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Implementation method for improved GWO (Gray Wolf Optimization) algorithm

A technology for implementing methods and algorithms, applied in genetic rules, gene models, etc., can solve problems such as prematurity, poor stability, and falling into local optimum

Inactive Publication Date: 2018-09-07
JIANGSU UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0002] The new bionic intelligent GWO algorithm proposed by Mirjalili in 2014 is widely used in the field of neural network optimization, but it is also prone to premature, poor stability, and easy to fall into local optimum when solving optimization problems alone.

Method used

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  • Implementation method for improved GWO (Gray Wolf Optimization) algorithm
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  • Implementation method for improved GWO (Gray Wolf Optimization) algorithm

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Embodiment 1

[0023] Supercapacitor is a new type of energy storage device between traditional physical electrolytic capacitors and batteries. It has the advantages of high power density, long cycle life, wide temperature range and environmental protection. It is widely used in the field of new energy vehicles. Therefore, accurately predicting the state of charge (SOC) of supercapacitors in new energy vehicles is one of the core functions of on-board energy management systems. The extreme learning machine (extreme learning machine, ELM) algorithm is a single hidden layer forward neural network, the network structure is simple, the learning speed is fast, and the generalization performance is good. Using the Moore-Penrose generalized inverse to solve the network weight, the input can be randomly generated The connection weights between layers and hidden layers and the threshold of hidden layer neurons do not need to be adjusted during the training process. Only the number of hidden layer neur...

Embodiment 2

[0077] Accurately predicting the state of charge (SOC) of the on-board battery of new energy vehicles is one of the core functions of the power battery management system. The extreme learning machine (extreme learning machine, ELM) algorithm is a single hidden layer forward neural network, the network structure is simple, the learning speed is fast, and the generalization performance is good. Using the Moore-Penrose generalized inverse to solve the network weight, the input can be randomly generated The connection weights between layers and hidden layers and the threshold of hidden layer neurons do not need to be adjusted during the training process. Only the number of hidden layer neurons can be set to obtain the only optimal solution, which is very suitable for Prediction of SOC for on-board batteries. However, the influence of the selection of the weight w of the input layer of the ELM on the performance of the ELM is difficult to find an inevitable correspondence in theory...

Embodiment 3

[0131] At present, the user-side micro-grid with residential quarters, commercial buildings, and industrial factories as the main body has become an effective way to promote the local consumption and utilization of renewable energy and give full play to the efficiency of distributed power. Short-term load forecasting is an important part of the user-side microgrid energy management system and the basis for optimal dispatching of the microgrid. The forecast results will directly affect the microgrid operation strategy and power trading. Relevant studies have shown that higher microgrid load forecast errors will lead to a substantial increase in operating costs. Compared with the large power grid environment, short-term load forecasting for microgrids is more difficult, mainly due to the strong randomness of loads, the low similarity of historical load curves, and the limited capacity of users, and the smooth interaction of load characteristics between users. Small, the overall ...

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Abstract

The invention discloses an implementation method for an improved GWO (Gray Wolf Optimization) algorithm. The method comprises the following steps that: (1) initializing a population scale, a maximum iteration frequency, a search dimension and range and a control coefficient; (2) importing the backward learning method of a Logistic chaotic mapping strategy to initialize a grey wolf population; (3)updating a convergence factor, calculating the fitness of each individual in the population, carrying out sorting, carrying out Cauchy variation on the optimal individual, and selecting [Alpha], [Beta] and [Delta] wolves; (4) calculating a constant swinging and convergence factor; (5) updating the individual position of each grey wolf, and determining the position of a prey; (6) skipping to (3) until the maximum iteration frequency is achieved; and (7) outputting the position of the [Alpha] wolf. In order to improve the performance of the GWO algorithm, the GWO algorithm is improved, and the backward learning method of the Logistic chaotic mapping strategy initializes the grey wolf population to guarantee that initial positions are evenly distributed; the convergence factor is improved, and a local optimal solution can be accurately searched; the Cauchy variation increases algorithm searching ability and quickens convergence speed.

Description

technical field [0001] The invention relates to a method for realizing an improved gray wolf optimization (GWO) algorithm. Background technique [0002] The new bionic intelligent GWO algorithm proposed by Mirjalili in 2014 is widely used in the field of neural network optimization, but it is also prone to shortcomings such as premature maturity, poor stability, and easy to fall into local optimum when solving optimization problems alone. Contents of the invention [0003] In order to solve the above defects, the present invention provides an implementation method of an improved GWO algorithm. [0004] To achieve the above object, the present invention adopts the following technical solutions: [0005] A kind of implementation method of improving GWO algorithm, it comprises the following steps: [0006] Step 1, initialize the population size N, the maximum number of iterations t max , search dimension D, search range [lb,ub], control coefficient k; [0007] Step 2, usi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/12
CPCG06N3/126
Inventor 王琪韩晓新沃松林罗印升
Owner JIANGSU UNIV OF TECH
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