中文说明：应用背景粒子群优化算法是一种受启发的进化计算技术鸟群行为。PSO算法是首先由甘乃迪和Eberhart（1995）；连续非线性函数的优化。该算法的基本原理方法依赖于研究社会生物的运动进行了模拟电脑（李维斯，1983；雷诺兹，1987；赫普纳&；Grenander，1990）。粒子群优化算法的研究算法在过去的几年中显着增长，并取得了一些成功关于单和多目标优化的应用程序（甘乃迪&；2001；Eberhart，Coello等人。，2004）。这种受欢迎程度部分原因是由于事实在典型的粒子群优化算法中，只有一小部分的参数必须被调整同时由于对基于该技术的算法实现容易。受粒子群优化算法的成功与持续的问题，研究人员的处理随着离散优化问题的研究方法，以适应原来的建议到离散的情况。在许多研究中，新的方法是用旅行商问题，旅行商问题，一旦它一直是一个重要的测试地面算法思想。关键技术粒子群优化算法旅行商问题
English Description：
Application backgroundParticle swarm optimization, PSO, is an evolutionary computation technique inspired in the behavior of bird flocks. PSO algorithms were first introduced by Kennedy & Eberhart (1995) for optimizing continuous nonlinear functions. The fundamentals of this metaheuristic approach rely on researches where the movements of social creatures were simulated by computers (Reeves, 1983; Reynolds, 1987; Heppner & Grenander, 1990). The research in PSO algorithms has significantly grown in the last few years and a number of successful applications concerning single and multi-objective optimization have been presented (Kennedy& Eberhart, 2001; Coello et al., 2004). This popularity is partially due to the fact that in the canonical PSO algorithm only a small number of parameters have to be tuned and also due to the easiness of implementation of the algorithms based on this technique.