Parallel Implementations of the Cooperative Particle Swarm Optimization on Many-core and Multi-core Architectures

Particle swarm optimization (PSO) is an evolutionary heuristics-based method used for continuous function optimization. PSO is stochastic yet very robust. Nevertheless, real-world optimizations require a high computational effort to converge to a good solution for the problem. In general, parallel PSO implementations provide good performance. However, this depends heavily on the parallelization strategy used 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 focuses on a distinct subset of the problem dimensions. The optimization work for all the selected sub-problems is done in parallel. We map the work onto four different parallel high-performance multiprocessors, which are based on multi- and many-core architectures. The performance of the strategy thus implemented is evaluated for four well known benchmark functions with high-dimension and different complexity. The obtained speedups are compared to that yielded by a serial PSO implementation.