Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32834426
&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215
Accelerating supply chains with Ant Colony Optimization across a range of
hardware solutions
#MMPMID32834426
Dzalbs I
; Kalganova T
Comput Ind Eng
2020[Sep]; 147
(?): 106610
PMID32834426
show ga
Ant Colony algorithm has been applied to various optimisation problems, however,
most of the previous work on scaling and parallelism focuses on Travelling
Salesman Problems (TSPs). Although useful for benchmarks and new idea comparison,
the algorithmic dynamics do not always transfer to complex real-life problems,
where additional meta-data is required during solution construction. This paper
explores how the benchmark performance differs from real-world problems in the
context of Ant Colony Optimization (ACO) and demonstrate that in order to
generalise the findings, the algorithms have to be tested on both standard
benchmarks and real-world applications. ACO and its scaling dynamics with two
parallel ACO architectures - Independent Ant Colonies (IAC) and Parallel Ants
(PA). Results showed that PA was able to reach a higher solution quality in fewer
iterations as the number of parallel instances increased. Furthermore, speed
performance was measured across three different hardware solutions - 16 core CPU,
68 core Xeon Phi and up to 4 Geforce GPUs. State of the art, ACO vectorisation
techniques such as SS-Roulette were implemented using C++ and CUDA. Although
excellent for routing simple TSPs, it was concluded that for complex real-world
supply chain routing GPUs are not suitable due to meta-data access footprint
required. Thus, our work demonstrates that the standard benchmarks are not
suitable for generalised conclusions.
free
free
free
