一种嵌入局部混沌搜索的混合微粒群优化算法

该文研究了基于种群演化的微粒群优化算法，针对此算法在迭代的过程中陷入局部极小点而产生群体演化停滞的现象，提出了一种嵌入局部混沌搜索的混合微粒群优化算法。此混合方法利用混沌迭代的遍历性来增强算法的局部精确搜索能力从而达到全局搜索性能和局部搜索性能的平衡，使群体快速脱离停滞状态。实验结果表明，相比于其他演化搜索算法如标准微粒群算法，标准遗传算法和改进微粒群算法，嵌入局部混沌搜索的混合微粒群算法在收敛性和鲁棒性方面得到了较大的改善，很大程度上避免了演化停滞现象的发生，是一种高效的搜索方法。

A Hybrid Particle Swarm Algorithm with Embedded Chaotic Search

Evolutionary - based particle swarm algorithm has been investigated in this paper. A new hybrid particle swarm algorithm with embedded chaotic search is proposed for optimization in order to avoid trapping in local optimum and stagnancy of population. This hybrid method makes use of the ergodicity of chaotic search to improve the capability of precise search and keep the balance between the global search and the local search, which makes population escape the stagnancy rapidly. It has been compared with other methods such as standard particle swarm algorithm, standard genetic algorithm and improved particle swarm algorithm. In comparison, the proposed method shows its superiority in convergence property and robustness. It is validated by the simulation results.