Hidden Genes Genetic Algorithm for Multi-Gravity-Assist Trajectories Optimization

The problem of optimal design of a multi-gravity-assist space trajectory, with a free number of deep space maneuvers, poses a multimodal cost function. In the general form of the problem, the number of design variables is solution dependent. This paper presents a genetic-based method developed to handle global optimization problems where the number of design variables vary from one solution to another. A fixed length for the design variables is assigned for all solutions. Independent variables of each solution are divided into effective and ineffective segments. Ineffective segments (hidden genes) are excluded in cost function evaluations. Full-length solutions undergo standard genetic operations. This new method is applied to several interplanetary trajectory design problems. 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. The results presented in this paper show that solutions obtained using this tool match known solutions for complex case studies.