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Particle swarm optimization algorithm, multi-computer parallel processing method and system

A particle swarm optimization and particle swarm technology, which is applied in various digital computer combinations, calculations, calculation models, etc., can solve the problem of optimization algorithms falling into local optimum, achieve the effect of improving accuracy and efficiency, and expanding coverage

Inactive Publication Date: 2017-07-14
CHANGZHOU COLLEGE OF INFORMATION TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to provide a particle swarm optimization algorithm, multi-computer parallel processing method and system to solve the problem that the initial value of the accurate particle swarm optimization algorithm is easy to fall into local optimum

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  • Particle swarm optimization algorithm, multi-computer parallel processing method and system
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  • Particle swarm optimization algorithm, multi-computer parallel processing method and system

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Embodiment 1

[0055] Such as figure 1 and figure 2 As shown, the present embodiment 1 provides a particle swarm optimization algorithm, including:

[0056] Step S1, initialize the generalized interval particle swarm;

[0057] Step S2, calculating the initial fitness value of each particle; and

[0058] Step S3, iteratively updating the velocity and position of the particle swarm until the global optimal solution is output.

[0059] Specifically, the method for initializing the generalized interval particle swarm in step S1 includes:

[0060] Suppose there are m particles in this particle swarm, and each particle is a generalized interval. The initial position and initial velocity expressions of each particle are as follows:

[0061] Initial position expression:

[0062] in, is the lower bound of the initial position of the i-th particle, is an upper bound on the initial position of the i-th particle; and

[0063] Initial velocity expression:

[0064] in, is the lower bound...

Embodiment 2

[0089] Such as image 3 As shown, on the basis of Embodiment 1, Embodiment 2 also provides a multi-computer parallel processing method.

[0090] Adopt the particle swarm optimization algorithm as described in embodiment 1 to obtain global optimal solution by main frame and some extensions, namely

[0091] Each extension is suitable for calculating the position and velocity of the assigned particle at each moment, and record the best position of the particle, and transmit the calculation result to the host;

[0092] The host is suitable for collecting the optimal position, current position and speed of each particle transmitted by each extension, calculating the position of the global optimal particle, and transmitting this information to each extension for each extension to determine the speed of the particle it belongs to.

Embodiment 3

[0094] Such as image 3 As shown, on the basis of embodiment 1, this embodiment 3 also provides a multi-computer parallel computing system applying the particle swarm optimization algorithm as described in embodiment 1.

[0095] The multi-computer parallel computing system includes: a mainframe and several extensions, wherein

[0096] Each extension is suitable for calculating the position and velocity of the assigned particle at each moment, and record the best position of the particle, and transmit the calculation result to the host;

[0097] The host is suitable for collecting the optimal position, current position and speed of each particle transmitted by each extension, calculating the position of the global optimal particle, and transmitting this information to each extension for each extension to determine the speed of the particle it belongs to.

[0098] On the basis of embodiment 2 and embodiment 3, by Figure 4 It can be seen that compared with the traditional part...

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Abstract

The invention relates to a particle swarm optimization algorithm, a multi-computer parallel processing method and a system. The algorithm comprises steps of step S1, initializing a particle swarm in a generalized section; S2, calculating an initial fitness value of each particle; and step S3, updating speed and positions of the particle swarm in an iteration manner until the overall optimal solution is output. According to the invention, in a way of replacing precise particles through the generalized section, the coverage range of the particle swarm is greatly expanded; a problem that the initial value of the precise particles easily allow the optimization algorithm to suffer from local optimum is effectively solved; and the parallel processing ability of the current multi-core computer can be combined, so precision and efficiency of calculation results are remarkably improved.

Description

technical field [0001] The invention relates to a particle swarm optimization algorithm, a multi-computer parallel processing method and a system. Background technique [0002] Particle swarm optimization (PSO) is similar to genetic algorithm and is an optimization algorithm based on iteration. The system is initialized as a set of random solutions, and the optimal value is searched through iteration. But it does not have the crossover and mutation used by the genetic algorithm, but the particles follow the optimal particle to search in the solution space. Compared with genetic algorithm, the advantage of PSO is that it is simple and easy to implement and there are not many parameters to adjust. It has been widely used in function optimization, neural network training, fuzzy system control and other application fields of genetic algorithm. [0003] In actual use, due to the limited coverage of the precise particle swarm, it is easy to cause the initial value of the precise...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/00G06F15/16
Inventor 王二化赵黎娜
Owner CHANGZHOU COLLEGE OF INFORMATION TECH
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