Matlab粒子群优化算法的性能仿真研究+源代码
毕业论文关键词： 群体智能，粒子群优化算法，压缩因子，权重，学习因子，MATLAB，测试函数
Performance Simulation of Particle Swarm Optimization
Abstract: In this paper, the theory of swarm intelligence algorithms typical of PSO and improved analytical performance of the method of simulation studies focusing. Swarm intelligence is the natural biological communities through cooperation exhibit intelligent behavior of a system. Swarm intelligence algorithm is a method of crowd behavior simulation by social groups of organisms to achieve artificial intelligence. PSO (Particle Swarm Optimization, PSO algorithm), was proposed in 1995 by Dr. Eberhart and Dr. Kennedy, from the study of birds, predatory behavior. By particle swarm optimization algorithm and its basic ideas and theoretical knowledge its optimization, with compression factor PSO weights improved particle swarm optimization, variable study PSO factor particle swarm optimization and second-order second-order oscillation particle swarm optimization, and use MATLAB software to complete the particle swarm algorithm and its improved algorithm design and performance simulation of compiling and using multiple test functions to accomplish each of these algorithms as well as between the performance of multiple algorithms comparison.
Keywords: swarm intelligence , particle swarm optimization , compression factor , weights , learning factor , matlab , test function
目录
摘要 iv
Abstract v
目录 vi
1 绪论 1
1.1 群体智能 1
1.1.1 群体智能的研究背景 1
1.1.2 群体智能算法的研究现状 2
1.2 粒子群算法 4
1.2.1 粒子群算法的研究现状 4
1.2.2 粒子群算法的应用 6
2 粒子群优化算法 8
2.1 粒子群算法概述 8
2.2 基本粒子群算法 9
2.2.1 算法原理 9
2.2.2 算法步骤 9
2.2.3 算法的MATLAB实现 10
2.3 带压缩因子的粒子群算法 10
2.3.1 算法原理 10
2.3.2 算法步骤 11
2.3.3 算法的MATLAB实现 11
2.4 权重改进的粒子群算法 12
2.4.1 线性递减权重法 12
2.4.2 自适应权重法 13
2.4.3 随机权重法 14
2.5 变学习因子的粒子群算法 16