Title :
Cuckoo Search Clustering Algorithm: A novel strategy of biomimicry
Author :
Goel, Samiksha ; Sharma, Arpita ; Bedi, Punam
Author_Institution :
Dept. of Comput. Sci., Delhi Univ., Delhi, India
Abstract :
A novel, nature inspired, unsupervised classification method, based on the most recent metaheuristic algorithm, stirred by the breeding strategy of the parasitic bird, the cuckoo, is introduced in this paper. The proposed Cuckoo Search Clustering Algorithm (CSCA) yields good results on benchmark dataset. Inspired by the results, the proposed algorithm is validated on two real time remote sensing satellite- image datasets for extraction of the water body, which itself is a quite complex problem. The CSCA makes use of Davies-Bouldin index (DBI) as fitness function. Also a method for generation of new cuckoos used in this algorithm is introduced. The resulting algorithm is conceptually simpler, takes less parameter than other nature inspired algorithms, and, after some parameter tuning, yields very good results.
Keywords :
geophysical image processing; optimisation; pattern classification; pattern clustering; remote sensing; search problems; visual databases; water resources; Davies-Bouldin index; biomimicry; breeding strategy; cuckoo search clustering algorithm; fitness function; metaheuristic algorithm; nature inspired classification method; parasitic bird; real time remote sensing satellite-image; unsupervised classification method; water body extraction; Accuracy; Algorithm design and analysis; Benchmark testing; Clustering algorithms; Optimization; Remote sensing; Satellites; Cuckoo Search Clustering Algorithm (CSCA); Cuckoo Serach; Davies-Bouldin index (DBI); Satellite Image;
Conference_Titel :
Information and Communication Technologies (WICT), 2011 World Congress on
Conference_Location :
Mumbai
Print_ISBN :
978-1-4673-0127-5
DOI :
10.1109/WICT.2011.6141370