Improved particle swarm algorithm and application thereof

Abstract

The invention relates to an improved particle swarm algorithm and the application of the improved particle swarm algorithm. The improved particle swarm algorithm includes the following steps that firstly, the algorithm is initialized; secondly, the positions x and speeds v of particles are randomly initialized; thirdly, the number of iterations is initialized, wherein the number t of iterations is equal to 1; fourthly, the adaptive value of each particle in a current population is calculated, if is smaller than or equal to , then is equal to and is equal to , and if is smaller than or equal to , then is equal to and is equal to ; fifthly, if the adaptive value is smaller than the set minimum error epsilon or reaches the maximum number Maxiter of iterations, the algorithm is ended, and otherwise, the sixth step is executed; sixthly, the speeds and positions of the particles are calculated and updated; seventhly, the number t of iterations is made to be t+1, and the fourth step is executed. By means of the improved particle swarm algorithm, at the initial iteration stage, the population has strong self-learning ability and weak social learning ability, and therefore population diversity is kept; at the later iteration stage, the population has weak self-learning ability and strong social learning ability, and therefore the convergence speed of the population is improved.

Description

technical field [0001] The invention relates to an improved particle swarm algorithm and its application, and belongs to the technical field of rolling bearing fault diagnosis and prevention. Background technique [0002] Particle swarm optimization algorithm has attracted the attention of many researchers since it needs to set few parameters, has a simple structure and is easy to implement. Therefore, this algorithm is often used to solve combinatorial optimization problems and train neural networks. While artificial neural networks are useful black-box testing methods capable of approximating arbitrary continuous functions without making any assumptions about the underlying model, they suffer from the problem of local optimal solutions, and in traditional analysis finding optimal It is very difficult to solve, and it also requires a large amount of training data and a long training time. Therefore, we need to find a method that can achieve the global optimal solution. ...

AI Technical Summary

Problems solved by technology

While artificial neural networks are useful black-box testing methods capable of approximating arbitrary continuous functions without making any assumptions about the underlying model, they suffer from the problem of local optimal solutions, and in traditional analysis finding optimal It is very difficult to solve, and it also requires a large amount of training data and a long training time

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