本論文提出自我學習蟻群演算法的觀念，並應用於機器人最佳路徑規劃。本方法包含局部費洛蒙更新、螞蟻歸巢法與自我學習費洛蒙更新機制；自我學習費洛蒙更新機制包含了階段式費洛蒙更新機制與自我學習階段選擇機制。當陷入搜索停滯時，這方法能夠使系統自動的切換至新的更新方式以提升搜索效率。同時透過局部費洛更新機制改良禁忌表所產生的鎖死問題，並使用螞蟻歸巢法初步排除冗餘路徑。相較於蟻群系統(ACS)與改良式蟻群系統(IACS)，模擬結果證實本文提出的方法於最短路徑長、平均路徑常和最佳路徑成功率皆有較好的表現。

This thesis proposes the concept of Self-Learning Ant Colony Optimization (SLACO), and applies it to the optimal robot path planning. Therein, SLACO includes Partial Pheromone Updating, Ant Homing Method and Self-Learning Pheromone Updating Mechanism (SLPUM). SLPUM includes a phased pheromone updating mechanism and a self-learning stage selection mechanism. When the search stagnation happened, this method allows the system switch automatically to new updated mechanism to improve search efficiency. The other improvements are the strengthening of the Partial Pheromone update algorithm to improve issues arising from the deadlock problems in the taboo table, as well as homing method reduces redundant path preliminarily. Simulation results show the proposed approach has a better performance in terms of shortest distance, mean distance, and successful rate of the optimal paths than those obtained by the Ant Colony System and Improved Ant Colony System.

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