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imdmoea's Introduction

IMDMOEA CODE

一、算法的总体流程

二、算法中各个函数接口的说明

函数名 函数功能 入参 出参
gradient_predict 在决策空间对种群进行聚类预测，
并以向量形式输出种群下一时刻可能的进化方向及进化步长		 Pt1: t-1时刻的种群
Pt2: t 时刻的种群
Global: 包含变化严重程度和频率信息
center:连续时刻的聚类中心点位置
seq:聚类中心点的迭代序号
idx:种群中个体的归属类		 Population_cluster:种群的聚类结果
center:聚类中心点信息（包含进化向量）
P1:预测的下一时刻种群
函数名 函数功能 入参 出参
PS_Sampling 在决策空间中生成探索种群 Pt2:t时刻的种群
sample:对每个个体的采样数目
seq:聚类中心点的迭代序号
center:聚类中心信息（包含进化向量）
Population_cluster:种群的聚类结果
Global:变化频率等基础信息		 P2:生成的探索种群
history_pop:P2的副本信息
函数名 函数功能 入参 出参
PF_Sampling 用于预测下一时刻POF可能出现的区域，并选择探索种群中的优秀个体进行高斯变异 Pt1:t-1时刻的种群
Pt2:t时刻的种群
history_pop:探索种群
acc:POF的预测精度
Global:环境变化的基础细腻系		 P3:诱导变异生成的种群
函数名 函数功能 入参 出参
CrowdingDistance1 在决策空间中计算种群中个体的拥挤度距离 PopObj:决策空间种群中个体的坐标 CrowDis:每个个体的拥挤度距离

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