一种基于杂交机制的改进的双子群粒子群优化算法

An Improved Hybrid Mechanism Based on the Twin-Swarm Particle Swarm Optimization Algorithm

为了克服粒子群算法的早熟收敛问题和易陷入局部最优问题,本文提出了一种新的基于双子群的改进粒子群优化算法,通过2组搜索方向相反的主、辅子群之间的相互协同,扩大搜索范围,并借鉴杂交机制,使搜索速度更快,收敛精度更高。再采用自适应惯性权重的粒子群算法,根据种群的进化状态来动态调整惯性权重。

To overcome the problem of premature convergence of particle swarm algorithm easy to fall into local optimization problem , we propose a new optimization based on the twin group improved particle swarm optimization , search in the opposite direction through the two main groups , subgroups between each other auxiliary coordination, broaden your search and learn hybrid mechanism to search faster and more convergence precision. Then using adaptive inertia weight particle swarm optimization, according to the evolutionary state of the population to dynamically adjust the inertia weight.