Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Parallel implementation of niched Pareto genetic algorithm code for X-ray plasma spectroscopy
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
On the scalability of parallel genetic algorithms
Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A Solution Framework for Environmental Characterization Problems
International Journal of High Performance Computing Applications
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We use a parallel multi-objective genetic algorithm to drive a search and reconstruction spectroscopic analysis of plasma gradients in inertial confinement fusion (ICF) implosion cores. In previous work, we had shown that our serial multi-objective Genetic Algorithm was a good method to solve two-criteria X-ray spectroscopy diagnostics problems. However, this serial version was slow and we therefore could not incorporate better physics and more criteria to solve larger problems and handle larger data sets. In this paper, we develop and use a parallel multi-objective genetic algorithm based on a master-slave model to solve three criteria spectroscopic analysis problems. The algorithm works well in reconciling experimental observations with theoretical physics model parameters. In addition, theoretical analysis and experimental results on the parallelized version show good scalability with up to 150 processors. This reduces the time for running the GA from 9.6 hours to 5.9 minutes.