Abstract
The performance of a genetic algorithm is compared with that of particle swarm optimization for the constrained, non-linear, simulation-based optimization of a double flash geothermal power plant. Particle swarm optimization converges to better (higher) objective function values. The genetic algorithm is shown to converge more quickly and more tightly, resulting in a loss of solution diversity. Particle swarm optimization obtains solutions within 0.1% and 0.5% of the best known optimum in significantly fewer objective function evaluations than the genetic algorithm.
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