特徵選擇是分類問題中降低問題維度的一種重要前處理技術，能消除冗餘和不相關的特徵，留下有用的特徵進行分類，提高分類準確率。隨著資料集維度增加，搜尋空間將急遽加大，這對各種最佳化演算法來說是一個挑戰。本研究希望能提出演算法，以小量計算資源找到一組少量且具有良好分類效果的特徵子集。研究基於當今主流的粒子群演算法，加入搜尋空間調整相關設計、參考相對優秀粒子的較差粒子調整策略，以及一個具方向性的新粒子群產生策略，該策略使用外部族群更新與紀錄演化過程中找到的多組特徵子集，並將其作為產生新粒子群的參考點。實驗結果與文獻演算法比較後，顯示所提出之演算法機制與設計具有良好的分類效能與較少的特徵數。

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