Abstract: Aiming at the problem that high-dimensional complex optimization problems are prone to dimension disaster, which makes the algorithm easily fall into local optimization, a particle swarm optimization algorithm based on multigroup parallel cooperation is proposed, which can comprehensively consider the characteristics of high-dimensional complex optimization problems and dynamically adjust the evolution strategy. Based on the analysis of the characteristics of particles in the process of solving high-dimensional complex problems, the network model of multigroup parallel cooperation of particle swarm optimization algorithm (PSO) which integrates ring topology, fully connected topology and von Neumann topology is established. The model combines the advantages of three kinds of topology particle swarm optimization algorithm in solving high-dimensional complex optimization problems, designs a multigroup particle broadcast feedback dynamic evolution strategy, and designs an evolutionary algorithm to realize the dynamic adaptation of topology in high-dimensional complex optimization environment, so that the algorithm has strong search ability in solving high-dimensional unimodal function and multi-modal function. The simulation results show that the algorithm has good performance in the optimization accuracy and convergence speed of solving high-dimensional complex optimization problems.

Key words: high dimensional complex optimization, multigroup parallel cooperation, dimension disaster, particle swarm optimization (PSO) algorithm