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A bacterial foraging optimization method based on gravity induction

A bacterial foraging algorithm and bacterial foraging technology, applied in the directions of instruments, computational models, biological models, etc., can solve the problems of not being able to find the optimal solution, difficult to determine the step size, and reducing the optimization accuracy, so as to increase the search efficiency. The effect of excellent accuracy, reduced step size, and fast iterative convergence speed

Pending Publication Date: 2019-05-21
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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Problems solved by technology

[0003] The needs of actual life and production have promoted the development of optimization methods. In 2002, Passino first proposed a bacterial foraging optimization algorithm that simulates E. coli, and it was introduced into China in 2007. However, the traditional bacterial foraging algorithm still has the problem of immature theory , For example: the step size is difficult to determine, if it is too large, it will cause no optimal solution or fall into local optimum, which will reduce the optimization accuracy; if the step size is too small, it will reduce the calculation efficiency, easily fall into local optimum or lead to premature and immature

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  • A bacterial foraging optimization method based on gravity induction
  • A bacterial foraging optimization method based on gravity induction
  • A bacterial foraging optimization method based on gravity induction

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Experimental program
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Effect test

Embodiment 1

[0066] Using the Schaffer function

[0067] where -10≤x 1 ,x 2 ≤10, both numbers are between -10 and 10, verifying the optimization ability of the gravity-aware bacterial foraging algorithm. Such as image 3 is the three-dimensional image of the function, Figure 4 is a two-dimensional graph. This function is a relatively complex two-dimensional function, and the minimum point is f(0,0)=0; because this function has a strong oscillation form, it is difficult to find the global optimal value.

[0068] S1: There are two variables that directly affect the value of the function, so X={x 1 ,x 2};

[0069] S2: The optimization value (fitness) of the problem to be studied is the function

[0070] S3: Initialize gravity sensing parameters and bacterial foraging parameters:

[0071] S31: Initialize gravity perception parameters: slope angle α=45°, drag coefficient c=0.5, air density ρ=1.293g / L, wind impact area S=1m 2 ;

[0072] S32: Initialize the relevant parameters of ...

Embodiment 2

[0085] Using the Rastrigrin function where -5≤x 1 ,x 2 ≤5, verify the optimization ability of the gravity-aware bacterial foraging algorithm. This function is a relatively complex two-dimensional function, the minimum point is f(0,0)=0, this function has countless minimum value points, it is easy to fall into a local optimum, and it is difficult to find the minimum value of the whole play, such as Figure 5 , Figure 6 .

[0086] S1: There are two variables that directly affect the value of the function, so X={x 1 ,x 2};

[0087] S2: The optimization value (fitness) of the problem to be studied is the function

[0088] S3: Initialize gravity sensing parameters and bacterial foraging parameters:

[0089] S31: Initialize gravity perception parameters: slope angle α=45°, drag coefficient c=0.5, air density ρ=1.293g / L, wind impact area S=1m 2 ;

[0090] S32: Initialize the relevant parameters of the bacteria foraging algorithm: initial step size Cstart=0.1, the numb...

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Abstract

The invention relates to a bacterial foraging optimization method based on gravity induction. The method comprises the following steps: selecting optimization parameters X = {x1, x2, ..., xp} of a to-be-studied object; Converting the optimization value, namely fitness, of the to-be-studied object into a fitness function with the optimal value of 0: J = fitness (X), and optimally, Jmin = 0; Initializing gravity sensing parameters and bacterial foraging parameters; and optimizing by using a gravity sensing bacterial foraging algorithm. The step length can be automatically adjusted according to small ball energy attenuation, and the defect of traditional bacterial foraging is overcome; The step length is reduced from large to small, the iteration convergence speed is high when the initial step length is increased, and when the iteration convergence speed is close to global optimum, the step length is reduced, and the optimization precision is improved.

Description

technical field [0001] The invention relates to the technical field of heuristic search algorithms, in particular to a bacterial foraging optimization method based on gravity sensing. Background technique [0002] Heuristic algorithms are all derived from biological intelligence algorithms, such as particle swarm algorithm, genetic algorithm, ant colony algorithm, etc. Heuristic search is to evaluate each existing state position (mostly from random) in the state space, and imitate some characteristics of creatures, and complete the search in the state space to obtain the best position, which can reduce a lot of searches The path reduces the amount of calculation and increases the efficiency of calculation. [0003] The needs of actual life and production have promoted the development of optimization methods. In 2002, Passino first proposed a bacterial foraging optimization algorithm that simulates E. coli, and it was introduced into China in 2007. However, the traditional b...

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Application Information

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IPC IPC(8): G06N3/00
Inventor 许锋孔德义唐敏卫玉梁卞世元王焕钦
Owner HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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