Understanding the Effects of Ant Algorithms on Path Planning with Gain-Ant Colony Optimization

Viswanathan, S; Ragunanthan, K; Ravichandran, KS; Gandomi, A

Understanding the Effects of Ant Algorithms on Path Planning with Gain-Ant Colony Optimization

Viswanathan, S Ragunanthan, K Ravichandran, KS Gandomi, A

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Conference Proceeding
Citation:: ACM International Conference Proceeding Series, 2022, pp. 50-54
Issue Date:: 2022-04-09

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Field	Value	Language
dc.contributor.author	Viswanathan, S
dc.contributor.author	Ragunanthan, K
dc.contributor.author	Ravichandran, KS
dc.contributor.author	Gandomi, A https://orcid.org/0000-0002-2798-0104
dc.date.accessioned	2023-04-20T03:06:30Z
dc.date.available	2023-04-20T03:06:30Z
dc.date.issued	2022-04-09
dc.identifier.citation	ACM International Conference Proceeding Series, 2022, pp. 50-54
dc.identifier.isbn	9781450396288
dc.identifier.uri	http://hdl.handle.net/10453/170012
dc.description.abstract	With the advent of more automated and unmanned systems, there is an increasing need for path planners. Intelligent path planners play an important role in the navigation of automated systems. In this work, the performance of an enhanced gain-ant colony optimization has been tested with the most popularly used ant algorithms-Ant system, Ant colony system and Min-Max ant system in the application of path planning. The pheromone update mechanism of traditional ant metaheuristic is enhanced with a local optimization mechanism and simulated with popular ant algorithms for an efficient choice of update rule. Evaluation is done using performance measures like path length and computation time taken. The results are statistically verified and analyzed. Path planned by proposed algorithm was found to be 3.25% shorter than existing algorithms.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	ACM International Conference Proceeding Series
dc.relation.ispartof	2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence
dc.relation.isbasedon	10.1145/3533050.3533058
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Understanding the Effects of Ant Algorithms on Path Planning with Gain-Ant Colony Optimization
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-20T03:06:29Z
pubs.publication-status	Published

Abstract:

With the advent of more automated and unmanned systems, there is an increasing need for path planners. Intelligent path planners play an important role in the navigation of automated systems. In this work, the performance of an enhanced gain-ant colony optimization has been tested with the most popularly used ant algorithms-Ant system, Ant colony system and Min-Max ant system in the application of path planning. The pheromone update mechanism of traditional ant metaheuristic is enhanced with a local optimization mechanism and simulated with popular ant algorithms for an efficient choice of update rule. Evaluation is done using performance measures like path length and computation time taken. The results are statistically verified and analyzed. Path planned by proposed algorithm was found to be 3.25% shorter than existing algorithms.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/170012