Freezing firefly algorithm for efficient planted (ℓ, d) motif search

The detection of inimitable patterns (motif) occurring in a set of biological sequences could elevate new biological discoveries. Its application in recognition of transcription factors and their binding sites have demonstrated the necessity to attain knowledge of gene function, human diseases, and drug design. The literature identifies (ℓ, d) motif search as the widely studied problem in PMS (Planted Motif Search). This paper proposes an efficient optimization algorithm named "Freezing FireFly (FFF)" to solve (ℓ, d) motif search problem. The new strategy freezing such as local and global was added to increase the performance of the basic Firefly algorithm. It freezes the best possible out coming positions even in the lesser brighter one. The performance of the proposed algorithm is experienced on simulated and real datasets. The experimental results show that the proposed algorithm resolves the instance (50, 21) within 1.47 min in the simulated dataset. For real (such as ChIP-seq (Chromatin Immunoprecipitation)) and synthetic datasets, the proposed algorithm runs much faster in comparison to existing state-of-the-art optimization algorithms, including Samselect, TraverStringRef, PMS8, qPMS9, AlignACE, FMGA, and GSGA.