Research and Algorithm Test of Adaptive Interbreeding Hybrid Particle Swarm Optimization

Particle swarm optimization (PSO) is a new evolutionary algorithm developed in recent years. It is easy to implement with few parameters, but it is often easy to fall into local optimization in the later period. This paper introduces the interbreeding algorithm in genetic algorithms (GA) to increase population size diversity, and uses the inertia weight adjustment method of the chaos algorithm mechanism to adjust the inertia factor, and proposes an adaptive interbreeding hybrid particle swarm optimization (AIHPSO). In this paper, six representative nonlinear experimental functions are used to simulate and compare the algorithms. The results prove that AIHPSO plays a better role in a complex optimization process. It can improve local development capabilities, enhance convergence speed and the accuracy is significantly improved, while avoiding the problems of premature maturity and local optimization.