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Particle swarm optimization method based on mutual information similarity strategy

A technology of particle swarm optimization and optimization method, which is applied to instruments, artificial life, computing, etc., and can solve problems such as slow convergence speed, wandering in the local range of high-dimensional space functions, and inability to find them.

Inactive Publication Date: 2016-12-21
ANYANG NORMAL UNIV
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Problems solved by technology

[0011] The purpose of the present invention is to provide a particle swarm optimization method based on the mutual information similarity strategy, aiming at solving the problem that PSO tends to wander in the local range when solving high-dimensional space functions in practical applications, and the late convergence speed is slow, and it is impossible to find the optimal solution. solve the problem

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  • Particle swarm optimization method based on mutual information similarity strategy
  • Particle swarm optimization method based on mutual information similarity strategy
  • Particle swarm optimization method based on mutual information similarity strategy

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[0029] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0030] The application principle of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0031] Such as figure 1 As shown, the particle swarm optimization method based on the mutual information similarity strategy in the embodiment of the present invention includes the following steps:

[0032] S101: Initialize parameters, including number of particles, number of path points, particle position, speed;

[0033] S102: Initialize the mutual information joint histogram figure 2 D matrix;

[0034]S103: If the end condition is met, go to step S111;

[0035] S104: Calculate...

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Abstract

The invention discloses a particle swarm optimization method based on a mutual information similarity strategy. The particle swarm optimization method based on the mutual information similarity strategy is characterized by using a combined histogram method of mutual information solving to redefine a speed updating formula of h (e, g) and a particle in a combined histogram, wherein the e and the g are a path to be matched and a template path respectively; and the h (e, g) expresses generation times of a position g corresponding to a historical path at a position where an optimal path e is generated. Compared to an existing basic PSO algorithm, by using the method of the invention, iteration times are reduced, a convergence speed is increased and an average result of searching is increased too.

Description

technical field [0001] The invention belongs to the technical field of particle swarm optimization algorithms, in particular to a particle swarm optimization method based on a mutual information similarity strategy. Background technique [0002] Particle Swarm Optimization (PSO) was invented by Eberhart and Dr. Kennedy in 1995. This algorithm is a random search algorithm based on group cooperation developed by simulating the foraging behavior of birds. It uses the method that birds use simple rules to determine their own flight direction and flight speed. The flight speed and displacement are updated by the fitness value determined by the optimized function (namely, the objective function). PSO searches the optimal area in the complex space through the interaction between particles, which is a kind of stochastic global optimization algorithm based on iteration. [1] . The algorithm is simple and easy to operate and is widely used in combination function optimization proble...

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Application Information

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IPC IPC(8): G06N3/00
CPCG06N3/006
Inventor 汪向征葛彦强孙华高峰熊晶
Owner ANYANG NORMAL UNIV
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