To improve the search quality of the standard PSO algorithm for solving high-dimensional function, a dynamic particle swarm optimization algorithm is proposed.
针对基本粒子群优化算法对高维函数优化时搜索精度不高的缺陷，提出了一种动态粒子群优化算法。
On the basis of analyzing the particle swarm optimization and introducing the idea of sub-swarms, a particle swarm optimization algorithm with dynamic sub-swarms (DPSO) is proposed.
在分析基本微粒群优化算法的基础上，引进分群思想，提出了一种动态分群的微粒群优化算法(DPSO)。
In allusion to the problem of dynamic self-calibration, a novel self-calibrating algorithm for visual position based on particle swarm optimization (PSO) is suggested in this paper.
针对动态自标定的问题，提出了一种改进的基于粒子群优化(PSO)的自标定位置视觉定位算法。
Aiming at the problem that normal Particle Swarm Optimization (PSO) algorithm can not approach the best position effectively in dynamic environment, this paper proposes a dynamic PSO algorithm.
针对普通粒子群优化算法难以在动态环境下有效逼近最优位置的问题，提出一种动态粒子群优化算法。
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.
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.
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。
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