本論文將競賽旅程問題發展為多目標最佳化問題，利用原先競賽旅程問題的 旅行總距離當作目標之一，並額外增加了最長旅行距離為另外一種目標。本論文 使用限制處理機制來求解此問題，藉由發展成多目標問題讓競賽旅程問題更貼近 現實生活中，讓使用者不再只能選擇旅行總距離。
本論文用群體式模擬退火法當作區域搜尋，另外比較使用隨機鄰域函式跟變 動鄰域函式的效能差異，並且針對文獻中的鄰域函式作分析，將搜尋能力較差的 鄰域函式剔除。因為變動鄰域函式使用的鄰域順序會大幅的影響搜尋能力，因此 針對本論文提出的做法也會將鄰域函式依照搜尋能力做排序，藉由使用恰當的順 序來增進搜尋效能，最終結果發現使用變動鄰域函式的效能較優。在隨機鄰域函 式的方法中，能夠挑選到所有鄰域去做搜尋，並在這之中選最好的來替換；而使 用變動鄰域函式時，搜尋的區域只有某種鄰域函式能夠搜尋到的範圍，因為解空 間的差異，變動鄰域函式能夠找到較好的解。確認完對於單目標的搜尋效能之後，
將此作法套用在多目標最佳化問題上，因為此問題多目標最佳化並沒有文獻可以 比較，因此本論文列出找到的所有最佳解。
因為起初是要比較變動鄰域函式是否能夠比隨機鄰域函式更適合用在此問 題，因此在模擬退火法的部分在換另一種鄰域時，會將溫度升溫回原先使用的溫 度，而不是接著停止時的溫度做搜尋，以避免溫度過低而導致換鄰域沒有效果，
未來可以將此作法改為從頭到尾都用一樣的初始溫度，而不是做一定的次數後升
溫，藉由此方式看是否能讓隨機鄰域函式的效能增加；對於鄰域還能夠有待改良，
未來可以試著改良或者設計新鄰域，藉由此方法能夠更有效率的去搜尋，或許藉 由此做法能讓變動鄰域函式的效能超越隨機鄰域函式；另外未來也可試著加入基 因演算法。
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