Particle swarm optimization method based on multi-strategy synergistic function

Abstract

An object of the invention is to provides a particle swarm optimization method based on a multi-strategy synergistic function. The technical scheme of the method comprises a first step of initializing a particle swarm, wherein NP particles are initialized, a second step of calculating fitness values of the NP particles, a third step of determining particle speeds and position change modes, a fourth step of executing Cauchy variation on positions of the particles, and a fifth step of determining a particle execution stop condition. The method is suitable for optimization solving of a function, takes the full advantage of elite reverse learning, and improves a function optimization speed and function optimization precision; and the particles are prevented from falling into a local optimal value by utilizing gauss variation, and variation is performed on the positions of the particles by utilizing the provided Cauchy variation a Cauchy distribution proportion parameter of which decreases linearly and progressively, so more excellent particles are generated to guide other particles to move toward a better solution direction, the function optimization precision is improved, and function optimization stability are also improved.

Description

technical field [0001] The invention belongs to intelligent computing in the field of artificial intelligence, in particular to a realization method with efficient function optimization. Background technique [0002] The swarm intelligence algorithm is a stochastic optimization algorithm that simulates the biological population in nature, and the particle swarm optimization algorithm (particleswarm optimization, PSO) is a swarm intelligence algorithm proposed by scholars Kennedy and Eberhart. [0003] The PSO algorithm is a stochastic intelligent optimization algorithm, which originated from the research on the foraging behavior of birds. The position of each particle in the algorithm is a potential solution in the search space. In each iterative search process, the particle updates its position by chasing the individual extremum pbest and the global extremum gbest. The particle has a fitness The fitness value determined by the function evaluates the quality of the particle...

AI Technical Summary

Problems solved by technology

However, the PSO algorithm also has the disadvantages of premature convergence and slow convergence in the later stage of evolution.

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