An Improved Multi-Objective Particle Swarm Optimization-Based Hybrid Intelligent Algorithm for Index Screening of Underwater Manned/Unmanned Cooperative System of Systems Architecture Evaluation

An improved multi-objective particle swarm optimization algorithm is combined with a machine learning classifier to meet the needs of underwater manned/unmanned cooperative warfare architecture evaluation. Firstly, based on the traditional Cauchy variation method, the particles in the population are disturbed in a dynamic way so that the particles trapped in the local optimal can jump out of the local optimal, and the convergence performance of the particle swarm optimization is improved. Secondly, the accuracy of the index set is analyzed based on the CART decision tree algorithm and the IWRF algorithm. A screening method of key indexes with fewer evaluation indexes and high evaluation accuracy is developed to solve the problem of a large number of evaluation indexes and unclear correlation of the underwater combat system. Through simulation, the extraction results of key indicators were verified, and the reliability coefficient of the final simulation experiment was 0.93, which can be considered as high reliability and effectiveness of the key indicators extracted in this study. By combining multi-objective optimization with machine learning and weighing evaluation efficiency and accuracy, a high-precision and rapid evaluation of a few indicators is achieved, which provides support for establishing an evaluation model of SoS architecture for underwater manned/unmanned cooperative operations. This research result can provide inspiration for the evaluation and evaluation of the system in order to analyze the accuracy of indicators and the evaluation effect and carry out research by simulating the actual system with tools with a high simulation degree.