A parallel particle swarm optimization framework based on a fork-join thread pool using a work-stealing mechanism

Abstract

Particle Swarm Optimization (PSO) is one of the most popular optimization algorithms that has been adopted in various fields, including design, scheduling, and biochemistry. However, the algorithm is time-consuming when facing high-dimensional or multi-objective optimizing problems. Parallel PSO is thus proposed to improve its computing efficiency, and many studies have been conducted. However, the low-level system design is seldom considered in parallel programming, which may have a nonnegligible impact on computing efficiency, creating a gap between low-level optimization and high-level algorithm design. Therefore, this paper proposes a Parallel Asynchronous PSO (PAPSO) framework based on thread pools utilizing multicore processors and adopts a cross-level approach to bridge the gap. A series of experiments are designed and conducted to examine how the aforementioned method can improve the parallel execution efficiency of PSO compared with the OpenMP framework and nonparallel PSO. Results indicate that PAPSO can significantly improve PSO computing efficiency by reducing the elapsed time, approaching approximately 20% optimization compared with OpenMP. Additionally, it achieves an approximately linear speedup of up to 4.5 threads. In addition, the particle communication experiment shows that the nonblocking communication protocol is effective for maintaining the computing elapsed time in the same level facing different neighborhood sizes. Finally, the work-stealing mechanism achieves an average of 16% improvement for general computing scenarios and maintain up to 16% improvement for imbalanced workload scenarios. Generally, the major contribution of this paper is that we innovate the thread pooling management concept with the fork-join model in parallelizing PSO to make sufficient use of multiple core CPUs for parallel computing through multithread programming.