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Method for improving population diversity in gravitational search algorithm

A gravitational search algorithm and a variety of technologies, applied in computing, biomolecular computers, special data processing applications, etc., can solve problems such as failure to solve premature convergence, and achieve the effect of avoiding premature convergence and improving search ability.

Inactive Publication Date: 2014-08-27
JIANGNAN UNIV
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Problems solved by technology

Although these improved algorithms have reduced the possibility of the gravitational search algorithm falling into a local optimum to a certain extent, they still fail to solve the problem of premature convergence.

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  • Method for improving population diversity in gravitational search algorithm
  • Method for improving population diversity in gravitational search algorithm
  • Method for improving population diversity in gravitational search algorithm

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Embodiment Construction

[0016] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0017] A method for improving population diversity in the gravitational search algorithm, the algorithm steps are as follows:

[0018] Step 1: Determine the search space of the entire gravitational field according to the objective function problem.

[0019] Step 2: Randomly initialize the position of each particle in the population within the scope of the search space, set the number of population particles as N, the dimension of the search space as D and the maximum number of iterations as T times.

[0020] Step 3: According to the objective function problem, set the position X of each particle i Substitute into the objective function to calculate its fitness value with respect to the objective function.

[0021] Step 4: Update the best fitness value and worst fitness value of the particles in the entire population, that is, the optimal s...

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Abstract

The invention relates to the field of intelligent optimization algorithms and discloses a method for improving population diversity in a gravitational search algorithm. The particle population diversity is calculated in each iteration process for performing optimization search through the gravitational search algorithm. When the population diversity is larger than the maximum threshold, each particle gets close to the current best position and the previous best position thereof, and the particles perform the suction operation of a bacterial chemotaxis process to improve the local optimization ability; when the population diversity is smaller than the minimum threshold, each particle gets away from the current worst position and the previous worst position, the particles perform the exclusive operation of the bacterial chemotaxis process to increase the population diversity; when the population diversity is located between the maximum diversity threshold and the minimum diversity threshold, the original velocity updating formula in the gravitational search algorithm is used. According to the method for improving the population diversity in the gravitational search algorithm, the exclusive operation of the bacterial chemotaxis process is led to the gravitational search algorithm to improve the particle population diversity and avoid premature convergence, and accordingly the optimization ability of the algorithm is improved.

Description

technical field [0001] The invention relates to a method for improving population diversity in a gravitational search algorithm, belonging to the field of intelligent optimization algorithms. Background technique [0002] The gravitational search algorithm is a heuristic algorithm proposed in recent years for solving optimization problems. Compared with other existing famous heuristic optimization algorithms, the gravitational search algorithm has better global search ability and faster convergence ability. Its basic idea is based on Newton's law of universal gravitation: "In the universe, every particle attracts each other due to the effect of universal gravitation. The magnitude of gravitation is proportional to the mass of the particles and inversely proportional to the distance between them." The gravity search algorithm has the advantages of high optimization performance, simple structure, and few setting parameters. It has been applied in the fields of function optimiz...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/9032G06N3/002
Inventor 潘丰王蕾
Owner JIANGNAN UNIV
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