An Efficient Ant Colony Optimization Clustering Algorithm

This paper presents a new algorithm for clustering which is called an “efficient ant colony optimization clustering algorithm” (EACOC) based on a classic algorithm “LF algorithm”. We have proved the algorithm efficiency when dealt with a big variety of different data as well as providing high quality and converging speed simultaneously. This is considered as the outcome of many changes we have made including redefining the digital manner of ants, setting new formula to find out the degree of similarity and measuring the distance between objects; as well as creating a process to determine the degree of similarity between the collections resulting from the repeated processes. Experimental results show, by using clustering benchmarks indicate, that this suggested algorithm is the best of (LF) Algorithm, as it could defeat the defects found in (LF) involving; the law converging speed and the big number of repeating processes.