Optimization method based on improved sparrow search algorithm

A technology of search algorithm and optimization method, which is applied in the field of racing, can solve the problems of premature algorithm, inability to overcome the premature algorithm of swarm intelligence optimization algorithm, poor robustness, etc., and achieve the effect of high optimization accuracy, retain local search ability, and improve robustness

Pending Publication Date: 2021-06-18
ZHEJIANG UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

Although the swarm intelligence optimization algorithm has the characteristics of strong robustness and wide application range compared with the traditional optimization algorithm, it is easy to fall into the local optimal solution, which leads to the problem of premature algorithm
[0003] The Sparrow Search Algorithm (SSA) was proposed by Xue Jiankai et al. in 2020. Compared with the traditional optimization algorithm and the traditional swa

Method used

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  • Optimization method based on improved sparrow search algorithm
  • Optimization method based on improved sparrow search algorithm
  • Optimization method based on improved sparrow search algorithm

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Embodiment

[0024] Embodiment: Based on the optimization method of the improved sparrow search algorithm, the sparrow search algorithm is a kind of discoverer-follower model by imitating the sparrow foraging for food, and an early warning detection is added on the basis of the discoverer-follower model mechanism. The sparrow search algorithm divides the sparrows into discoverers, followers who rob the discoverers of food, and followers who need to search extensively because of hunger. After updating the discoverers and followers in turn, the sparrows who are aware of the danger are randomly selected from the population. Sparrow and update. This embodiment improves the sparrow search algorithm, such as figure 1 shown, proceed as follows:

[0025] 1) Initialize the population;

[0026] 2) According to the formula update finder;

[0027] In the formula: t represents the current iteration number, Indicates the position of the i-th sparrow individual at the t-th iteration; ChiSquare(ν)...

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Abstract

The invention discloses an optimization method based on an improved sparrow search algorithm. Sparrows in the sparrow search algorithm are divided into discoverers, followers grabbing food of the discoverers and followers needing wide search due to starvation and windlass according to a fitness ascending order; chi-square variation is introduced into a discoverer updating formula to update a discoverer, a follower is updated according to a discoverer updating result, and then sparrows aware of danger are randomly selected and updated; chi-square variation is performed on the individuals with the fitness values smaller than the average value of all the sparrow fitness values; and if the current number of iterations is smaller than the maximum number of iterations, the step S2 is returned to until the maximum number of iterations is reached. By introducing chi-square variation and sacrificing partial solution duration, certain local search ability of Gaussian variation is reserved, global search ability is improved, the problem that SSA is prone to falling into a local optimal solution in the later stage can be improved, and optimization ability and robustness of SSA are improved.

Description

technical field [0001] The invention relates to the technical field of racing cars, in particular to an optimization method based on an improved sparrow search algorithm. Background technique [0002] In 1975, Holland proposed the genetic algorithm based on the evolution law of the survival of the fittest, SMETS P proposed the ant colony algorithm based on the foraging behavior of ant colonies in 1991, and Kennedy proposed the particle swarm algorithm based on the foraging behavior of birds in 1995. Since then, more and more Scholars have proposed multiple swarm intelligence optimization algorithms and improved algorithms based on biological characteristics, such as gray wolf optimization algorithm and locust algorithm. Although the swarm intelligence optimization algorithm has the characteristics of strong robustness and wide application range compared with the traditional optimization algorithm, it is easy to fall into the local optimal solution, which leads to the prematu...

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Application Information

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IPC IPC(8): G06N3/00
CPCG06N3/006
Inventor 李强王家欣
Owner ZHEJIANG UNIVERSITY OF SCIENCE AND TECHNOLOGY
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