Particle swarm optimization algorithm based on clustering degree of swarm

Abstract

The present invention discloses a particle swarm optimization algorithm based on a clustering degree of a swarm. The algorithm comprises the following steps of carrying out initialization; updating the swarm; judging whether a number of iterations is greater than a preset number of iterations and executing a corresponding step; judging whether a number of update iterations is greater than a preset number of times of stagnation and executing a corresponding step; calculating a particle clustering degree of each particle and a particle clustering degree of a swarm optimal position so as to acquire a distance between each particle and the swarm optimal position; according to a fitness of each particle, selecting a plurality of particles of which the number accords with a swarm scale to form a current swarm; and carrying out iterative optimization and updating until the maximum number of iterations is reached. According to the particle swarm optimization algorithm disclosed by the embodiment of the present invention, different evolutionary strategies can be adopted for different particles according to the progress of the optimizing process and the particle clustering degree so as to reduce the possibility of falling into the local minimum, improve the global searching ability of the algorithm and effectively avoid premature convergence.

Description

technical field [0001] The invention relates to the technical field of optimization algorithms, in particular to a particle swarm algorithm based on the aggregation degree of populations. Background technique [0002] The current particle swarm optimization (PSO) algorithm is a typical heuristic bionic algorithm, which originated from the simulation of the foraging behavior of birds, and its essence is to achieve optimization based on the information interaction between individuals and groups. It is a typical group optimization algorithm. [0003] Particle swarm optimization algorithm uses particles to represent a solution to the optimization problem, and its corresponding objective function value is called the fitness of particles, and multiple particles form a population. Each particle has a position and speed. In each iteration, each particle adjusts its own position and flight speed according to the best position found by the individual and the best position found by th...

AI Technical Summary

Problems solved by technology

Although this method can obtain a faster convergence speed, it is easy to cause the population to fall into a local extreme point, which is not conducive to finding the global optimal solution. Especially in the late iteration, a large number of particles gather in a small search space, and the entire population is single is very strong, and has basically lost the ability to search for other areas in the space

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