Improved particle swarm algorithm based on support vector machine regression

A support vector machine, a technique for improving particle swarms, applied in the field of evolutionary algorithms

Pending Publication Date: 2020-10-30
NANJING INST OF TECH
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Problems solved by technology

[0007] In order to solve the above-mentioned technical problems, the present invention proposes an improved particle swarm algorithm based on support vector machine regression, which is spe

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  • Improved particle swarm algorithm based on support vector machine regression
  • Improved particle swarm algorithm based on support vector machine regression
  • Improved particle swarm algorithm based on support vector machine regression

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[0041] In order to facilitate the understanding and implementation of the present invention by those of ordinary skill in the art, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0042] Such as image 3 As shown, an improved particle swarm algorithm based on support vector machine regression of the present invention includes the following steps:

[0043] Step 1: Initialize the parameters of the improved particle swarm algorithm based on support vector machine regression. The parameters include the number of groups n, the maximum number of iterations k, the inertia weight w, and the learning factor c 1 And c 2 , R 1 And r 2 Is a random number of [-1,1], the number of random positions is t, and the random number of random positi...

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Abstract

The invention discloses an improved particle swarm algorithm based on support vector machine regression. The improved particle swarm algorithm includes: randomly taking a point near the current globally optimal particle position, predicting an optimal random point by using support vector machine regression, then replacing a non-optimal particle, and carrying out next iteration. Along with continuous increase of input regression information, prediction becomes more and more accurate. When a bird flock searches for food, the bird flock always moves towards a known optimal position, the bird flock has a certain probability and does not move towards the optimal position, an intelligent person exists in the bird flock, and the position of a better point near the optimal position can be pre-judged according to the point where the bird flock passes through. A random value range of an original particle swarm algorithm is changed from [0, 1] to [-1, 1], a value is randomly taken near an optimalvalue after an optimal particle position is calculated each time, and a support vector machine is used for regression prediction of the optimal value, so that the globality and locality of a group are enhanced, and the global and local optimization capability can be effectively enhanced. The improved particle swarm algorithm can be specifically applied to complex optimization problems such as function optimization, planning problems, mode recognition and image processing problems.

Description

Technical field [0001] The invention belongs to the technical field of evolutionary algorithms, and relates to a particle swarm optimization algorithm, in particular to an improved particle swarm algorithm based on support vector machine regression. Background technique [0002] Particle swarm optimization (PSO) is a biological heuristic method in the field of computational intelligence, and belongs to a kind of swarm intelligence optimization algorithm. It comes from Kennedy and Eberhart's observation and research on certain social behaviors of birds. Due to its simple operation and fast convergence speed, PSO has been widely used in many fields such as neural network training, semiconductor device synthesis, decision-making and scheduling. But the particle swarm algorithm does not handle discrete optimization problems well, and it is easy to fall into the local optimum. Now the main development direction of particle swarm algorithm: [0003] (1) Adjust the parameters of PSO to...

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Application Information

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IPC IPC(8): G06N3/00
CPCG06N3/006
Inventor 何鸿天李先允倪喜军王书征张效言
Owner NANJING INST OF TECH
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