粒子群优化算法及其参数设置

毕业论文

题目粒子群算法及其参数设置专业信息与计算科学

班级计算061

学号**********

学生xx

指导教师徐小平

2016年

I

粒子群优化算法及其参数设置

专业：信息与计算科学

学生： xx

指导教师：徐小平

摘要

粒子群优化是一种新兴的基于群体智能的启发式全局搜索算法，粒子群优化算法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。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。论文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，重点利用单因子方差分析方法，分析了粒群优化算法中的惯性权值，加速因子的设置对算法基本性能的影响，给出算法中的经验参数设置。最后对其未来的研究提出了一些建议及研究方向的展望。

关键词：粒子群优化算法；参数；方差分析；最优解

II

Particle swarm optimization algorithm and its

parameter set

Speciality: Information and Computing Science

Student: Ren Kan

Advisor: Xu Xiaoping

Abstract

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 has easy to understand, easy to achieve, the characteristics of strong global search ability, and has never wide field of science and engineering concern, has become the fastest growing one of the 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; Variance analysis; Optimal solution

III

目录

摘要.................................................................................................................................. II Abstract ............................................................................................................................. I II 1.引言. (1)

1.1 研究背景和课题意义 (1)

1.2 参数的影响 (1)

1.3 应用领域 (2)

1.4 电子资源 (2)

1.5 主要工作 (2)

2.基本粒子群算法 (3)

2.1 粒子群算法思想的起源 (3)

2.2 算法原理 (4)

2.3 基本粒子群算法流程 (5)

2.4 特点 (6)

2.5 带惯性权重的粒子群算法 (7)

2.7 粒子群算法的研究现状 (8)

3.粒子群优化算法的改进策略 (9)

3.1 粒子群初始化 (9)

3.2 邻域拓扑 (9)

3.3 混合策略 (12)

4.参数设置 (14)

4.1 对参数的仿真研究 (14)

4.2 测试仿真函数 (15)

4.3 应用单因子方差分析参数对结果影响 (33)

4.4 对参数的理论分析 (34)

5结论与展望 (39)

致谢 (43)

附录 (44)

IV