This paper proposes a particle swarm optimization based on local and global combined search.
提出一种基于局部与全局搜索相结合的粒子群算法。
To generate the velocity schema curve of optimization operation for train, a computation model combined local optimization with global optimization is proposed.
以仿真计算获得局部优化规律，用神经网络实现局部优化规律的数据组织，应用遗传算法进行全局优化计算，获得了令人满意的结果。
Optimal flow pattern and branch changing can't assure the global optimization result , but it can get the result in short time if it combined with the elicitation method .
最优流模式和基于支路的交换方法不能保证得到全局最优解，但与启发式规则结合后，可以在较短的时间得到结果。
Optimal flow pattern and branch changing can't assure the global optimization result , but it can get the result in short time if it combined with the elicitation method .
最优流模式和基于支路的交换方法不能保证得到全局最优解，但与启发式规则结合后，可以在较短的时间得到结果。
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