Double four-bar crank-slider mechanism dynamic balancing by
  meta-heuristic algorithms
  In this paper, a new method for dynamic balancing of double four-bar crank
slider mechanism by meta- heuristic-based optimization algorithms is proposed.
For this purpose, a proper objective function which is necessary for balancing
of this mechanism and corresponding constraints has been obtained by dynamic
modeling of the mechanism. Then PSO, ABC, BGA and HGAPSO algorithms have been
applied for minimizing the defined cost function in optimization step. The
optimization results have been studied completely by extracting the cost
function, fitness, convergence speed and runtime values of applied algorithms.
It has been shown that PSO and ABC are more efficient than BGA and HGAPSO in
terms of convergence speed and result quality. Also, a laboratory scale
experimental doublefour-bar crank-slider mechanism was provided for validating
the proposed balancing method practically.
