9、多目标优化与城市固废预测：技术融合与应用探索

多目标优化与城市固废预测：技术融合与应用探索

多目标优化在NOMA - IoT网络中的应用

在NOMA - IoT网络中，最大化网络总速率和最小化功耗可被构建为多目标优化（MOO）问题。为解决这一问题，提出了MOGA - RL算法，以下是该算法的详细介绍。

MOGA - RL算法

Algorithm 2: MOGA - RL
Input: Population size = max_pop, Maximum number of generation = max_gen, MOO problem
1 Initialize:
2 foreach s, a ∈Q −Table do
3     Initialize: Q(s,a)
4 t ←0
5 Generate random population set of size max_pop
6 for t ←1 to max_gen do
7     Evaluate fitness of the population based on step 2 of Algorithm 1
8     Apply ε - Greedy Action Selection policy
9     Generate off - spring population of size max_pop with ac, am
10    Obtain reward for each combination of ac, am
11    Update Q - Table
12    Use selection to generate chromosomes for next generation b