P-HS-SFM: a parallel harmony search algorithm for the reproduction of experimental data in the continuous microscopic crowd dynamic models.

Finding the optimal parameters that can reproduce experimental data (such as the velocity-density relation and the specific flow rate) is a very important component of the validation and calibration of microscopic crowd dynamic models. Heavy computational demand during parameter search is a known limitation that exists in a previously developed model known as the Harmony Search-Based Social Force Model (HS-SFM). In this paper, a parallel-based mechanism is proposed to reduce the computational time and memory resource utilisation required to find these parameters. More specifically, two MATLAB-based multicore techniques (<italic>parfor</italic> and <italic>create independent jobs</italic>) using shared memory are developed by taking advantage of the multithreading capabilities of parallel computing, resulting in a new framework called the Parallel Harmony Search-Based Social Force Model (P-HS-SFM). The experimental results show that the <italic>parfor</italic>-based P-HS-SFM achieved a better computational time of about 26 h, an efficiency improvement of <inline-graphic></inline-graphic> 54% and a speedup factor of 2.196 times in comparison with the HS-SFM sequential processor. The performance of the P-HS-SFM using the <italic>create independent jobs</italic> approach is also comparable to <italic>parfor</italic> with a computational time of 26.8 h, an efficiency improvement of about 30% and a speedup of 2.137 times. [ABSTRACT FROM AUTHOR]