Parallelized Particle Swarm Optimization for Minimum-Time Satellite Orientation Maneuvers
- Restrictions on Access:
- Open Access.
- Particle swarm optimization has developed as a popular method of solution discovery for manynumerical problems. Yet, these methods are computationally expensive and require numerous runsto confidently discover a quasi-optimal solution. These major bottlenecks - execution time anda low probability of optimal solution convergence - restrict the usage of particle swarm methodsfor time-sensitive calculations. In an exploration of the finite thrust arc problem as a benchmarkcase, this thesis analyzes particle swarm optimization improvements in an effort to broaden thealgorithms utility.The research presented here studies two methods to significantly improve each of the major particleswarm bottlenecks. First, reducing execution time is explored through various means of problemimplementation. Memory-optimized single-threaded and parallelized C++ algorithms are found toperform up to 96% faster then MATLAB. Second, minimizing premature convergence is studiedwith rehydration - a proposed method of resetting a portion of the swarm under population stagna-tion. Rehydration results demonstrate up to a 44% improvement in average solution discovery. Inemploying these two methods in concert, this research suggests the potential feasibility of reliable,real-time particle swarm optimization techniques.
- Dissertation Note:
- B.S. Pennsylvania State University 2020.
- Technical Details:
- The full text of the dissertation is available as an Adobe Acrobat .pdf file ; Adobe Acrobat Reader required to view the file.
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