粒子群算法改进及其应用-硕士论文

粒子群算法已经被国内外学者认为是一种有效的优化方法，但是自身也 存在着一些缺点，比如在搜索后期易陷入局部最优和出现早熟现象。如何加 快粒子群算法的收敛速度和避免出现早熟收敛，一直是研究者关注的重点。 本文在基本粒子群算法的基础上，进行了一些改进。引入云理论把粒子群分 为三个种群，用云方法修改粒子群算法中惯性权重，同时修改速度更新公式 中的“认知部分”和“社会部分”，加入“均值”的概念，提出一种基于均值 的云自适应粒子群算法；考虑惯性权重对算法的影响，较大的权值有利于提 高算法的全局搜索能力，而较小的权值会增强算法的局部搜索能力。提出了 一种基于位置多样性和种群多样性来修改惯性权值的粒子群优化算法。让惯 性权值随着位置移动的长短和适应度的大小来改变。最后把改进的方法应用 在求解工程约束优化问题中。数值实验结果表明，改进的算法对于高维非线 性的无约束优化问题表现出了良好的性能，对工程实例的约束优化问题也显 示了其优越性。
学 校 代 码 10608
学
号 200808120306
分 类 号 TP18
密
级 公开
硕士学位论文
粒子群算法改进及应用
研 究 生 姓 名 ：刘洪霞
导师姓名职称 ：周永权 教授
学 科 专 业 ：计算机应用技术
所 属 学 院 ：数学与计算机科学学院
年
级 ： 2008 级
论 文 完 成 时 间 ： 2011 年 4 月
ABSTRACT
The particle swarm optimization was a kind of modern optimization method that was proposed by Eberhart and Kennedy through mimic natural biological community grazing. Later, Shi, who was the introduction of inertia weight to better control the convergence and, thus, the current standard PSO algorithm. Because the algorithm is simple, needs to adjust the few parameters, has been widely applied to function optimization, communication system design, electronic system design and economic management, etc.
1.1 群智能算法研究背景 ........................................................................................ 1 1.2 国内外研究现状 ................................................................................................ 1 1.3 本文的主要内容和创新点 ................................................................................ 3
glowworm algorithm is solving the engineering constraint optimization problems. Experimental results show that the improved algorithm is effective. KEYWORDS: particle swarm optimization；self-adoptive；cloud model；mean； constrained optimization; glowworm swarm optimization
2.4.1 参数的改进...........................................................................................................................7 2.4.2 协同PSO算法 .......................................................................................................................8 2.4.3 离散PSO算法 .......................................................................................................................8
Particle swarm optimization is thinking an efficient optimization method by the domestic and overseas scholars, but oneself also exist some shortcomings, such as easily trapped into local optimal in the later and premature phenomenon. How to speed up the particle swarm algorithm convergence speed and avoid premature convergence is always the most researchers’ focus of concern. In this paper, based on the standard particle swarm algorithm, some improvements were made. Introducing cloud theory, the particle swarm is divided into three populations. It is modified inertia weight using cloud method, at the same time modified the “social” and “cognitive” section, and the notion of mean was introduced, an improved cloud adaptive theory particle swarm optimization algorithm named CAMPSO is proposed; Considering the influence of inertia to the algorithm, a larger weights is helpful to improve the search ability of the global, while smaller weights will can enhance the local search capability. In view of this, based on position diversity and population diversity to revise the inertial weights of particle swarm optimization algorithm was proposed. Make the inertial weights with the position of the length and fitness value to change. Finally the improved method is used in solving engineering constraints in optimization. Numerical experiments show that the improved algorithm not only shows good performance in the higher dimensional nonlinear unconstrained optimization problem, but also shows its superiority in the
III
目录
目录
摘 要................................................................................ຫໍສະໝຸດ Baidu............................ I
ABSTRACT ..................................................................................................II 目 录........................................................................................................... IIV 第一章 绪论...................................................................................................1
第二章 粒子群算法.......................................................................................4
2.1 粒子群算法简介 ................................................................................................ 4 2.2 基本粒子群算法 ................................................................................................ 4 2.3 粒子群算法流程 ................................................................................................ 5 2.4 粒子群算法的改进 ............................................................................................ 6
摘要
粒子群算法改进及应用
摘要
粒子群优化算法最早是由 Eberhart 和 Kennedy 模拟自然界的生物群体觅 食提出的一种群智能化方法。后来 Shi 等人引入惯性权重来更好的控制收敛和 探索，形成了当前的标准 PSO 算法。由于该算法实现简单，需要调整的参数 少，已被广泛地应用于函数优化、通信系统设计、电子系统设计以及经济管 理等领域。
II
ABSTRACT
engineering example of the constrained optimization problem also. Finally, this paper use particle swarm algorithm into sense range thought of
将人工萤火虫算法与粒子群算法结合提出一种基于萤火虫算法感知范围 的粒子群算法。并应用到求解工程实例约束优化问题中，实验结果也表明了 改进算法的有效性和正确性。
关键词：粒子群 自适应 云理论 均值 约束优化 萤火虫算法
I
ABSTRACT
IMPROVED AND APPLICATION BASED
ON PARTICLE SWARM OPTIMIZATION