This book provides a comprehensive introduction to, and application of, heuristic search methods, specifically the genetic algorithm, for use with mission sets operating in multi-body gravitational systems. Detailed dynamical and mission models, as well as accompanying space-focused scenarios will allow the reader to implement different forms of the genetic algorithm to find Pareto optimal architectures within a multi-objective optimization problem. This book covers the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and delivers the first treatment of a scalable genetic algorithm employing a categorical chromosome form in book format. Although the book focuses on the space situational awareness (SSA) mission with space-based and lunar surface architectures, the heuristic techniques contained herein can be applied to other mission sets, such as positioning, navigation, and timing (PNT), communications, and space logistics.
This book gives the reader a singular reference for space domain architecture analysis and optimal search as the focus of mission development and planning continues to expand into cislunar space, low lunar orbit, and the lunar surface. It is meant for the beginning researcher and expert astronautical engineer alike, with detailed mathematical algorithms and application scenarios allowing for a wide range of skill-sets and acclimation to heuristic search methods to use genetic algorithms in order to solve complex multi-objective optimization problems. The book provides a go-to source for students, teachers, engineers, and mission planners for investigating space system architecture design for space-based and lunar surface applications in the Earth-Moon and Mars-Phobos-Deimos systems, and anywhere else in the Solar System required for a given mission.
Table of Contents:
ChapterMulti Body Dynamics and Periodic Orbits.- Fundamentals of Space Situational Awareness.- Fundamentals of Select Space Missions.- Heuristic Search Methods and Genetic Algorithms.- Genetic Algorithm Variant The Categorical Form.- Space Based Cislunar SSA.- Lunar Surface SSA.- Mars System SSA.- Optimization Framework for Select Mission Types.
About the Author :
Dr. Jacob Dahlke is an astronautical engineer and attained his doctorate at the Air Force Institute of Technology in 2024, where he specialized in multi-body dynamics and the application of genetic algorithms to determine optimal architectures for space-based space situational awareness (SSA) sensors in cislunar space.
Dr. Robert Bettinger is an Associate Professor of Aerospace Engineering at the Air Force Institute of Technology. He serves as the Curriculum Chair of the Astronautical Engineering program and teaches courses in multi-body gravitational dynamics, spacecraft survivability, atmospheric reentry, and space law/policy.
Mr. Clint Spesard is an astronautical engineer and attained his Master of Science at the Air Force Institute of Technology in 2024, where he studied multi-body dynamics and the application of genetic algorithms to determine optimal architectures for lunar surface sensors to conduct cislunar SSA.
Dr. Bruce Cox is an Associate Professor of Data Science at the Air Force Institute of Technology. His research focuses, in part, on developing and utilizing heuristic search techniques, deterministic optimization, and synergistic pipelines of machine learning techniques to transform data into information.
