Considering that the particle swarm optimization (PSO) algorithm is quite simple and easy to implement, it was used to estimate the nonlinear model parameters in this paper.
粒子群算法操作简便、容易实现且全局搜索功能较强，适用于非线性参数估计。
It USES the dynamic scale-free like network as the particle's optimization neighborhood. It proposes an improved PSO algorithm based on variety inertia weight and dynamic neighborhood.
将有向动态类无标度网作为粒子寻优邻域，提出一种基于变惯性权重及动态邻域的改进P SO算法。
The test results on benchmark functions show that ADPSO achieves better solutions than other improved PSO, and it is an effective algorithm to solve multi-objective problems.
在基准函数的测试中， 结果显示ADPSO算法比其他PSO算法有更好的运行效果，是求解多峰问题的一种有效算法。
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