人工智能导论——智能计算（进化算法+群智能优化）

前言

CSDN好迷啊，前一段时间的粘贴图片自动居中已经取消了，现在还得手动居中QAQ。不过冲就完事了。
PS：此章节的考察内容比较迷惑，记得在网上找些习题帮助复习！

“你可以吗！”
“可以”
冲鸭！

0. 智能计算概述

进化算法与群智能算法是智能优化方法的两大类，下面这张图整挺好。

其中，我们所熟知的遗传、模拟退火、人工神经忘了都属于进化算法；蚁群、粒子群算法则属于群智能算法。

进化算法：主要通过选择、重组、变异（模拟生物进化过程）实现优化问题的求解。
群智能算法：受动物群体智能启发的算法

1. 遗传算法（GA）

GA：求解问题时，从多个解开始，通过一定的法则迭代产生新的解。

遗传算法要经过如下步骤：

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编码

群体设定

适应度函数

选择

交叉

变异

满足终止条件输出

遗传算法具有如下优点：

没有太多数学要求
高效率搜索
易于并行
个体间进行信息交换

人工生命：具有生命特征的人造系统。

2. 粒子群优化算法

粒子群优化算法流程如下:

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初始化粒子群设置参数

计算粒子适应度

个体最优群体最优

状态更新

是否满足条件

输出

3. 蚁群算法

蚁群算法在解决离散组合优化方面具有良好性能。在学习之前，需要掌握如下概念：

信息素跟踪：按照一定概率沿着信息素较强的路径觅食
信息素遗留：在走过的路上释放信息素。
信息素启发因子α：反映蚁群在路径搜索中随机性因素作用强度。α过大可能陷入局部最优。
期望值启发式因子β：反映先验性、确定性因素作用强度。搜索速度加快，过大陷入局部最优

根据蚁群信息素浓度的更新规则，可分为三种不同的模型：

蚂蚁圈系统：利用全局信息，蚂蚁完成一个循环后，更新所有路径上信息。效果最好
蚂蚁数量系统：利用局部信息，蚂蚁每走一步都更新
蚂蚁密度系统：同上

4. 问题解析

问题1

错，当适应度函数选择不当时，遗传算法确实可能陷入局部最优值，但遗传算法个体之间可以共享信息，不容易陷入局部最优值。相应的，如果粒子群间没有共享信息，就容易陷入局部最优解。

问题2
遗传算法结束条件：

计算结构收敛到最优解或者算法达到了规定的迭代次数。

问题3
什么时候适用进化算法？

对于问题的解决没有现成算法或现成算法复杂度太高时，使用进化算法。