- 期刊
Chaotic Butterfly Optimization Algorithm based on Particle Swarm Optimization
摘要
In this article, I proposes an improved particle swarm hybrid butterfly optimization algorithm (IPBOA).In order to solve the problem of low accuracy and slow convergence in the butterfly optimization algorithm (BOA), I designed two strategies to improve the basic butterfly optimization algorithm (BOA) algorithm. The improved particle swarm hybrid butterfly optimization algorithm (IPBOA) includes three main steps. First, I added the cubic chaotic map to initialize the butterfly population. This has increased the diversity of butterfly populations to a certain extent; Secondly, I adopt a non-linear parameter control strategy. This strategy can effectively balance the global search and local search capabilities of the algorithm to a certain extent; finally, in order to improve the basic butterfly optimization algorithm (BOA) for global optimization, I mixed the particle swarm optimization (PSO) algorithm with BOA. In order to verify the effectiveness of the proposed algorithm, I selected 10 test functions. I use these test functions for comparative experiments. Experimental comparison results show that, compared with famous group optimization algorithms such as PSO and BOA,the performance of the IPBOA algorithm I proposed has been further improved. It has fast convergence speed, higher optimization precision and stronger robustness in the optimization problem of benchmark test functions.