Analysis of a hybrid genetic simulated annealing strategy applied in multi-objective optimization of orbital maneuvers

Abstract

Optimization of orbital maneuvers is one of the main issues in conceptual and preliminary design of spacecraft in different space missions. The main issue in optimization of highthrust orbit transfers is that the common optimization algorithms such as Genetic Algorithm and Simulated Annealing are not effectual in finding optimal transfer when they are purely used in optimization. In such problems, modified algorithms are required to find the optimal transfer. Such modifications involve consecutive search and dynamic boundary delimitation. This paper presents a direct approach to optimize high-thrust orbit transfers using a hybrid algorithm based on Simulated Annealing and Genetic Algorithm. This multiobjective optimization method considers optimum fuel transfers while minimizing the error of orbital elements at the end of orbital maneuver. Trajectory optimization is conducted based on converting the orbit transfer problem into a parameter optimization one by assigning proper mathematical functions to the variation of thrust vector direction.Optimization problem is solved using intelligent boundary delimitation in a general optimization method. Taking advantage of nonlinear simulation, a technique is proposed to acquire good quantity for optimization variables, which results in enlarged convergence domain. Numerical example of a three dimensional optimal orbit transfer is analyzed and the accuracy of proposed algorithm is presented. Optimality and convergence of the proposed algorithm is discussed by comparing the results obtained by different approaches.Results confirm the efficiency of the proposed hybrid algorithm in comparison to Simulated Annealing and Genetic Algorithm.

Orbital Maneuver Optimization of Earth Observation Satellites Using an Adaptive Differential Evolution Algorithm

“…Since ∆v is associated with the capacity of fuel, which is the key parameter of the satellite remained lifetime, constraint (25) ensures that the velocity increment is limited to a reasonable range. Here ∆v max represents the maximum allowed velocity increment and the negative value indicates the velocity in the reverse direction.…”

“…Previously, evolutionary algorithms have been widely employed to address the orbit design problem. The algorithms used mainly include particle swarm optimization [20,22,23], genetic algorithms [16,21,24], and hybrid algorithms [5,25]. For example, Shirazi [25] applied a hybridization of the genetic algorithm and simulated annealing to a multiobjective orbit maneuver optimization problem.…”

“…The algorithms used mainly include particle swarm optimization [20,22,23], genetic algorithms [16,21,24], and hybrid algorithms [5,25]. For example, Shirazi [25] applied a hybridization of the genetic algorithm and simulated annealing to a multiobjective orbit maneuver optimization problem. Based on the particle swarm optimization (PSO) algorithm, Pontani et al [22] solved four kinds of impulsive orbital transfer problems, focusing on the optimization of impulsive transfers between two coplanar and non-coplanar, circular and elliptic orbits, respectively.…”

A new shape-based multiple-impulse strategy for coplanar orbital maneuvers

“…In addition, the effect of path and thrust constraints on impulsive maneuvers have been investigated [5,6], and the optimal impulsive transfer in presence of time constraints is driven [7]. Further, various heuristic optimization algorithms such as the genetic algorithm (GA) [8], the particle swarm optimization (PSO) [9,10], and the simulated annealing [11] have been proposed to design an optimal impulsive trajectory (also the reader can refer to [12] and references therein for more details). However, the Lamberts approach has traditionally been utilized for conic trajectories between any two spatial points in space within a specified time interval.…”