Comparison of genetic algorithm to particle swarm for constrained simulation-based optimization of a geothermal power plant

  • Authors:
  • Joshua Clarke;Laura Mclay;James T. Mcleskey, Jr.

  • Affiliations:
  • -;-;-

  • Venue:
  • Advanced Engineering Informatics
  • Year:
  • 2014

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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.