An Improved Whale Migration Algorithm for Global Optimization of Collaborative Symmetric Balanced Learning and Cloud Task Scheduling
H. Lu, Shenghao Cheng, Xinsheng Zhang · Symmetry · 2025
In today’s complex and ever-changing fields of science and engineering, intelligent optimization algorithms have become a key tool. However, the complexity of the problem itself often poses a severe challenge to the performance of the algorithm. The whale migration algorithm stands out among numerous optimization algorithms with its simple and efficient implementation and has received extensive attention. However, when confronted with complex issues such as global optimization and task scheduling, it still exposes some deficiencies including low initial population symmetry (i.e., poor distribution uniformity and insufficient balance between exploration and exploitation in iterative processes). The development ability of the algorithm is relatively weak, making it difficult to conduct an effective search and optimization in the complex problem space. The task scheduling strategy is not optimized enough, which affects the application of the algorithm in actual task scheduling scenarios. To overcome these challenges, this paper proposes an improved whale migration algorithm. Based on inheriting the original advantages of the whale migration algorithm, this algorithm effectively solves the above problems by introducing a new mechanism. The CEC2021 test function set was selected, and the effectiveness of the proposed strategy was verified through point-by-point ablation experiments. The algorithm was comprehensively verified through the CEC2022 test problem set, verifying the effectiveness and robustness of the algorithm in global optimization problems. Furthermore, the proposed algorithm was tested for cloud task scheduling problems of different scales. The experimental results show that the proposed algorithm can reduce the total scheduling cost by about 9% or more.