Data-driven RLV multi-objective reentry trajectory optimization based on new QABC algorithm

In recent years, big data and cloud technology have been widely used in information extraction and optimization decision. An improved artificial bees colony (ABC) algorithm called QABC is proposed for optimum design of the reusable launch vehicle (RLV) reentry trajectory. Because of poor convergence property of classical ABC algorithm in solving constrained nonlinear optimization problems (CNOPs), several modifications are carried out in this paper. The modifications include a quantum delta potential well model, two dynamic tolerance mechanisms, and a general generation mechanism of selection probability which is associated with the fitness of food source. In this paper, taking RLV three-dimension reentry trajectory design as an application example, a single-objective/multi-constraints optimization model was established with physical programming (PP) method and static penalty function method, in which four objectives (maximum range, minimum heat load, minimum heat flux (MHF), minimum oscillation) and five constraints (dynamic pressure, overload, heat flow, terminal altitude, and terminal velocity) were taken into account. Four single objective trajectory designs and two typical multi-objective trajectory designs with different preference structures were resolved, and the results showed that the optimization model founded by PP method was effective and flexible to reflect the designers preference. The improved algorithm, QABC, show excellent performance in solving RLV reentry trajectory optimization problem and a good prospect in other engineering applications.