A Hybrid Crow Search And Penguin Optimization Algorithm (CPMM) for Efficient Cloud Workflow Scheduling

In cloud computing, efficient workflow scheduling is essential for resource management and task execution. However, many existing algorithms struggle to strike a balance between exploration and exploitation. This study introduces a novel hybrid algorithm, CPMM, which merges the Crow Search Algorithm (CSA) and the Penguin Optimization Algorithm (POA) with Adaptive Memory Management (AMM). CPMM dynamically adjusts the memory size, maintaining a diverse set of high-quality solutions and improving scheduling efficiency. Simulations show that CPMM outperforms CSA and POA individually, achieving better makespan, cost savings, and resource utilization. The adaptive memory approach helps CPMM balance exploration and exploitation, making it a promising solution for cloud-based workflow scheduling.