  In this work, the hybrid intelligent computing method, which combines
efficient Jaya algorithm with classical Runge-Kutta method is applied to solve
the Falkner-Skan equations with various wedge angles, which is the fundamental
equation for a variety of computational fluid mechanical problems. With some
coordinate transformation, the Falkner-Skan boundary layer problem is then
converted into a free boundary problem defined on a finite interval. Then using
higher order reduction strategies, the whole problem can be boiled down to a
solving of coupled differential equations with prescribed initial and boundary
conditions. The hybrid Jaya Runge-Kutta method is found to yield stable and
accurate results and able to extract those unknown parameters. The sensitivity
of classical shooting method to the guess of initial values can be easily
overcome by an integrated robust optimization method. In addition, the Jaya
algorithm, without the need for tuning the algorithm-specific parameters, is
proved to be effective and stable for minimizing the fitness function in
application. By comparing the solutions using the Jaya method with PSO
(particle swarm optimization), Genetic algorithm (GA), Hyperband, and the
classical analytical methods, the hybrid Jaya Runge-Kutta method yields more
stable and accurate results, which shows great potential for solving more
complicated multi-field and multiphase flow problems.
