中文说明： 近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的，详细参考1。目的是将原来只能用在单目标上的粒子群算法（PSO）应用于多目标上。
English Description：
In recent years, the heuristic based multi-objective optimization technology has been greatly developed, and the research shows that the technology is more practical and efficient than the classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, spea, SPEA2, PAEs and PESA. Particle swarm optimization (PSO) algorithm is a kind of evolutionary technology based on swarm intelligence, which simulates social behavior. With its unique search mechanism, excellent convergence performance and convenient computer implementation, it has been widely used in the field of engineering optimization. Multi objective PSO (MOPSO) algorithm has been applied to different optimization fields [9 ~ 11], but it has high computational complexity, low universality and poor convergence Good and so on. Multi objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos a. coello, coello et al. In 2004. The purpose is to apply particle swarm optimization (PSO), which can only be used for single target, to multi-target.