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To expand mission capabilities needed without a proportional increase in cost or risk for exploration of the solar system,the multiple objective trajectory using low-thrust propulsion and gravity-assist technique is considered.However,low-thrust,gravity-assist trajectories pose significant optimization challenges because of their large design space.Here,the planets are selected as primal scientific mission goals,while the asteroids are selected as secondary scientific mission goals,and a global trajectory optimization problem is introduced and formulated.This multi-objective decision making process is transformed into a bi-level programming problem,where the targets like planets with small subsamples but high weight are optimized in up level,and targets like asteroids with large subsamples but low weight are optimized in down level.Then,the selected solutions for bi-level programming are optimized thanks to a cooperative Differential Evolution(DE) algorithm that is developed from the original DE algorithm;in addition,an sequential quadratic programming(SQP) method is used in low-thrust optimization.This solution approach is successfully applied to the simulation case of the multi-objective trajectory design problem.The results obtained are presented and discussed.
To expand mission capabilities needed for without a proportional increase in cost or risk for exploration of the solar system, the multiple objective trajectory using low-thrust propulsion and gravity-assist technique is considered to be .owever, low-thrust, gravity-assist trajectories pose significant optimization challenges because of their large design space.Here, the planets are selected as primal scientific mission goals, while the asteroids are selected as secondary scientific mission goals, and a global trajectory optimization problem is introduced and formulated. This multi-objective decision making process is transformed into a bi-level programming problem, where the targets like planets with small subsamples but high weight are optimized in up level, and targets like asoids with large subsamples but low weight are optimized in down level. Chen, the selected solutions for bi- level programming are optimized thanks to a cooperative Differential Evolution (DE) algorithm that is developed from the o riginal DE algorithm; in addition, an sequential quadratic programming (SQP) method is used in low-thrust optimization. This solution approach is successfully applied to the simulation case of the multi-objective trajectory design problem. The results obtained are presented and discussed.