Multi-objective optimization of space station short-term mission planning

Abstract

This paper studies the multi-objective optimization of space station short-term mission planning (STMP), which aims to obtain a mission-execution plan satisfying multiple planning demands. The planning needs to allocate the execution time effectively, schedule the on-board astronauts properly, and arrange the devices reasonably. The STMP concept models for problem definitions and descriptions are presented, and then an STMP multi-objective planning model is developed. To optimize the STMP problem, a Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted and then improved by incorporating an iterative conflict-repair strategy based on domain knowledge. The proposed approach is demonstrated by using a test case with thirty-five missions, eighteen devices and three astronauts. The results show that the established STMP model is effective, and the improved NSGA-II can successfully obtain the multi-objective optimal plans satisfying all constraints considered. Moreover, through contrast tests on solving the STMP problem, the NSGA-II shows a very competitive performance with respect to the Strength Pareto Evolutionary Algorithm II (SPEA-II) and the Multi-objective Particle Swarm Optimization (MOPSO). space station, short-term mission planning, multi-objective optimization, NSGA-II Citation:Bu H J, Zhang J, Luo Y Z, et al. Multi-objective optimization of space station short-term mission planning.

Real‐time dynamic energy efficiency evaluation and analysis of industrial processes based on multi‐objective state transition algorithm with reference vector

“…The multi-objective optimization technique has been widely utilized to address the multi-indicator characteristic problem. Non-dominated sorting genetic algorithm II (NSGA-II), [14] strength Pareto evolutionary algorithm 2 (SPEA2), [15] multiple objective particle swarm optimization (MOPSO), [16] multiple objective state transition algorithm (MOSTA), [17] multiple objective evolutionary algorithm (MOEA), [18] and grid-based evolutionary algorithm (GrEA) [19] are some of the most popular optimization algorithms for multi-objective problems. Among them, MOSTA, proposed by Han, [20] is a relatively new method that can improve the performance of multi-objective optimization by combining the traditional single-objective state transition algorithm (STA) [21][22][23][24][25][26] with the non-dominated quick sort method of the NSGA-II algorithm.…”

Simulation and Evaluation of a Space Station Operational Plan Considering Launch Delay of Cargo Vehicles

“…China will then become one of the few countries that have mastered the technology of long-duration manned space flight. [3][4][5] From an engineering perspective, some scholars have investigated models of space station operational mission planning, 6) decision-making for space station logistics strategies, 7) multi-objective optimization of space station short-term mission planning, 8) and overall mission planning for space station long-term operation. 9) Although these approaches enable valid formulations for planning space station operational missions, the uncertainties of the missions are not considered.…”

Hybrid membrane computing and pigeon-inspired optimization algorithm for brushless direct current motor parameter design

“…Each individual in the swarm searches within the searching space by taking the information of the environment and other peer individuals into account. Optimization algorithms have shown superior performance in solving nonlinear and complex problems which require high computational resources and involve a large number of constraints [12][13][14][15][16].…”