An Improved Multi-Objective Moth Flame Optimization Algorithm Based on r-Domination and Its Application in Parameter Optimization of Multi-Spacecraft Attitude Control

To solve the multi-objective optimization problem (MOP) for parameter optimization of a multiple spacecraft attitude controller, an improved multi-objective moth–flame optimization algorithm based on r-domination (rIMOMFO) is proposed. In rIMOMFO, Cauchy mutation is utilized to produce flames that can improve the global search ability. Based on the difference of fitness variance between the two generations of moths, a hybrid mutation strategy is used to perturb moths, which can improve the population diversity of moths. Meanwhile, an adaptive parameter is applied to the moth position update mechanism, which can further balance the exploitation and exploration. In addition, r-domination is used to lead the search toward the preference of the decision maker. The effectiveness of the rIMOMFO is verified using test problems (CF, UF and WFG). Finally, rIMOMFO is used to optimize the parameters of a distributed cooperative controller for multiple spacecraft attitudes. The experimental topological structure is symmetrical. The results show that the optimized controller can accelerate the convergence speed and improve the control accuracy.