Intelligent Optimization Algorithm Based on Big Data in Behavioral Finance of Securities Investment

Abstract

Optimal calculation of securities investment is a hot and difficult problem. At present, the research mainly focuses on genetic algorithm, quadratic programming, ant colony algorithm and so on, and the results are relatively many. But these algorithms still have some shortcomings. Intelligent algorithm is a general term for a class of algorithms. It means that human beings are inspired by the collective behaviors of various organisms in nature and find better optimal strategies by imitating these intelligent behaviors. With the progress of science and technology, some of the traditional optimization method has been unable to effectively solve complex problems in reality, especially the NP hard problem. The scholars began to adopt more population of swarm intelligence optimization algorithm is used to, and showed the significant advantage. In the field of engineering application and scientific research, Swarm intelligence algorithm is paid more and more attention by scholars. This paper mainly studies the analysis of intelligent optimization algorithm based on big data in behavioral finance of securities investment. In this paper, after the accumulation and degradation of the collected original data, the optimization calculation is carried out on the condition that the portfolio income remains unchanged. The results of this paper show that compared with genetic algorithm and differential evolution algorithm, the latter result is more suitable for the investment model discussed in this paper.