A parallel differential evolution with cooperative multi-search strategy for sizing truss optimization

Abstract

The increasing complexity of modern structural design problems requires optimization algorithms to have an acceptable completion time regarding the huge number of design variables. This paper proposes a parallel differential evolution with cooperative multi-search strategies (PDECMS) and the implementation with Compute Unified Device Architecture (CUDA) for improving execution time by leveraging the Graphical Processing Unit (GPU). Three sub-populations with dedicated mutation schemes are used to establish island models, which start searching at distinct initial points. As the evolution process begins, the exchange of knowledge between islands is synchronously conducted via the migration of elite individuals. The PDECMS is used to solve five discrete sizing optimization problems of a truss structure to demonstrate the achieved solution quality, convergence speed, and scalability. It has been found that the computing time of PDECMS was at least two times faster than its serial implementation for the large population size and the attained solution quality was generally agreeable with other methods despite the sacrifice for the enhancement of performance. Numerical results reveal that the accomplishment of optimal solutions with fewer iterations and a shorter time comes from the cooperative multi-search strategy and the use of GPU. This outcome, therefore, shows that the PDECMS is capable of optimally solving multi-variable problems with a large search space.