即時任務已被應用於各類重要領域，如自動化控制系統、嵌入式系統、電信系統、以及多媒體應用。提升多處理器環境的即時任務排程效率亦至關重要。近年量子計算與電腦的迅速發展，學者開始應用量子計算來改良最佳化演算法效率。在即時任務排程問題中，過往研究曾基於量子啟發式基因演算法（Quantum-Inspired Genetic Algorithm, QIGA），提出了混合量子啟發式基因演算法（Hybrid Quantum-Inspired Genetic Algorithm, HQIGA）來提升排程效率。此方法雖然提升了隨機性，卻因收斂過快的問題而使排程品質尚有改善空間。
為了改善過往量子啟發式演算法過早收斂的問題，本研究利用簡化群體演算法（Simplified Swarm Optimization, SSO）之階梯式更新機制來調整希爾伯特空間中由量子位元（Qubit）描述的變數並執行量子測量，提出了量子啟發式簡化群體演算法框架（Quantum-Inspired Simplified Swarm Optimization, QISSO）。以避免量子解的過早收斂並利用量子計算之隨機性來提升解空間的探索域、發展出更高品質的解。此外，我們針對多處理器的即時任務排程問題提出混合量子啟發式簡化群體演算法（Hybrid Quantum-Inspired Simplified Swarm
Optimization, HQISSO）。在模擬實驗中，我們在傳統電腦上模擬實測量子簡化群體演算法（QISSO）與量子基因演算法（QIGA）之函數最佳化效率。為了進一步證實所提出演算法的有效性，我們同時在真實量子電腦 IBMQ-Santiago 中進行模擬。實驗結果證實在函數最佳化中量子簡化群體演算法能獲得較優異的平均解品質。本研究亦模擬各種數量規模的即時任務集與處理器個數，並與 HQIGA、混合粒子群演算法（Hybrid Particle Swarm Optimization, HPSO）、混合基因演算法（Hybrid Classical Genetic Algorithm, HCGA）進行比較。HQISSO 在混合最早截止時間優先（Earliest deadline first, EDF）與最短計算時間優先（Shortest Computational Time First, SCTF）的五種不同規模之隨機即時任務集中
皆能發展出最佳的平均成功排程百分比，並維持量子位元優異的收斂性。

Modern real-time (RT) tasks are utilized in numerous practical areas such as automation control systems, embedded systems, and multimedia applications. Therefore, RT task scheduling in a multiprocessor (MP) system has become more critical. Researchers have proposed the Hybrid Quantum-Inspired Genetic Algorithm (HQIGA) to optimize RT task scheduling in the MP systems as quantum computing advances. While quantum-inspired approaches have improved the stochasticity of classical algorithms, there remains room for improvement in the scheduling quality of theoretical approaches due to the issue of early convergence.
In this research, we proposed a Quantum-Inspired Simplified Swarm Optimization (QISSO) framework to overcome the premature issue. By stepwise update scheme of the Simplified Swarm Optimization Algorithm (SSO), we successfully enhance the exploration of solution space without premature. We further proposed the Hybrid Quantum-Inspired Simplified Swarm Optimization Algorithm (HQISSO) for RT task scheduling in the MP system to improve the scheduling quality.
In the simulation experiments, the proposed QISSO achieved better average fitness than QIGA in terms of functional optimization. Additionally, we simulated various numbers of random task sets and processors, comparing with HQIGA, Hybrid Particle Swarm Optimization (HPSO), and Classical Genetic Algorithm (CGA). The proposed HQISSO 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).

摘要
Abstract
Contents
List of Figures
List of Table
1. INTRODUCTION ------------------------------------------ 1
2. LITERATURE REVIEW ------------------------------------- 6
3. METHODOLOGY ------------------------------------------- 13
4. SIMULATION RESULTS ------------------------------------ 45
5. CONCLUSION AND FUTURE WORKS --------------------------- 60
REFERENCE ------------------------------------------------ 62

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