Heterogeneous Group Risk Decision Behavior Simulation Based on Particle Swarm Optimization Algorithm

Abstract

This paper studies the general SoC design AMBA bus and proposes an automatic generation method of software structure test data based on adaptive data optimization algorithm. By simplifying the basic particle expansion equation and eliminating the particle velocity term, an adaptive scheme based on inertia weight is proposed. In the research process, this article fully considers heterogeneous groups. Investment companies are divided into three types of decision-makers: reciprocal, intelligent, and leveraged, and their investment behavior is modeled. Given that factors such as technical level will affect the future of the project, and swarm simulation software is used to simulate and analyze the impact of the profitability of smart community microgrid construction projects on decision-making and to conduct dynamic research on market modeling risks. This article first outlines the construction of the renewable energy macro- and micro-market risk decision-making behavior model and clarifies the logic process of the market to promote the consumption of renewable energy. Then, it analyzes the causes of market risks based on dynamic models, first examines the relationship between key risks and risk factors, forms risks that affect returns (bilateral random risks and market efficiency risks), and then an analysis framework for the impact of renewable energy. This paper applies it to the analysis of data-based algorithms, thereby promoting the development of data-based algorithms.