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Bat algorithm (BA) is one of the most recent bio-inspired algorithm. It is based on the echolocation behavior of microbats. The standard BA is proposed only for continuous optimization problems. In this paper, we try to solve the graph coloring problem using a binary bat algorithm. To show the feasibility and the effectiveness of the algorithm, we have used the DIMACS benchmark, and the obtained results are very encouraging.

The quality of fingerprint images strongly affects any fingerprint biometric system. In order to deal with low quality fingerprint images and overcome the limits of traditional enhancement techniques, a cuckoo-search based algorithm for fingerprint image contrast enhancement is proposed in this paper. The algorithm combines the use of cuckoo search for both the gray level mapping technique for contrast enhancement, and a new objective function as a quality metric for global fingerprint image enhancement. The enhancement scheme is assessed on low quality images from a reference database, the FVC 2000. The obtained results show that the proposed cuckoo algorithm can qualitatively and quantitatively enhance fingerprint images, on the general level of both noise eradication and quality metrics. In addition, the proposed algorithm can visually and numerically clarify the fingerprints ridge structure, thus, the minutiae detection process in comparison with the use of a traditional enhancer. Hence, the proposed algorithm has proven to be very efficient for fingerprint image quality enhancement for easy further processing.