Particle swarm improved algorithm based on chaotic backward learning

Abstract

The invention discloses a particle swarm improved algorithm based on chaotic backward learning. The algorithm mainly comprises a backward learning strategy and chaotic particle swarm optimization. The basic principle of the backward learning strategy includes: the backward learning strategy generates a corresponding backward solution for each initial candidate solution, and selects the solutions with short distance (high fitness) from two kinds of solutions (candidate solutions and corresponding backward solutions) as members of an initial population so that the convergence rate in an optimization process can be increased. In order to maintain the diversity of the population and enable individuals of the initial population to be uniformly distributed as much as possible, the initial population is generated by employing the backward learning strategy.

Description

technical field [0001] The present invention relates to the technical field of particle swarm optimization algorithm, in particular to an improved particle swarm algorithm based on chaos reverse learning. Background technique [0002] The performance of the particle swarm optimization algorithm is affected by its own parameter settings, and the speed is slow in the later stage, and it is easy to fall into local minimum and other problems. In recent years, the research and application of PSO algorithm are mainly carried out from the following aspects: the improvement of topology structure; the research of learning strategy, the research of PSO hybrid optimization algorithm, and the application research of PSO algorithm. The purpose of improving the learning strategy is to enhance the information exchange between particles, enhance the diversity of the population, and then improve the ability of the population to jump out of the local optimal solution. A comprehensive learnin...

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Problems solved by technology

However, the above-mentioned improvement strategies have played a positive role to some extent, but there are still deficiencies in convergence accuracy and other aspects.

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