微粒群优化算法是求解连续函数极值的一个有效方法。
Particle Swarm Optimization (PSO) algorithm is a powerful method to find the extremum of a continuous numerical function.
经典的粒子群优化算法是一个在连续的定义域内搜索数值函数极值的很有效的方法。
The classical Particle swarm optimization (PSO) algorithm is a powerful method to find the minimum of a numerical function, on a continuous definition domain.
针对多极值连续函数优化问题，提出了一种自适应蚁群算法。
An adaptive ant colony algorithm is presented for the optimization of multi-minimum continuous function.
动态规划法是运筹学中的一种常用的优化算法，可以用来求解约束条件下的函数极值问题。
Dynamic programming is an optimal arithmetic which is commonly used in operational research and can be used to solve the extreme value of the function in restricted condition.
针对K均值聚类算法依赖于初始值的选择，且容易收敛于局部极值的缺点，提出一种基于粒群优化的K均值算法。
Local optimality and initialization dependence disadvantages of K-means are analyzed and a PSO-based K-means algorithm is proposed.
针对粒子群优化算法(PSO)应用于多极值点函数易陷入局部极小值，提出旋转曲面变换(RST)方法。
Aimed at particle swarm optimization (PSO) algorithm being easily trapped into local minima value in multimodal function, a rotating surface transformation (RST) method was proposed.
粒子群优化算法应用于多极值点函数优化时，存在陷入局部极小点和搜寻效率低的问题。
Particle Swarm optimization (PSO) algorithm is a population-based global optimization algorithm, but it is easy to be trapped into local minima in optimizing multimodal function.
对于变分同化中经常遇到的多极值问题，一般的优化算法无法解决。
The ordinary optimization algorithm can not solve the multi-extreme value problem in data assimilation, so an improvement to steepest descent algorithm is proposed to solve the problem.
实验实例表明：该算法收敛速度快，有极强的避免过早收敛及避免局部极值的全局优化的能力。
The position and the weighted coefficients can be optimized at the same time. The cases showed that this method had a strong ability to find to global optimization solution.
粒子群优化聚类算法具有参数简单，收敛快等优势，但也有局部极值问题。
PSO clustering algorithm is known to have simple parameters and fast convergence, but there are also local optimal problems.
该算法基本保持了粒子群优化算法简单容易实现的特点，但改善了粒子群优化算法摆脱局部极值点的能力，提高了算法的收敛速度和精度。
The proposed algorithm is almost as simple for implement as particle swarm optimizer, but can improve the abilities of seeking the global excellent result and evolution speed.
该算法基本保持了粒子群优化算法简单容易实现的特点，但改善了粒子群优化算法摆脱局部极值点的能力，提高了算法的收敛速度和精度。
The proposed algorithm is almost as simple for implement as particle swarm optimizer, but can improve the abilities of seeking the global excellent result and evolution speed.
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