中文摘要：

针对标准粒子群优化算法在信息共享机制的不足,提出基于邻域空间的混合粒子群优化算法。该算法修改了粒子速度更新方程,提出了一种将模式搜索算法嵌入粒子群优化算法新方法。通过4个典型的测试函数的实验研究,表明了所提出的算法充分发挥了模式搜索算法强大的局部搜索能力和基于邻域空间的粒子群优化算法的全局寻优能力,很好地平衡了算法的全局＂探索＂与局部＂开发＂。新算法具有优化精度高、鲁棒性强的特点,特别适合对高维多峰函数进行优化。

英文摘要：

Considering the information sharing deficiency of standard particle swarm optimization,this paper proposes a hybrid particle swarm optimization based on neighborhood space which modifies the updating equation for particle velocity by embedding pattern search algorithm into the particle swarm.The experimental study of four typical test functions demonstrates the suggested algorithm has accomplished the balance between global＂exploration＂and local＂exploitation＂by taking advantage of the local search power of pattern search and the global optimum capacity of particle swarm algorithm based on neighborhood space.The study also shows that the suggested algorithm is especially applicable to optimizing high-dimensional multimodal functions with the characteristics of high precision and strong robustness.