A New Macroevolutionary Algorithm for Nonlinear Constrained Optimization Problems

A new macroevolutionary algorithm is proposed for complex nonlinear constrained optimization problems. The proposed algorithm combines uniform experimental design, simulated annealing and macroevolutionary algorithm such that it get a good balance between exploration and exploitation, therefore it has a very good performance. Constraints are handled by embodying them in an augmented Lagrangian function, where the penalty parameters and multipliers are adapted as the execution of the algorithm proceeds. The validity of the proposed algorithm is illustrated by solving some benchmark problems