In-depth Analysis of Chinese Traditional Music Overseas Communication Path Based on Multi-objective Optimization Algorithm

Song Gao1, Dong Liu2
1 School of Contemporary Music, Shandong University of Arts, Jinan, Shandong, 250000, China
2Postdoctoral Workstation of Hisense Group, Qingdao, Shandong, 266000, China

Abstract

This paper combines the multifactorial influence of the actual situation, adds the objectives of user interest preference and traditional music overseas communication budget into the influence maximization model, and constructs the Multi-Objective Influence Maximization Model (MOIM) of Chinese traditional music overseas communication to deal with the problem of objective inconsistency in the process of music communication. After that, the seed node selection algorithm of MOEA/D based on decomposition strategy is proposed to improve the search optimization strategy of seeds in the MOIM model. The cross-variance operator designed in the algorithm optimizes the set of solutions generated by the chromosome in the iterative process and finally obtains the Pareto non-dominated solution. The results show that the distribution of Pareto optimal solutions for each graph in the three datasets of TFM, TCC and TCO is very uniform when T=300, and the distribution of Pareto optimal solutions is more uniform with the increase of the number of iterations. The more influential nodes in the multi-objective optimization model of this paper, the higher the cost. The influence and cost of the seed set need to be considered in the overseas dissemination of music, and the seed set should be selected to maximize the influence within the budget. When the network structure and user behavior conform to different characteristics, the MOEA/D model can also get the corresponding undominated solution.The MOEA/D model integrally optimizes the influence index and cost index, so it provides a more flexible set of decision-making solutions for the overseas dissemination of Chinese traditional music.

Keywords: user interest preference, influence maximization, MOEA/D, node selection algorithm, crossvariance operator