ADAPTIVE ANT COLONY OPTIMIZATION BASED GRADIENT FOR EDGE DETECTION
Abstract: Ant Colony
Optimization (ACO) is a nature-inspired optimization algorithm which is
motivated by ants foraging behavior. Due to its favorable advantages, ACO has
been widely used to solve several NP-hard problems, including edge detection.
Since ACO initially distributes ants at random, it may cause imbalance ant
distribution which later affects path discovery process. In this paper an
adaptive ACO is proposed to optimize edge detection by adaptively distributing
ant according to gradient analysis. Ants are adaptively distributed according
to gradient ratio of each image regions. Region which has bigger gradient
ratio, will have bigger number of ant distribution. Experiments are conducted
using images from various datasets. Precision and recall are used to
quantitatively evaluate performance of the proposed algorithm. Precision and
recall of adaptive ACO reaches 76.98 % and 96.8 %. Whereas highest precision
and recall for standard ACO are 69.74 % and 74.85 %. Experimental results show
that the adaptive ACO outperforms standard ACO which randomly distributes ants.
Author: Febri Liantoni,
Kartika Candra Kirana, Tri Hadiah Muliawati
Journal Code: jptkomputergg140013
