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I have been interested in trajectory optimization for a while now and I have read a few papers on that topic and bought the book "Spacecraft trajectory optimization" from Cambridge University Press and want to start programming with the goal to optimize a trajectory in a simplified solar system, not in real time but as close as possible, without the need of an exact solution but a preview of a good one.

The books and papers propose different approaches like

- Analytical solutions with the primer vector
- Direct transcription + nonlinear programming
- particle swarm algorithms
- evolutionary/genetic algorithms

From what I have understood so far genetic algorithms (GA's) are pretty good for finding global minima of the cost function even with a bad first guess but are pretty slow. I am looking for a solution that is as fast as possible but where finding the global minima is not required - a 'good' solution is fine. So I am still unsure of which approach to take; as mentioned before it should be consider a preview.

Particle swarm algorithms have been suggested to me as well and seem to be faster than GA's but converge slower towards the optimum, which is fine to me but I could not find any papers on the topic of speed comparisons regrading these optimal control problems.

If this is the wrong forum for questions like mine please move this post; I was unsure because this question is related to computer science an engineering as well.

Thanks for your help!

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# Trajectory opimization: Fast preview algorithm

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