Particle swarm optimization method based on survival of the fittest and step-by-step selection

Abstract

The invention discloses a particle swarm optimization method based on survival of the fittest and step-by-step selection. The method mainly comprises the steps that particles are divided to two sets in the operating process, the first set of particles are superior particles, and the second set of particles are inferior particles. A solution space is searched for in the global scope through a species, and the global searching ability is enhanced; after each time of evolution, m the best particles in the species are maintained, the position spaces of better particles of the m particles are selected as new solution spaces, new particles are selected to replace the position of poor particles in the species in the new solution spaces, in this way, the optimal particles can be approached step by step, and the optimal solution is found out. The searching ability of the particle swarm optimization method is enhanced, the defects that a fundamental particle swarm optimization algorithm easily falls into local extremum or premature convergence or stagnation are overcome, and the optical values of parameters to be optimized can be more accurately and quickly found out.

Description

technical field [0001] The invention relates to an improved particle swarm optimization method, in particular to a particle swarm optimization method based on survival of the fittest and step-by-step selection. Background technique [0002] The particle swarm optimization method (abbreviated as PSO) uses the principle of swarm intelligence to establish a simplified model to simulate the foraging behavior of birds. The basic principles are as follows: firstly, each individual is regarded as a particle without volume, and all the particles form a particle swarm to search in the space; secondly, the group flies at a certain speed in the search space, and the particle’s The flight speed is constantly adjusted by the particle itself and the flight experience of its companions; finally, the fitness value of the particle is calculated, and the quality of the particle is measured according to the size of the fitness, and the optimal value of the individual particle and the optimal v...

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Problems solved by technology

[0003] In the process of evolution, the particle swarm optimization method tends to fall into a local extremum, that is, it may be difficult to find a better solution after reaching a certain optimization accuracy; the premature convergence of the particle swarm optimization method makes the evolution of the entire population stagnate

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