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粒子群算法、蚁群算法以及人工蜂群算法等都是智能型算法,这些算法通过对生物种群寻找食物过程的模拟,从而提出了一种人工寻找最优解的模拟算法。粒子群算法和蚁群算法在科研领域和工程领域都有广泛的应用,都用于解决最优解的问题。尽管粒子群算法已不是什么新算法,但仍然有应用价值和研究价值,因此经过对粒子群算法研究之后,从以下几个方面对粒子群算法进行了综述。在介绍了粒子群算法的基本概念之后,介绍了粒子群的应用和不足之处,给出了粒子群算法两个重要参数。
Particle swarm optimization (PSO), ant colony optimization (ACO) and artificial bee colony algorithm are all intelligent algorithms. These algorithms propose a simulation algorithm to find the optimal solution by looking for the food process simulation of the biological population. Particle swarm optimization algorithm and ant colony algorithm have a wide range of applications in the field of scientific research and engineering, are used to solve the optimal solution. Although Particle Swarm Optimization is not a new algorithm, it still has application value and research value. After the study of Particle Swarm Optimization algorithm, Particle Swarm Optimization (PSO) is reviewed from the following aspects. After introducing the basic concepts of Particle Swarm Optimization (PSO), the application and the disadvantages of PSO are introduced. Two important parameters of PSO are given.