A Mixed-Heuristic Quantum-Inspired Simplified Swarm Optimization Algorithm for scheduling of real-time tasks in the multiprocessor system

Abstract

Real-time task scheduling in multiprocessor (MP) systems has become more critical. As quantum computing advances, scholars have proposed different Quantum-Inspired (QI) Optimization Algorithms to optimize real-time task scheduling problems. In this research, we first take advantage of the Simplified Swarm Optimization Algorithm (SSO) and then propose a Quantum-Inspired Simplified Swarm Optimization (QISSO) to not only overcome the early convergence problem but also enhance the exploration of solution space without premature. Furthermore, we propose the Mixed-Heuristic QISSO for real-time task scheduling in the MP system to improve the scheduling. The proposed MHQISSO achieves the best average percentage of success within the shortest running time in five different size task sets according to two scenarios, Earliest Deadline First (EDF) and Shortest Computational Time First (SCTF).