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10 - Swarming Theory Applied to Space Trajectory Optimization

Published online by Cambridge University Press:  06 December 2010

By
Mauro Pontani  and
Bruce A. Conway
Edited by
Bruce A. Conway
Mauro Pontani
Affiliation:
Scuola di Ingegneria Aerospaziale, University of Rome “La Sapienza,” Rome, Italy
Bruce A. Conway
Affiliation:
Department of Aerospace Engineering, University of Illinois at Urbana-Champaign, Urbana, IL
Bruce A. Conway
Affiliation:
University of Illinois, Urbana-Champaign
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Summary

Introduction

The determination of optimal (either minimum-time or minimum-propellant-consumption) space trajectories has been pursued for decades with different numerical optimization methods. In general, numerical optimization methods can be classified as deterministic or stochastic methods. Deterministic gradient-based methods assume the continuity and differentiability of the objective function to be minimized. In addition, gradient-based methods are local in nature and require the identification of a suitable first-attempt “solution” in the region of convergence, which is unknown a priori and strongly problem dependent. These circumstances have motivated the development of effective stochastic methods in the last decades. These algorithms are also referred to as evolutionary algorithms and are inspired by natural phenomena. Evolutionary computation techniques exploit a population of individuals, representing possible solutions to the problem of interest. The optimal solution is sought through cooperation and competition among individuals. The most popular class of these techniques is represented by the genetic algorithms (GA), which model the evolution of a species based on Darwin's principle of survival of the fittest. Differential evolution algorithms represent alternative stochastic approaches with some analogy with genetic algorithms, in the sense that new individuals are generated from old individuals and are eventually preserved after comparing them with their parents. Ant colony optimization is another method, inspired by the behavior of ants, whereas the simulated annealing algorithm mimics the equilibrium of large numbers of atoms during an annealing process.

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Spacecraft Trajectory Optimization , pp. 263 - 294
Publisher: Cambridge University Press
Print publication year: 2010

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