Application of Particle Swarm Optimization Algorithm in the Production of Original Songs for College Students on Campus

Abstract

In response to the shortcomings of slow speed and heavy workload in traditional campus composition production algorithms, this paper proposes a particle swarm optimization algorithm for intelligent music composition. This paper introduces particle swarm optimization algorithm into music composition, and achieves numerical encoding of music through the encoding of sound level, beat duration, and note. The weighted evaluation results of the corresponding group, non corresponding group, and professional group are selected as the fitness function, and three indicators, namely intra regional uniformity measure (UM), inter regional contrast (RC), and comprehensive measure, are used to evaluate the effectiveness of intelligent music composition. The results show that this algorithm has the advantages of fast composition speed and high quality, which is helpful for songwriters to create music, greatly reducing workload, and has certain promotional value.