在本文中，將討論如何設計一個螞蟻演算法(Ant Colony Optimization Algorithm, ACO Algorithm)用來解決零壹多限制式背包問題(zero-one Multidimensional Knapsack Problem, zero-one MKP)。零壹多限制式背包問題一般定義如下：如何在符合 條限制條件下，從 項物品內選出數項物品，以期達到效益極大化。
設計螞蟻演算法主要可分為下列三個部份討論：1. 計算啟發法權值的方法(heuristic value)，2. 螞蟻搜尋解的方法(solution construction)、3. 更新費洛蒙的方法(pheromone update)。在第一章將介紹過去曾被提出過各種不同設計的螞蟻演算法。在第二章將提出針對零壹多限制式背包問題的特性，可能有那些合適的螞蟻演算法可應用在該問題上。第三章將介紹兩種參數設定方法。在第四章則是對第二章所提出的設計以範例問題做試驗。由實驗之結果得知，本文所提出之螞蟻演算法比起過去的設計所需的運算時間較短，但解的品質相同。

In this thesis, we design an Ant Colony Optimization Algorithm (ACO Algorithm) for the zero-one Multidimensional Knapsack Problem. The zero-one Multidimensional Knapsack Problem is the problem of choosing some of n items such that the corresponding profit sum is maximized without violating m constraints.
ACO algorithm can be discussed with three aspects: heuristic value, solution construction, and pheromone update. In chapter 1, we introduce some different ACO algorithms that have been discussed. In chapter 2, we propose 5 kinds of ACO algorithm for the zero-one Multidimensional Knapsack Problem. In chapter 3, we try to set parameters using two different methods. In chapter 4, we discuss the computational results between the algorithms we proposed in chapter 2. Compared with the past algorithm, the ACO algorithm we designed can solve the zero-one Multidimensional Knapsack Problem with less computing time, and the solution qualities are the same.

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