# Research on Multi-Objective Evolutionary Algorithms Based on Large-Scale Decision Variable Analysis
## 4 Experiment
### Container Strategy Experiment  
To validate the effectiveness of the DVA-TPCEA algorithm in practical applications, this section constructs a large-scale container multi-objective scheduling constrained optimization strategy based on the algorithm, tailored to meet the demands of containerized environments. This strategy comprehensively considers multiple objectives, including load balancing, resource utilization, cost, and cluster imbalance, aiming to provide an efficient resource scheduling solution for Kubernetes.  
In the simulation tests, we selected the iteration efficiency and load balancing degree of the algorithm as the main evaluation metrics. These two indicators can directly reflect the algorithm's performance in resource allocation and system stability, thus verifying its applicability in practical applications. By analyzing resource consumption and costs under varying numbers of Pod application deployments in a Kubernetes cluster with 50 nodes, we compared the effects of Kubernetes' LRP and BRP strategies with the proposed scheduling algorithm. This comparison will help to comprehensively assess the superiority of the proposed algorithm and its potential value in practical applications.  
To validate the actual performance improvement achieved by the proposed k8s scheduling strategy optimization for the cluster, this study analyzes the number of al  
Figure 4: The population parallel coordinate map obtained by LMEA and DVA-TPCEA algorithms on target 10 WFG4 problem.  
Figure 3: The population parallel coordinate map obtained by LMEA and DVA-TPCEA algorithms on target 10 WFG1 problem.  
gorithm iterations. Figure 5 illustrates the iteration counts during the actual operation of the three algorithms, showing that the DVA-TPCEA algorithm exhibits efficient performance throughout the iterative process. In the early stages of the optimization process, this algorithm demonstrates rapid convergence. Moreover, compared to other optimization algorithms, the DVA-TPCEA algorithm typically achieves better solutions at the same number of iterations. This result provides strong evidence that the improved algorithm has a faster iteration speed and higher iteration efficiency compared to the original algorithm. This advantage stems from the effective utilization of genetic characteristics in the encoding variables of the population individuals, allowing the algorithm to converge more swiftly and thereby quickly identify solutions that approximate the ideal targets.  
As shown in Figure 6, the comparison of balance among algorithms indicates that the LRP algorithm has a lower balance than the BRP algorithm, while the DVA-TPCEA algorithm shows the lowest balance. This implies that, in terms of resource allocation, the DVA-TPCEA algorithm achieves more balanced utilization, reducing the phenomenon of uneven resource distribution. The LRP and BRP algorithms mainly focus on CPU and memory as resource indicators when deploying Pod applications. This strategy ensures that the number of Pods deployed on each node is relatively even, avoiding situations where some nodes are overloaded while others are idle. However, this simple balance allocation strategy may increase overall resource consumption costs as it does not fully consider the trade-offs and optimization between different resource metrics. In contrast, the DVA-TPCEA algorithm takes more factors into account during resource scheduling. It considers not only conventional resource metrics such as CPU and memory but also factors in resource utilization, resource consumption cost, cluster load, and cluster imbalance. This comprehensive consideration enables the DVA-TPCEA algorithm to more accurately assess the resource status of each node, thereby making more optimized scheduling decisions.  
Figure 5: Number of iterations of LRP, BRP, and DVA-TPCEA runs.  
Figure 6: Cluster load balancing under LRP, BRP, and DVA-TPCEA policies.