A Particle Swarm Optimization Algorithm Based on Optimal Result Set for Haplotyping a Single Individual

In this paper, a practical algorithm PS-MEC is presented based on the idea of generating a small set of optimal results to reduce the probability of losing the best result. We design a kind of short particle code for the algorithm by taking advantage of the low heterozygous frequency of single nucleotide polymorphisms. Experimental results indicate PS-MEC can get a set containing no more than four results in general, and which contains at least a pair of haplotypes that has higher reconstruction rate than those generated by previous algorithms solving the model. Moreover, PS-MEC is still efficient even for solving large size problems.