Speeding up Composite Differential Evolution for structural optimization using neural networks

ABSTRACT Composite Differential Evolution (CoDE) is categorized as a (µ + λ)-Evolutionary Algorithm where each parent produces three trials. Thanks to that, the CoDE algorithm has a strong search capacity. However, the production of many offspring increases the computation cost of fitness evaluation. To overcome this problem, neural networks, a powerful machine learning algorithm, are used as surrogate models for rapidly evaluating the fitness of candidates, thereby speeding up the CoDE algorithm. More specifically, in the first phase, the CoDE algorithm is implemented as usual, but the fitnesses of produced candidates are saved to the database. Once a sufficient amount of data has been collected, a neural network is developed to predict the constraint violation degree of candidates. Offspring produced later will be evaluated using the trained neural network and only the best among them is compared with its parent by exact fitness evaluation. In this way, the number of exact fitness evaluations is significantly reduced. The proposed method is applied for three benchmark problems of 10-bar truss, 25-bar truss, and 72-bar truss. The results show that the proposed method reduces the computation cost by approximately 60%.