the improvement in agricultural production efficiency. In order to find the financial risk of agricultural industry in time, this article proposes an agricultural industry financial risk early warning system based on improved K-means clustering algorithm. Because the traditional K-means algorithm is easy to fall into local optimization in the clustering process, the clustering effect is not reliable. In this article, the idea of immune cloning and particle swarm optimization location update is added to the grey wolf optimization algorithm. Grey wolf optimization algorithm and K-means algorithm are combined to solve the problem that K-means algorithm is easy to fall into local optimization. In the experimental part, through comparative verification, it can be found that the prediction performance of this system is superior to other models. Its practical application value is higher. Therefore, choosing this system for early warning of agricultural industry finance can effectively improve the accuracy of early warning and provide guarantee for the economic development of agricultural industry.

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Section: Immune Clone Selection Operationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A K-Means Clustering Algorithm for Early Warning of Financial Risks in Agricultural Industry

Li¹,

Duan²

2022

Security and Communication Networks

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“…When the output result is different from the expected value, the principle of backpropagation is used to optimize. The threshold and weight of the gray wolf algorithm are used as the weight and threshold of the RBNNA algorithm ( Liu and Wang, 2020 ). The relative error value between the predicted and true value of soil nutrient content is used as the fitness value.…”

Section: Algorithms and Models Designmentioning

confidence: 99%

A method for durian precise fertilization based on improved radial basis neural network algorithm

Tang,

Wei,

Jianxun

et al. 2024

Front. Plant Sci.

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IntroductionDurian is one of the tropical fruits that requires soil nutrients in its cultivation. It is important to understand the relationship between the content of critical nutrients, such as nitrogen (N), phosphorus (P), and potassium (K) in the soil and durian yield. How to optimize the fertilization plan is also important to the durian planting.MethodsThus, this study proposes an Improved Radial Basis Neural Network Algorithm (IM-RBNNA) in the durian precision fertilization. It uses the gray wolf algorithm to optimize the weights and thresholds of the RBNNA algorithm, which can improve the prediction accuracy of the RBNNA algorithm for the soil nutrient content and its relationship with the durian yield. It also collects the soil nutrients and historical yield data to build the IM-RBNNA model and compare with other similar algorithms.ResultsThe results show that the IM-RBNNA algorithm is better than the other three algorithms in the average relative error, average absolute error, and coefficient of determination between the predicted and true values of soil N, K, and P fertilizer contents. It also predicts the relationship between soil nutrients and yield, which is closer to the true value.DiscussionIt shows that the IM-RBNNA algorithm can accurately predict the durian soil nutrient content and yield, which is benefited for farmers to make agronomic plans and management strategies. It uses soil nutrient resources efficiently, which reduces the environmental negative impacts. It also ensures that the durian tree can obtain the appropriate amount of nutrients, maximize its growth potential, reduce production costs, and increase yields.

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“…Zhang et al [5] proposed a multi-objective capacity optimization method for the PV-solar thermal the system, based on the optimal target of economy and stability performance, the PV capacity in the system was optimized. Liu et al [6] studied the optimization of PV capacity in the combined cooling and thermal power supply system, and the economic performance of the system was compared and analyzed with different PV capacities. Li et al [7]integrated different PV power plants into a system and optimized the PV capacity from the aspects of initial investment cost, and operation and maintenance cost.…”

Section: Introductionmentioning

confidence: 99%

Research on optimization of photovoltaic capacity in the multi-energy complementary power generation system

Zhang

Wei

Li³

et al. 2023

J. Phys.: Conf. Ser.

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Multi-energy complementary power generation (MEPG) technology is one of the effective utilization means of renewable energy generation. In this paper, a MEPG system is proposed, which includes a photovoltaic (PV) subsystem and ISCC (solar integrated combined cycle) subsystem. To optimize the capacity of the PV subsystem, the dynamic simulation of the MEPG system is carried out under the condition of operating for 2 hours continuously. By stabilizing the output power of the MEPG system at the target value during the operation, the relatively suitable capacity of the PV subsystem is obtained. The results show that for the MEPG system, the PV subsystem should be less than the capacity of the solar field in the ISCC subsystem.

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Grey Wolf algorithm based on S-function and particle swarm optimization

Cited by 5 publications

References 4 publications

A K-Means Clustering Algorithm for Early Warning of Financial Risks in Agricultural Industry

A K-Means Clustering Algorithm for Early Warning of Financial Risks in Agricultural Industry

A method for durian precise fertilization based on improved radial basis neural network algorithm

Research on optimization of photovoltaic capacity in the multi-energy complementary power generation system

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