Structural optimization by real-coded probabilistic model-building GA

In this paper, a probabilistic model-building genetic algorithm (PMBGA) is applied to structural optimization problems. PMBGA has high searching ability but it sometimes converges to the local minimum. To avoid this problem, the concept of distributed GA is applied to PMBGA. To deal with constraints, the penalty function and pulling back methods are also applied to PMBGA. Using the proposed methods, a truss structure is designed to minimize its volume as a numerical example. Through the numerical example, the comparison between PMBGA and conventional DGA shows the effectiveness of PMBGA. The penalty function and pulling back methods are also effective in the example.