A multiobjective approach based on the behavior of fireflies to generate reliable DNA sequences for molecular computing

The reliability of artificially generated DNA molecules is a key factor for applications which depend on DNA-based technologies, such as DNA computing or nanotechnology. In those cases, interactions between sequences have to be controlled to avoid undesirable reactions. In the specific case of molecular computing, the design of robust sets of sequences prevent from incorrect computations because DNA sequences are designed in order to avoid potentially conflicting interactions between the DNA molecules within the artificially generated library. However, the design of reliable DNA libraries which can be used for molecular computing involves several heterogeneous and conflicting design criteria that cannot be properly modeled by using traditional optimization algorithms. In this paper, we formulate the problem as a multiobjective optimization problem and we solve it with a novel multiobjective algorithm based on the behaviour of fireflies. Specifically, our approach, multiobjective firefly algorithm (MO-FA), works with six different conflicting design criteria that measure the reliability of the generated sequences. Furthermore, in order to compare our results in multiobjective terms, we have also developed and adjusted the well-known fast non-dominated sorting genetic algorithm (NSGA-II). Results show that our proposal obtains very satisfactory results. In fact, the reliability of DNA sequences generated significantly surpasses the reliability of sequences obtained with other approaches previously published in the literature.