雲端運算是一種新興的商業計算模型。它將計算任務分佈在大量計算機構成的資源池上，使各種應用系統能夠根據需要獲取計算力、存儲空間和各種軟體服務。在目前對雲端運算任務分配問題的研究中，絕大多數學者只考慮單一目標，例如使得總成本最小化、總生產時間最小化等。然而，各虛擬機負載均衡情況，系統可靠度情況，能源消耗情況等因素都對雲端運算服務質量有影響，並且，大多目標間存在衝突關係，例如：可靠度增加會導致總成本增加。由此，我們很難以單一目標去評價一個任務分配方案的優劣。因此，從多目標的角度思考雲端運算的任務分配問題才能夠適應實際應用的發展。
雲端運算由於其伺服器規模龐大、用戶群體廣泛，面臨兩大問題：對雲端運算資源進行合理分配、對大量的應用任務進行高效調度。雲端運算的任務分配問題從本質上來講是一種優化組合類型的NP-hard問題，很難在多項式時間複雜度之內求得全域最優解。近年相關研究多使用啟發式演算法（如基因演算法、粒子群演算法等）來進行問題的求解。此類方法希望能發揮啟發式演算法的特長，在可以接受的演算時間內找到高品質的解。
在本研究中，會基於多目標粒子群演算法(Multi-Objective Particle Swarm Optimization，MOPSO)中建立外部集合儲存非淩駕解的流程與思路，並藉鑒簡化群演算法(Simplified Swarm Optimization, SSO)方便、高效的更新機制，發展出一種適用於多目標問題的新演算法，多目標簡化群演算法(Multi-Objective Simplified Swarm Optimization，MOSSO)。在此基礎上，爲了增強該演算法對可行解空間的搜索能力，本篇論文中設計了動態的參數，用以平衡演算法的全域與局部搜索能力。
爲了驗證本研究所提出演算法的效能優劣，本研究使用隨機生成的雲端資源資料進行模擬分析，並與其他演算法進行比較。最終的實驗結果顯示本文所提出的演算法對於處理多目標的雲端運算任務分配問題，相較于傳統的多目標粒子群演算法(MOPSO)以及精英機制的非淩駕排序基因演算法(NSGA II)，能獲得最優質的解。

Nowadays, cloud computing and big data are changing the enterprise. Cloud computing, as a new business computing mode, distributes computing tasks across resource pools made up of a large number of computers for large-scale calculation. In the current research on the task assignment problem of cloud computing, most scholars consider single-objective programming, for example minimizing the cost or makespan. However, factors such as load balancing of virtual machines, reliability of system, and energy consumption can influence the quality of the cloud computing service. Therefore, in order to adapt to the development of practical applications, multi-objective programming should be considered in the task scheduling problem of cloud computing.
Cloud computing refers to storing and accessing applications, data or services over the internet remotely. Due to its large server size and wide user base, cloud computing is faced with two major problems: reasonable allocation of cloud computing recourses and efficient scheduling of a large number of application tasks. The task scheduling problem of cloud computing is essentially an NP-hard problem of combinatorial optimization which is hard to find the global optima within polynomial time complexity. In recent researches, heuristic algorithms (such as genetic algorithms and particle swarm optimization) are used to solve the problem. Such methods can hopefully make use of the strength of heuristic algorithms and find high-quality solutions within an acceptable running time.
This paper proposes a new algorithm (Multi-Objective Simplified Swarm Optimization，MOSSO) for multi-objective problems, based on a new, convenient and efficient heuristic algorithm called Simplified Swarm Optimization (SSO) and using the procedure and idea of establishing a repository in the Multi-Objective Particle Swarm Optimization (MOPSO). In order to increase the search ability of feasible solution space in this algorithm, this paper designs dynamitic parameters to make the mutation rate large at the early stage to enhance global search ability and the mutation rate small at late stage to enhance local search ability.
In order to verify the performance of the proposed algorithm, this paper uses randomly generated cloud computing data for simulation analysis and makes comparison with other algorithms. The final result proves that the method proposed in this paper can take both efficiency of algorithm and quality of solutions into account, and play the strength of Simplified Swarm Optimization to help users find the best quality solution and finishing multiple objectives such as minimizing total power and minimizing makespan at the same time.

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