Application of Multi-objective Optimization Algorithm based on Artificial Fish School Algorithm in Financial Investment Portfolio Problems

Abstract

In order to comprehensively measure these two indicators and make reasonable portfolio investment decisions, the author proposes using swarm intelligence optimization algorithm - artificial fish swarm algorithm to solve multi-objective investment portfolio problems, and has achieved good results. In order to verify the effectiveness and superiority of the artificial fish school algorithm, the author used MATLAB programming to conduct simulation experiments using AFSA algorithm and genetic algorithm (GA), and compared the results obtained. The results show that compared to the GA algorithm, the artificial fish school algorithm can obtain better investment portfolio decision-making solutions for investing in five types of assets, making investment returns as large as possible while minimizing risks, indicating the efficiency and superiority of the algorithm in solving multi-objective investment portfolio problems.