Dynamic-Size Multiple Populations Genetic Algorithm for Multigravity-Assist Trajectories Optimization

The problem of the optimal design of a multigravity-assist space trajectory, with a free number of deep space maneuvers, in its general form poses a multimodal objective function in which design space size is variable. This paper presents a genetic-based method developed to handle global, variable-size, design space optimization problems where the number of design variables varies from one solution to another. Subpopulations of fixed-size design spaces are randomly initialized. Standard genetic operations are carried out for a stage of generations. A new population is then created by reproduction from all members in all subpopulations based on their relative fitnesses. The resulting subpopulations have different sizes from their initial sizes in general. The process repeats, leading to an increase in the size of subpopulations of more fit solutions and a decrease in the size of subpopulations of less fit solutions. This method has the capability to determine the number of swing-bys, the planets to swing by, launch and arrival dates, and the number of deep space maneuvers as well as their locations, magnitudes, and directions in an optimal sense. This new method is applied to several interplanetary trajectory design problems. The results presented in this paper show that solutions obtained using this tool match known solutions for complex case studies.