In this paper, an evolutionary optimization method, Evolian, is proposed
for the general constrained optimization problem, which incorporates the
concept of (1) a multi-phase optimization process and (2) constraint scaling techniques
to resolve problem of ill-conditioning. In each phase of Evolian, the typical
evolutionary programming (EP) is performed using an augmented Lagrangian objective
function with a penalty parameter fixed. If there is no improvement in the
best objective function in one phase, another phase of Evolian is performed after
scaling the constraints and then updating the Lagrange multipliers and penalty
parameter. This procedure is repeated until a satisfactory solution is obtained.
Computer simulation results indicate that Evolian gives outperforming or at least
reasonable results for multivariable heavily constrained function optimization as
compared to other evolutionary computation-based methods.