A novel algorithm to optimize complicated low-thrust trajectory

Purpose - This paper aims to investigate, a new optimization algorithm for complex orbit transfer missions with low-thrust propulsion system, which minimizes the drawbacks of traditional optimization methods, such as bad convergence, difficulty of initial guesses and local optimality. Design/methodology/approach - First, the trajectory optimization problem comes down to a nonlinear constraint parameter optimization by using the concept of traditional hybrid method. Then, one utilizes genetic algorithm (GA) to solve this parameter optimization problem after treating the constraints with the simulated annealing (SA) and randompenalty function. Finally, one makes use of localized optimization to improve the precision of the final solutions. Findings - This algorithm not only keeps the advantages of traditional hybrid method such as high precision and smooth solutions, but also inherits the merits of GA which could avoid initial guess work and obtain a globally optimal solution. Research limitations/implications -Further, research is required to reduce the computational complexity when the transfer trajectory is very complex and/or has many adjustable variables. Practical implications - By using this method, the globally optimal solutions of some complex missions, which could not be obtained by traditional method, could be found. Originality/value - This method combines the GA with traditional hybrid method, and utilizes SA and random penalty functions to treat with constraints, and then gives out a super convergence way to find the globally optimal low-thrust transfer orbit.