文章基本信息

标题：Study of Ant Colony Algorithm using Adaptive Schematization Methodology Based on Prognosticative Learning
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作者：Arjun Arora ; Ashish Pant ; R P Arora 等
期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2012
卷号：3
期号：1
页码：3170-3175
出版社：TechScience Publications
摘要：To solve the path schematization in the complicated environments, a new adaptive schematization methodology using ant colony algorithm (AACA) based on prognosticative learning is presented. A novel prognosticative operator for direction during the ant colony state transition is constructed based on an obstacle restriction method (ORM), and the prognosticative results of proposed operator are taken as the prior knowledge for the learning of the initial ant pheromone, which improves the optimization efficiency of ant colony algorithm (ACA). To further solve the stagnation problem and improve the searching ability of ACA, the ant colony pheromone is adaptively adjusted under the limitation of pheromone. Compared with the corresponding ant colony algorithms, the simulation results indicate that the proposed algorithm is characterized by the good convergence performance on pheromone during the path schematization. Furthermore, the length of planned path by AACA is shorter and the convergence speed is quicker.
关键词：Ant colony algorithm; Artificial intelligence ;Adaptive behaviour; Combinatorial optimization;Reinforcement learning; Path Schematization;Robotics;Prognosticative Learning; Adaptive Adjustment(key;words)s-xbap