Two Layer Hybrid Scheme of IMO and PSO for Optimization of Local Aligner: COVID-19 as a Case Study

Nowadays, meta-heuristic algorithm (MA) succeeded in optimizing many engineering problems. Ions motion optimization (IMO) algorithm is a MA that inspired its search strategy from ions attraction based on force law. IMO has good exploration capability but poor exploitation of the search space. The performance of IMO was tested for implementing fragmented local aligner technique (FLAT) which is a local aligner method for finding the longest common consecutive subsequence (LCCS) between pair of biological sequences. Due to the huge length of sequences FLAT based on IMO produce poor results due to the poor exploitation which need to be enhanced by adding particle swarm optimization (PSO) algorithm which has efficient exploitation capability. The enhanced version of IMO (IMO-PSO)was merged as two layer (bottom layer for exploration using IMO and the upper layer exploit the best solution founded from the bottom layer). This hybrid scheme increase the diversity of solutions which increase the quality of solutions. FLAT based on IMO-PSO was tested on real biological sequences gathered from NCBI versus IMO and the standard local alignment algorithm. Besides, COVID-19 was analyzed against other viruses to detect the LCCS between it. FLAT based on IMO-PSO produced an enhancement of the performance of IMO for finding LCCS between biological sequences.

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