本文主要提出改進式粒子尋優法模擬銷售員送貨路徑最佳化問題，整個演算流程分為兩個階段，第一階段為前置處理作業，包含新式的路徑排序方法、隨機交換策略以及基於模糊C-Means分群法的分群機制；新式的排序方法為以角度為參考的路徑排序法，此法改善了傳統旅行商問題會面臨的交叉現象，而基於模糊C-Means分群法的粒子尋優法應用於旅行商問題為一個新的概念，主要的貢獻在於當城市數量龐大時，降低各群聚的運算量，藉以取得更好的結果；第二階段為粒子尋優法，其中包含突變及交配策略，其概念來自於遺傳算則，將其概念結合在粒子尋優法搜尋策略中，藉以提升粒子尋優法的尋解效能。
　　本文中，粒子尋優法解旅行商問題的方法為空間轉換法(Space transformation)，其主要原理是將所有城市以浮點數來表示其順序，其中浮點數所組合成的向量稱為空間向量(Space vector)，粒子尋優法的數值運算皆架構在空間向量上，而空間向量透過映射(Mapping)的技術便能夠轉變為城市的排序(Tour)，用向量來描述路徑的方法，能夠大大節省運算量，在最佳化領域中，節省電腦運算量是提升效能的主要方法之一。
　　對於演算法的效能分析，本文利用固定運算時間的方式比較不同方法效率的優劣，而本文也著重於分群效率的比較，並證明分群法在解高城市數問題時能夠取得較優異的結果。

This dissertation proposed a improved version of Particle Swarm Optimization (PSO) to solve Traveling Salesman Problem (TSP). This evolutionary algorithm includes two phases. First phase includes Fuzzy C-Means clustering, a rule-based route permutation, a randomly swap strategy and a cluster merge procedure. This approach generates an initial route without cross link problem, which make TSB problem can be solved more efficiently by the proposed PSO algorithm. The use of sub-cluster to solve TSP is also a novel procedure in the literature, which reduces the complexity and obtains better performance for problems with a large number of cities. The second phase is the use of the improved PSO procedure, a Genetic Algorithm hybrid strategy, which solves the TSP with better performance.
In this study, the space transformation is applied for route tour in which space vector transfers into tour through mapping function. This method could decrease the complexity and raise the evolutionary efficiency. Fixed runtime was applied to compare the efficiency of novel algorithm with other algorithms. This research also focuses on the comparison of clustering efficiency, and the result reveals that the clustering method reaches a better result for the cases with a large number of cities.

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