Central Force Optimization on a GPU: A case study in high performance metaheuristics using multiple topologies

Central Force Optimization (CFO) is a powerful new metaheuristic algorithm that has been demonstrated to be competitive with other metaheuristic algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Group Search Optimization (GSO). While CFO often shows superiority in terms of functional evaluations and solution quality, the algorithm is complex and often requires increased computational time. In order to decrease CFO's computational time, we have implemented the concept of local neighborhoods and implemented CFO on a Graphics Processing Unit (GPU) using the NVIDIA Compute Unified Device Architecture (CUDA) extensions for C/C++. Pseudo Random CFO (PR-CFO) is examined using four test problems ranging from 30 to 100 dimensions. Results are compared and analyzed across four unique implementations of the PR-CFO algorithm: Standard, Ring, CUDA, and CUDA-Ring. Decreases in computational time along with superiority in terms of solution quality are demonstrated.