自適應細菌覓食的FCM聚類優化算法研究

劉志鵬 胡亞琦 張衛衛

摘? 要： 在初始化FCM聚類算法時，聚類類別數需要手動去設置，并隨機初始聚類中心，導致此算法極其容易陷入局部最優值。通過利用改進的細菌覓食算法，進行FCM算法的聚類中心的初始化，解決FCM算法對初始聚類中心敏感的問題;通過一些有效性的指標，對FCM算法和優化FCM算法進行評估，指標說明了優化FCM算法更好。在仿真實驗中，將優化FCM算法和標準FCM算法用到多類圖像分割中，進行了圖像分割的準確性和實時性的比較，且驗證了所述的優化算法的實時性。

關鍵詞： FCM算法; 自適應細菌覓食; 聚類優化; 算法評估; 仿真實驗; 圖像分割

中圖分類號： TN911.73?34; TP391.4? ? ? ? ? ? ?文獻標識碼： A? ? ? ? ? ? ? ? ? ? ? 文章編號： 1004?373X（2020）06?0144?05

Research on FCM clustering optimization algorithm for self?adaptive bacterial foraging

LIU Zhipeng， HU Yaqi， ZHANG Weiwei

（School of Electronic and Information Engineering， Lanzhou Jiaotong University， Lanzhou 730070， China）

Abstract： As the initialization of the FCM clustering algorithm is performed， the number of clustering categories needs to be set manually and the clustering center is initialized randomly， which makes this algorithm extremely easy to fall into the local optimum. The clustering center of FCM algorithm is initialized by means of the improved bacterial foraging algorithm to solve the problem that FCM algorithm is sensitive to the initial clustering center. The FCM algorithm and the optimized FCM algorithm are evaluated with some validity indexes， which shows that the optimized FCM algorithm is better. In the simulation experiments， the optimized FCM algorithm and the standard FCM algorithm were used in the multi?class image segmentation to compare their accuracy and real?time performance for image segmentation， by which the real?time performance of the proposed optimization algorithm was verified.

Keywords： FCM algorithm; self?adaptive bacterial foraging; clustering optimization; algorithm assessment; simulation experiment; image segmentation