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APSO-TVAC 演算法應用於資料分群問題

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圖 1. PSO 之流程圖  而較著重於參考個體最佳以搜尋全域最佳解,反 之隨著迭代增加,c 2 的值較大而 c 1 的值較小,會 比較偏向於參考群體最佳以搜尋區域最佳解,此 時演算法會具有較大之效能[18],c 1   與  c 2   之線 性遞減如公式(4)與公式(5)所示。此外針對粒子 搜尋過程中,可能發生過早收斂的情況,文獻中 亦有針對其求解標準差進行比較分析。  ( f i ) c i itercitercc111 1max⎟+ ⎠⎜⎞⎝×⎛−= (4) ( f i ) c i itercite
表 5:七種分群演算法的錯誤率比較表  資料集  評估  標準  K-means (%)[8]  PSO  (%)[8]  NM-PSO (%)[8]  K-PSO (%)[8]  K-NM-PSO (%)[8]  PSO-TVAC (%)  APSO-TVAC (%)  Iris  平均值 17.80  12.53  11.13  10.20  10.07  10.47  10.00  (標準差) (10.72)  (5.38)  (3.02)  (0.32)  (0.21)  (2.08)  (0.00
表 6:七種分群演算法的計算適應函數總數比較表  資料集  K-means  [8]  PSO [8]  NM-PSO [8]  K-PSO [8]  K-NM-PSO [8]  PSO-TVAC  APSO-TVAC Vowel 180  16,290 10,501 15,133 9,291  9,720  9,720  Iris 120 7,260  4,836  6,906  4,556  4,320 4,320  Crude Oil  150  11,325  7,394  10,807  7,05

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