A multi-objective imperialist competitive algorithm (MOICA) for finding motifs in DNA sequences

Motif discovery problem (MDP) is one of the well-known problems in biology which tries to find the transcription factor binding site (TFBS) in DNA sequences. In one aspect, there is not enough biological knowledge on motif sites and on the other side, the problem is NP-hard. Thus, there is not an efficient procedure capable of finding motifs in every dataset. Some algorithms use exhaustive search, which is very time-consuming for large-scale datasets. On the other side, metaheuristic procedures seem to be a good selection for finding a motif quickly that at least has some acceptable biological properties. Most of the previous methods model the problem as a single objective optimization problem; however, considering multi-objectives for modeling the problem leads to improvements in the quality of obtained motifs. Some multi-objective optimization models for MDP have tried to maximize three objectives simultaneously: Motif length, support, and similarity. In this study, the multi-objective Imperialist Competition Algorithm (ICA) is adopted for this problem as an approximation algorithm. ICA is able to simulate more exploration along the solution space, so avoids trapping into local optima. So, it promises to obtain good solutions in a reasonable time. Experimental results show that our method produces good solutions compared to well-known algorithms in the literature, according to computational and biological indicators.