DNA words based on an enhanced algorithm of multi-objective particle swarm optimization in a continuous search space

In this paper, particle swarm optimization algorithm in a continuous search space is implemented to generate a set of DNA words. A single swarm with 20 particles is used to find the best solution (gbest). Here, each particle has a number of sub-particles which is referred as the number of sequences to be designed. Overall, the particle which has the optimum fitness value is considered as the best solution. Fitness value of a particle is computed from the average value of all the applied objective functions. The solution obtained from this algorithm is found to be better as compared with other approaches. Furthermore, it has a fast convergence towards the optimum fitness value.