Combining genetic algorithms with optimality criteria method for topology optimization

This paper proposes a new algorithm for topology optimization by combining the features of genetic algorithms (GAs) and optimality criteria method (OC). An efficient treatment of initial population with optimality criteria method for evolutionary algorithm is presented which is different from traditional GAs application in structural topology optimization. The optimality method initializes a group of initial solutions near the best solution, then evolutionary operators of crossover and mutation are developed for evolutionary search. In so doing, the combining method can fully take advantage of the merits of both optimality criteria method and the genetic algorithm. The effectiveness of this method is demonstrated by some case studies of the widely studied structural minimum weight design problem. Compared with the solutions of other GA methods, several numerical examples show that the proposed optimization method can solve topology optimization problems more efficiently and also can achieve better results with lower computational cost.