Study of Self-Learning Ant Colony Optimization with Application to Optimal Robot Path Planning
| Autor: | Cheng-Feng Hung, 洪誠鋒 |
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| Rok vydání: | 2014 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 102 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. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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