Implementing Central Force Optimization on the Intel Xeon Phi

Central Force Optimization (CFO) is a fully deterministic population based metaheuristic algorithm based on the analogy of classical kinematics. CFO yields more accurate and consistent results compared to other population based metaheuristics like Particle Swarm Optimization and Genetic Algorithms, but does so at the cost of higher computational complexity, leading to increased computational time. This study presents a parallel implementation of CFO written in C++ using OpenMP as implemented for both a multi-core CPU and the Intel Xeon Phi Co-processor. Results show that parallelizing CFO provides promising speedup values from 5-35 on the multi-core CPU and 1-12 on the Intel Xeon Phi.