A huge number of genomic information, including protein and DNA sequences, is generated by the human genome project. Deciphering these sequences and detecting local residue patterns of multiple sequences are very difficult. One of the ways to decipher these biological sequences is to detect local residue patterns from them. However, detecting unknown patterns from multiple sequences is still very difficult. In this thesis, we propose an algorithm, based on the Gibbs sampler method, for identifying local consensus patterns (motifs) in monomolecular sequences. We first designed an ACO (ant colony optimization) algorithm to find a good initial solution and a set of better candidate positions for revising the motif. Then the Gibbs sampler method is applied with these better candidate positions as the input. The required time for finding motifs using our algorithm is reduced drastically. It takes only 20 % of time of the Gibbs sampler method and it maintains the comparable quality.