Distance-Constrained Two-Pulse Rendezvous Optimization Using Adaptive Genetic Algorithms

To address the convergence difficulties of genetic algorithms using impulse magnitudes as genes in distance-constrained two-impulse rendezvous problems, we designed an algorithm that adopts the relative velocity at the rendezvous point and the total mission duration as genes. This gene combination ensures all individuals more easily satisfy all constraints, effectively reducing the computational cost of the genetic algorithm. Simulation results demonstrate that under distance constraints: The impulse-based algorithm requires 1,000 individuals over 10 generations to achieve rendezvous. Our method obtains near-optimal solutions with only 50 individuals in 50 generations, significantly reducing computational costs.