Multimodal Multi-Objective Football Game Algorithm for Optimizing Test Task Scheduling Problems

This paper presents an innovative approach to addressing the complex challenges posed by Test Task Scheduling Problems (TTSPs) through the utilization of Multimodal Multi-objective Football Game Algorithm (MM-FGA). TTSPs hold significant importance in industries where testing plays a critical role in ensuring product quality. This study outlines the integration of MM-FGA with the Normalized Factor Random Key (NF-RK) encoding scheme, tailored to the discrete nature of TTSPs. Four real-world TTSPs are investigated with the objectives of minimizing makespan and mean workload. Comparative analyses are conducted against other prominent multiobjective algorithms, including NSGA-II, DN-NSGA-II, Tri-MOE-TA&R, MP-MMEA, and MO-Ring-PSO-CD. The results exhibit MM-FGA's competitive performance, particularly in terms of the diversity of solutions obtained in the decision space. This underscores MM-FGA's prowess in addressing the multimodality challenges of optimization problems. The study further suggests the prospect of advancing step-size control strategies through meta-optimization, aiming to refine the algorithm's exploration and exploitation balance for even more potent optimization outcomes. Overall, MM-FGA demonstrates promise in solving multimodal multiobjective discrete problems like TTSPs, while indicating room for future enhancements.