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Parallel asynchronous particle swarm optimization
Author(s) -
Koh ByungIl,
George Alan D.,
Haftka Raphael T.,
Fregly Benjamin J.
Publication year - 2006
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/nme.1646
Subject(s) - computer science , asynchronous communication , parallel computing , parallel algorithm , particle swarm optimization , computation , robustness (evolution) , distributed computing , load balancing (electrical power) , symmetric multiprocessor system , supercomputer , computational complexity theory , mathematical optimization , algorithm , mathematics , computer network , biochemistry , chemistry , geometry , gene , grid
The high computational cost of complex engineering optimization problems has motivated the development of parallel optimization algorithms. A recent example is the parallel particle swarm optimization (PSO) algorithm, which is valuable due to its global search capabilities. Unfortunately, because existing parallel implementations are synchronous (PSPSO), they do not make efficient use of computational resources when a load imbalance exists. In this study, we introduce a parallel asynchronous PSO (PAPSO) algorithm to enhance computational efficiency. The performance of the PAPSO algorithm was compared to that of a PSPSO algorithm in homogeneous and heterogeneous computing environments for small‐ to medium‐scale analytical test problems and a medium‐scale biomechanical test problem. For all problems, the robustness and convergence rate of PAPSO were comparable to those of PSPSO. However, the parallel performance of PAPSO was significantly better than that of PSPSO for heterogeneous computing environments or heterogeneous computational tasks. For example, PAPSO was 3.5 times faster than was PSPSO for the biomechanical test problem executed on a heterogeneous cluster with 20 processors. Overall, PAPSO exhibits excellent parallel performance when a large number of processors (more than about 15) is utilized and either (1) heterogeneity exists in the computational task or environment, or (2) the computation‐to‐communication time ratio is relatively small. Copyright © 2006 John Wiley & Sons, Ltd.