Parallel Differential Evolution in Unified Parallel C

Distributed environments and emerging highly-parallel platforms provide a suitable hardware infrastructure for parallel Evolutionary Computation. Partitioned Global Address Space model is a well-known parallel computing model used to implement scalable algorithms for many-core systems and clusters. This study investigates the Unified Parallel C programming language as a tool for implementation of scalable evolutionary algorithms for high-dimensional problems. The design concepts and initial implementation are demonstrated on the Differential Evolution algorithm. The mapping of Differential Evolution concepts to Unified Parallel C features is presented and three variants of parallel Differential Evolution for many-core shared memory systems and clusters of computers with distributed memory are implemented and evaluated in the environment of a small real-world cluster.