Parallel Metaheuristic Algorithms for Task Scheduling Problems - Mao, Sirui; Edahiro, Masato (INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2025)
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 modern computing environments.