A Cooperative Parallel Particle Swarm Optimization for High-Dimension Problems on GPUs

Abstract

Particle Swarm Optimization (PSO) is an evolutionary heuristics-based method used for continuous function optimization. Compared to existing stochastic methods, PSO is very robust. Nevertheless, for real-world optimizations, it requires a high computational effort. In general, parallel implementations of PSO provide better performance. However, this depends heavily on the parallelization strategy engineered as well as the number and characteristics of the exploited processors. In this paper, we propose a cooperative strategy, which consists of subdividing an optimization problem into many simpler sub problems. Each of these sub-problems focuses on a distinct subset of the original problem dimensions. The optimization work for all the selected sub-problems is done in parallel. We map the work onto a GPU-based architecture. The performance of the strategy thus implemented is evaluated for four benchmark functions with high-dimension and different complexity and compared to that yielded by other parallelization strategies.