Ant Colony algorithm is a novel simulated evolvement algorithm solving complicated combinatorial optimization problem and its typical feature is swarm intelligence.
蚁群算法是一种新颖的求解复杂组合优化问题的模拟进化算法，它具有典型的群体智能的特性。
The basic and typical algorithm of swarm intelligence is particle swarm optimization and Ant colony oprimation.
目前，群智能理论研究领域有两种主要的算法：微粒群优化算法和蚁群优化算法。
A new algorithm of swarm intelligence, Particle swarm Optimization (PSO), which is an algorithm of simple implementation and fast convergence with few parameters, is introduced in this paper.
介绍了一种新的集群智能算法-微粒群算法(PSO)，该算法具有实现简单、参数少且收敛快的特点。
Optimization algorithm based on the swarm intelligence is a simulated evolutionary method that simulating the behaviors of social insects searching for food and building of nest.
基于群集智能的优化算法是一种仿生自然界动物昆虫觅食、筑巢行为的模拟进化算法。
The swarm intelligence optimization algorithm include: ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and artificial fish swarm algorithm.
目前提出的群智能优化算法有蚁群优化算法、粒子群优化算法、人工鱼群算法、人工蜂群算法。
As an important branch of computational intelligence and swarm intelligence, ACO have been successfully applied to solve many combinational optimization problems.
作为计算智能和群智能的重要分支之一，蚁群优化算法已经成功地应用于许多组合优化问题的求解。
Particle swarm optimization (PSO) is a heuristic search method based on swarm intelligence, and has been widely used to solve various problems in engineering fields.
粒子群优化(PSO)算法是一种基于群体智能的启发式搜索方法，应用领域很广。
As a swarm intelligence algorithm, AFSA has its weakness, such as high complexity, low optimizing precision and low convergence speed in the later period of the optimization.
但是作为一种新的群智能算法，人工鱼群算法有自身的不足，如算法的复杂度高、算法后期的收敛速度慢和收敛精度低等。
As a swarm intelligence algorithm, AFSA has its weakness, such as high complexity, low optimizing precision and low convergence speed in the later period of the optimization.
但是作为一种新的群智能算法，人工鱼群算法有自身的不足，如算法的复杂度高、算法后期的收敛速度慢和收敛精度低等。
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