Improved Multi-Strategy Matrix Particle Swarm Optimization for DNA Sequence Design

Abstract

The efficiency of DNA computation is closely related to the design of DNA coding sequences. For the purpose of obtaining superior DNA coding sequences, it is necessary to choose suitable DNA constraints to prevent potential conflicting interactions in different DNA sequences and to ensure the reliability of DNA sequences. An improved matrix particle swarm optimization algorithm, referred to as IMPSO, is proposed in this paper to optimize DNA sequence design. In addition, this paper incorporates centroid opposition-based learning to fully preserve population diversity and develops and adapts a dynamic update on the basis of signal-to-noise ratio distance to search for high-quality solutions in a sufficiently intelligent manner. The results show that the proposal of this paper achieves satisfactory results and can obtain higher computational efficiency.

Advanced Machine Learning Applications in Big Data Analytics

“…Zhang et al [8] proposed an improved matrix particle swarm optimization algorithm (IMPSO) to optimize DNA sequence design. The algorithm incorporated centroid opposition-based learning and a dynamic update based on signal-to-noise ratio distance to search for high-quality solutions.…”