Research of Adaptive Multi-Swarm Dynamic Multi-Objective Particle Swarm Optimization Arithmetic in Power System

In order to realize the coordinated control of multi-objective system in dynamic environment, this paper proposes an adaptive multi-swarm dynamic multi-objective particle swarm optimization arithmetic based on sparse distance and ε domination method. The arithmetic adopts an improved ε domination strategy to update the external archive, adjust the ε value in real time, and ensure the fast distribution of particle swarm in the early stage and accurate search in the later stage. Global optimal solution is selected based on the sparse distance of external archive to ensure the distribution of non-inferior solution. Through the test of distribution and convergence, it is proved that the selection of learning samples based on sparse distance helps to keep the distribution of non-inferior solution and the improved ε control external file maintenance method not only improves the distribution, but also increases the external archive scale with the increase of iterations, so that the non-inferior solution can better cover the Pareto optimal frontier.