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摘要优化技术是一种基于数学用于求解各种组合优化问题的应用技术。最优化问题是人们在工程、科学研究、经济管理等许多领域中经常碰到的问题，它是指在满足一定的约束条件下，寻找一组参数值，使目标函数达到最大或最小。最优化问题根据其目标函数、约束条件的性质以及优化变量的取值范围可以分为许多类型，例如：根据目标函数和约束条件是否均为线性表达式，把最优化问题划分为线性规划问题和非线性规划问题。针对不同的最优化问题，提出了许多不同的优化方法，如牛顿法、拉格朗日乘子法等。这些优化算法能很好地找到问题的局部最优点，是成熟的局部优化算法。
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微粒群优化算法是基于群体智能理论的一种新兴演化计算技术。是一种启发式全局搜索算法，PSO算法通过群体中微粒间的合作与竞争而产生的群体智能指导优化搜索，通过粒子间的竞争和协作以实现在复杂搜索空间中寻找全局最优点，算法具有较强的通用性，具有全局寻优的特点，它具有易理解、易实现、全局搜索能力强等特点，倍受科学与工程领域的广泛关注，已经成为发展最快的智能优化算法之一。论文介绍了粒子群优化算法的基本原理，分析了其特点。文章中围绕粒子群优化算法的原理、特点、参数设置与应用等方面进行全面综述，分析了不同算法对于同一测试函数的仿真，迭代次数对算法结果影响，得出了仿真结果，以及调试代码。最后对其未来的研究提出了一些建议及研究方向的展望
毕业论文关键词：粒子群优化算法；参数；最优解
Abstract
Optimization technology is based on mathematics，it also can solve various combination optimization problems. Many problems possess parameters to be optimized, especially in engineering technology, scientific research and economic management.Optimization is to look for a set of parameters in definite restriction with the aim of minimize or maximize. According to quality of objective function and restrict condition and scope of variable, optimization problem can be pided into lots of types. For example, if objective function and restrict condition are both lineal expression, this problem belongs to linear programming problem, if not, it belongs to nonlinear programming problem. Different methods have been presented to solved different kinds of problems, such as Newton's method, Lagrange Multiplier Method and so on . These methods can easily find local extreme in different problems.
Particle swarm optimization is an emerging global based on swarm intelligence heuristic search algorithm, particle swarm optimization algorithm competition and collaboration between particles to achieve in complex search space to find the global optimum. It is easy to understand, to achieve, the characteristics of strong global search ability, and has never wide field of science and engineering concern, has become one of the fastest growing intelligent optimization algorithms. This paper introduces the particle swarm optimization basic principles, and analyzes its features. Paper around the particle swarm optimization principles, characteristics, parameters settings and applications to conduct a thorough review, focusing on a single factor analysis of variance, analysis of the particle swarm optimization algorithm in the inertia weight, acceleration factor setting the basic properties of the algorithm the impact of the experience of the algorithm given parameter setting. Finally, its future researched and prospects are proposed.
Key word：Particle swarm optimization; Parameter; Optimal solution
目录
摘要 2
Abstract 3
第一章 绪论 5
1.1课题的目的与意义 5
1.2国内外研究现状与水平 6
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