提出了一种基于微粒群优化(PSO)算法的连续属性离散化方法，很好的解决了建模过程中连续属性的离散化问题。
An algorithm for discretization based on Particle swarm optimization (PSO) is presented, which can settle the problem of continuous attributes discretization in systema modeling perfectly.
在离散微粒群算法的基础上，提出了一种基于二进制的随机多目标P SO算法，并对感知模型进行覆盖优化。
Then a binary awareness model based on stochastic sensor placement and a stochastic multi-objective PSO arithmetic based on binary system which is applied on awareness model have been present.
通过引入随机交换序、PMX算子使微粒群优化算法能够求解车辆路径问题这类离散组合优化问题。
PSO can solve a discrete combination optimization such as VRP by using random exchange sequence and PMX operator.
通过引入随机交换序、PMX算子使微粒群优化算法能够求解车辆路径问题这类离散组合优化问题。
A PSO can solve a discrete combination optimization such as VRP by using random exchange sequence and PMX operator.
通过引入随机交换序、PMX算子使微粒群优化算法能够求解车辆路径问题这类离散组合优化问题。
A PSO can solve a discrete combination optimization such as VRP by using random exchange sequence and PMX operator.
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