Parallel Metaheuristic Algorithms for Task Scheduling Problems

This study addresses the task-scheduling optimization challenges in parallel computing systems using a novel meta-heuristic framework. We analyze the differential evolution in task scheduling and propose an advanced shift-chain methodology to improve the cooperation between scheduling components. The proposed framework introduces a wait-ing time-based neighborhood exploration strategy for handling complex task dependencies, along with two parallel implementation approaches: basic matching vector (MV) parallelization and an event-driven strategy. The experi-mental results demonstrated superior solution quality and computational efficiency compared with existing methods, particularly in large scale problems. The modular design of this framework enables practical applications in moderncomputing environments.