近幾年來，工作指派問題 (task assignment problem, TAP) 被學者們針對各種不同的情境和特性進行大量研究，之所以被人們所重視，是因為在分佈式運算系統(distributed computing system) 裡，工作是否被恰當的指派決定了一個系統能否發揮最大的運算能力及體現最極致的表現。分佈式運算系統的工作指派問題裡，最終的目標無非是將數個工作指派到數台電腦上，可以將工作在機台上產生的運作成本 (execution costs)及兩兩在不同機台執行工作所產生的交互作用成本 (communication costs) 的總合最小化，同時還要滿足各種資源的限制。對於工作指派問題來說，超過三個工作以上的問題即可被視為非線性整數規劃 (nonlinear integer programming) 的問題且屬於NP-hard，因此對於大型的工作指派問題，要在可以合理的時間內找出最佳解是相當困難的。
本篇論文裡，我們採用簡化群體演算法 (simplified swarm optimization, SSO) 並結合以熵 (entropy) 為概念的區域搜尋法來解決工作指派問題。此外一個早先被學者所提出的標準化懲罰式適應值函數 (normalized penalty fitness function) 也將會被應用，目的是為了平衡目標函數值及滿足各種資源的限制兩者所產生的衝突。所提出的方法也將會和著名的基因演算法 (genetic method, GA) 、混合粒子群演算法 (hybrid particle swarm optimization, HPSO)、新式全局和諧演算法 (novel global harmony search, HGHS) 及改進差異進化演算法 (improved differential evolution, IDE)進行比較。就實驗結果而言，我們所提出的簡化群體-熵演算法(simplified swarm optimization with entropy local search, SSO-E)，相較於其它方法，在效能與效率上均占有優勢。

Task assignment problem (TAP) has been extensively studied under different characteristics in recent years, because task assignment is a crucial step in distributed computing system where system performance and computing power usage can be improved by properly allocating tasks to processors. The target of the task assignment problem in distributed computing system is to assign program tasks to processors so as to minimize overall costs made of execution and communication costs and satisfy with various limited resources constraints simultaneously within a system. The task assignment problem with more than three processors is not only considered to be NP-hard, but also a nonlinear integer optimization problem, therefore it is difficult to find the exact solution for a large-scaled problem in an acceptable time.
In this paper, we present an algorithm based on Simplified Swarm Optimization (SSO) to solve the problem, in the meantime, a local search optimizer based on the concept of entropy is embedded with SSO so as to better exploit local optimum. In addition, an earlier reported method called normalized penalty fitness function aimed to trade off the costs and the constraints is adopted in this paper. Afterwards, the results of the proposed simplified swarm optimization with entropy local search(SSO-E) is demonstrated by comparing with well-known Genetic method and other recently reported meta-heuristic algorithms such as Hybrid Particle Swarm Optimization (HPSO), Novel Global Harmony Search (NGHS) and Improved Differential Evolution (IDE). Finally, the experimental results show that that the proposed SSO-E produces superior quality of solutions among other compared methods on solving task assignment problem in distributed computing system.

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