Dr. Ossama Abdelkhalik
Associate Professor, Mechanical Engineering-Engineering Mechanics
One of the challenges for the future cyber-physical systems is
the exploration of large design spaces. Genetic algorithms
(GAs), which embody a simplified computational model of the
mutation and selection mechanisms of natural evolution, are
known to be effective for design optimization. However, the
traditional formulations are limited to choosing values for
a predetermined set of parameters within a given fixed
Dr. Abdelkhalik's research explores techniques, based on the idea of hidden genes, which enable GAs to select a variable number of components, thereby expanding the explored design space to include selection of a system’s architecture. Hidden genetic optimization algorithms have a broad range of potential applications in cyber-physical systems, including automated construction systems, transportation systems, micro-grid systems, and space systems.
In space systems, optimizing space missions’ trajectories is of significant importance due to its impact on the space mission cost and feasibility. To efficiently design a space mission trajectory, the concept of hidden genes is developed to compute, in an optimal sense, the number of planets’ fly-bys and the plan for propulsion system thrust usage so as to optimize the overall space mission cost.
For more information, please visit Dr. Abdelkhalik's website.
Hidden Genes Genetic Algorithm for Multi-Gravity-Assist Trajectories Optimization
A. Gad, O. Abdelkhalik
Journal of Spacecraft and Rockets, vol. 48, p. 629 (2011)
Dynamic-Size Multiple Populations Genetic Algorithm for Multigravity-Assist Trajectories Optimization
O. Abdelkhalik, A. Gad
Journal of Guidance, Control and Dynamics, vol. 35, p. 520 (2012)
Shape-Based Approximation of Constrained Low-Thrust Space Trajectories Using Fourier Series
E. Taheri, O. Abdelkhalik
Journal of Spacecraft and Rockets, vol. 49, p. 535 (2012)
Hidden Genes Genetic Optimization for Variable-Size Design Space Problems
Journal of Optimization Theory and Applications, vol. 156, p. 450 (2013)