Central force optimization on a GPU: a case study in high performance metaheuristics

Abstract

Central Force Optimization (CFO) is a new and deterministic population based 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 typically requires increased computational time. In order to decrease the computational time required for convergence when using CFO, this study presents the first parallel implementation of CFO on a Graphics Processing Unit (GPU) using the NVIDIA Compute Unified Device Architecture (CUDA). Two versions of the CFO algorithm, Parameter-Free CFO (PF-CFO) and Pseudo-Random CFO (PR-CFO), are implemented using CUDA on a NVIDIA Quadro 1000M and examined using four test problems ranging from 10 to 50 dimensions. Discussion is made concerning the implementation of the CFO algorithms in terms of problem decomposition, memory access, scalability, and divergent code. Results demonstrate substantial speedups ranging from roughly 1 to 28 depending on problem size and complexity.