Abstract: In the present work, the performance of the particle
swarm optimization and the genetic algorithm compared as a typical
geometry design problem. The design maximizes the heat transfer
rate from a given fin volume. The analysis presumes that a linear
temperature distribution along the fin. The fin profile generated using
the B-spline curves and controlled by the change of control point
coordinates. An inverse method applied to find the appropriate fin
geometry yield the linear temperature distribution along the fin
corresponds to optimum design. The numbers of the populations, the
count of iterations and time to convergence measure efficiency.
Results show that the particle swarm optimization is most efficient
for geometry optimization.