Gradient adaptive particle swarm optimization method based on swarm aggregation effect

Abstract

The invention discloses a gradient adaptive particle swarm optimization method based on swarm aggregation effect. The method comprises the following steps of firstly, setting initialization parameters, initializing the speed and position of a particle swarm, then initializing a population extreme value and an individual extreme value, then clustering the particle swarm by adopting a K-Means clustering algorithm according to the relative positions of particles in a search space to obtain a clustering result, and then calculating a clustering extreme value and a corresponding position accordingto the clustering result; adaptively adjusting the calculation parameter of each particle according to the descent gradient of the fitness value of the target function of the particle, and calculatingthe fitness value of the particle at the current position according to the current position of the particle and the target function; and finally, updating the individual extremum, the clustering extremum and the global extremum according to the fitness value of the particle at the current position, and updating the speed and the position of the particle. The method provided by the invention can effectively solve the problems of premature convergence, local optimum and the like of the existing particle swarm method, and greatly improves the optimization capability of the algorithm.

Description

technical field [0001] The invention relates to the technical field of intelligent algorithms, in particular to a gradient adaptive particle swarm optimization method based on the group reunion effect. Background technique [0002] Particle swarm optimization algorithm is a swarm intelligent random search optimization algorithm. It was originally inspired by the activities of social groups such as birds foraging, and a simplified model of random search process was abstracted from it. The process of changing from disorder to order in the search space, so as to jointly tend to an optimal solution. [0003] The standard particle swarm optimization method has problems such as premature convergence and easy to fall into local optimum, which leads to the fact that the algorithm cannot achieve the ideal effect in the actual application process. For example, in the early stage of PSO (Particle Swarm Optimization), the particles quickly move and gather near the better solution. As t...

AI Technical Summary

Problems solved by technology

[0006] In view of this, the present invention provides a gradient adaptive particle swarm optimization method based on the group reunion effect to solve or at least partially solve the technical problems in the existing methods that lead to poor optimization results in practical applications

Images