Modified fuzzy ants clustering approach
Being trapped in local optima within clustering search space currently is nontrivial difficulty. In order to relieve such a difficulty, even using genetic algorithm to optimize the initial clusters for fuzzy c-means is still unsatisfied. Since genetic algorithm intensifies only the current best solution, it will easily gets trapped in local minima. The ant colony system, dissimilarly to genetic algorithm, recognizes that the solutions near the best solution are also good ones and they bring about smoothness of solution. This paper proposes a modified fuzzy ant clustering. Such a presented method is a combination of genetic algorithm, ant colony system and fuzzy c-means. It is employed in creating fuzzy color histogram in image retrieval application. The performance measurement relates to the percentages of accuracy of image retrieval. Experimental results show that the proposed approach yields the best results among others with respect to sensitivity and robustness on dealing with lighting intensity changes, quantization errors, also changes in number of images and in size of color space, even the certain-range variation of a particular parameter of clustering.
genetic algorithm fuzzy c-means ant colony system color histogram image retrieval