为了克服粒子群算法在求解多峰函数时极易陷入局部最优解的缺陷，提出一种基于自适应动态邻居广义学习的改进粒子群算法(ADPSO)。
As Particle Swarm Optimization (PSO) may easily get trapped in a local optimum, an improved PSO based on adaptive dynamic neighborhood and comprehensive learning named ADPSO was proposed.
本文提出了用于求解单级多资源约束的生产批量计划问题的改进二进制粒子群算法，阐明了算法的具体实现过程。
In this paper, the improved particle swarm optimization algorithm for the single level capacitated dynamic lot-sizing problem is presented. The detailed realization of the algorithm is illustrated.
实验证明采用这种改进的粒子群算法解决多约束背包问题切实可行， 搜索效率较高。
The result of this experiment shows that this particle swarm algorithm is available and efficient in solving multi-c…
针对粒子群算法用于高维数、多局部极值点的复杂函数寻优时易陷入局部最优解现象，提出一种改进的带扰动项粒子群算法并进行收敛性分析。
Traditional particle swarm optimization(PSO) algorithms often trap into local minima easily when used for the optimization of high-dimensional complex functions with a lot of local minima.
针对粒子群算法用于高维数、多局部极值点的复杂函数寻优时易陷入局部最优解现象，提出一种改进的带扰动项粒子群算法并进行收敛性分析。
Traditional particle swarm optimization(PSO) algorithms often trap into local minima easily when used for the optimization of high-dimensional complex functions with a lot of local minima.
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