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On Comparative Algorithmic Pathfinding in Complex Networks for Resource-Constrained Software Agents

Software engineering projects that utilize inappropriate pathfinding algorithms carry a
significant risk of poor runtime performance for customers. Using social network theory,
this experimental study examined the impact of algorithms, frameworks, and map
complexity on elapsed time and computer memory consumption. The 1,800 2D map
samples utilized were computer random generated and data were collected and processed
using Python language scripts. Memory consumption and elapsed time results for each of
the 12 experimental treatment groups were compared using factorial MANOVA to
determine the impact of the 3 independent variables on elapsed time and computer
memory consumption. The MANOVA indicated a significant factor interaction between
algorithms, frameworks, and map complexity upon elapsed time and memory
consumption, F(4, 3576) = 94.09, p < .001, h2 = .095. The main effects of algorithms,
F(4, 3576) = 885.68, p < .001, h2 = .498; and frameworks, F(2, 1787) = 720,360.01, p
.001, h2 = .999; and map complexity, F(2, 1787) = 112,736.40, p < .001, h2 = .992, were
also all significant. This study may contribute to positive social change by providing
software engineers writing software for complex networks, such as analyzing terrorist
social networks, with empirical pathfinding algorithm results. This is crucial to enabling
selection of appropriately fast, memory-efficient algorithms that help analysts identify
and apprehend criminal and terrorist suspects in complex networks before the next attack.

Identiferoai:union.ndltd.org:waldenu.edu/oai:scholarworks.waldenu.edu:dissertations-5054
Date01 January 2017
CreatorsMoran, Michael
PublisherScholarWorks
Source SetsWalden University
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceWalden Dissertations and Doctoral Studies

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