Abstract
An efficient optimization algorithm is proposed which combines a conventional genetic algorithm as a global optimizer and the method of feasible directions as a local optimizer. During the global iterations of the genetic algorithm, the gradient information is used for an efficient crossover. After the final global iteration has been performed, the local optimization is used to further improve the solution. The proposed algorithm is benchmarked against a mathematical optimization problem to compare its efficiency and accuracy with those of other optimization algorithms. Then the algorithm is applied to various structural design optimization problems.
