论文部分内容阅读
在基本粒子群优化算法的理论分析的基础上,提出一种加速收敛的粒子群优化算法,并从理论上证明了该算法的快速收敛性,同时对该算法中的参数进行了优化.为了防止其在快速收敛的同时陷入局部最优,采用依赖部分最差粒子信息的变异操作.最后通过与其他几种经典粒子群优化算法的性能比较,表明了该算法的高效和稳健,且明显优于现有的几种经典的粒子群算法.
Based on the theoretical analysis of the basic particle swarm optimization algorithm, an accelerated convergence particle swarm optimization algorithm is proposed, and the fast convergence of the algorithm is proved theoretically. At the same time, the parameters of the algorithm are optimized. In order to prevent Which converges to a local optimum with fast convergence and adopts a mutation operation that relies on the worst part of the particle information.Finally, the performance comparison with several other classical particle swarm optimization algorithms shows that the algorithm is efficient and robust, Several existing classical particle swarm optimization algorithms.