Smell Detection Agent Optimization Algorithm: Difference between revisions
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== SDA Optimization Algorithm (2014) == | |||
Smell Detection Agent (SDA)<ref>{{Cite journal|last1=Vinod|first1=Chandra S S|date=2014|title=Smell Detection Agent Based Optimization Algorithm|url=https://link.springer.com/article/10.1007/s40031-014-0182-0|journal=Journal of The Institution of Engineers (India): Series B|language=en|volume=97|issue=3|pages=431–436|doi=10.1007/s40031-014-0182-0|doi-access=free}}</ref> is an algorithm proposed by Vinod that employs canine (dogs) trained behavior | Smell Detection Agent (SDA)<ref>{{Cite journal|last1=Vinod|first1=Chandra S S|date=2014|title=Smell Detection Agent Based Optimization Algorithm|url=https://link.springer.com/article/10.1007/s40031-014-0182-0|journal=Journal of The Institution of Engineers (India): Series B|language=en|volume=97|issue=3|pages=431–436|doi=10.1007/s40031-014-0182-0|doi-access=free}}</ref> is an algorithm proposed by Vinod that employs canine (dogs) trained behavior. The algorithm creates a cartion space over a search space using smell trails and iterates the agents in resolving a path. The method can be applied to problems that incorporate path-based problems which are NP-hard. | ||
==Applications== | ==Applications== | ||
* | * The algorithm is an intelligent multi agent algorithm that can be used in any optimal path identification. This path should be a shortest path among all paths. | ||
* Multi-objective optimization: Multiple SDAs with different smell capacity (modify the data structure) can be used to solve multi-objective optimization problems. Here the data structure plays an important role for recording the objectives. A multiple source-destination group can be handled by allowing more SDAs to solve the problem. | * Multi-objective optimization: Multiple SDAs with different smell capacity (modify the data structure) can be used to solve multi-objective optimization problems. Here the data structure plays an important role for recording the objectives. A multiple source-destination group can be handled by allowing more SDAs to solve the problem. | ||
* Computer networking: The shortest path identification in a WAN link is possible by SDA algorithm. Using SDA algorithm an optimised path is obtained in a multi hop environment. | * Computer networking: The shortest path identification in a WAN link is possible by SDA algorithm. Using SDA algorithm an optimised path is obtained in a multi hop environment. | ||
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==References== | ==References== | ||
{{reflist}} | {{reflist}} | ||
{{Uncategorized|date=April 2021}} | {{Uncategorized|date=April 2021}} | ||
{{Source Wikipedia}} | {{Source Wikipedia}} | ||
Revision as of 03:35, 5 April 2021
SDA Optimization Algorithm (2014)
Smell Detection Agent (SDA)[1] is an algorithm proposed by Vinod that employs canine (dogs) trained behavior. The algorithm creates a cartion space over a search space using smell trails and iterates the agents in resolving a path. The method can be applied to problems that incorporate path-based problems which are NP-hard.
Applications
- The algorithm is an intelligent multi agent algorithm that can be used in any optimal path identification. This path should be a shortest path among all paths.
- Multi-objective optimization: Multiple SDAs with different smell capacity (modify the data structure) can be used to solve multi-objective optimization problems. Here the data structure plays an important role for recording the objectives. A multiple source-destination group can be handled by allowing more SDAs to solve the problem.
- Computer networking: The shortest path identification in a WAN link is possible by SDA algorithm. Using SDA algorithm an optimised path is obtained in a multi hop environment.
- SDN controllers: An SDA based algorithm can be implemented in controller for Software Defined Networking(SDN) with a method to identify multiple disjoint paths between source and destination nodes. SDN separates the control intelligence from the data packet transmission and uses the controller to dynamically recompute the network state based on availability.
- Subgraph networks: Subgraph identification is an important application in many sciences. SDA algorithm is used for sub graph identification from a large complex network. This is especially in social media applications, drug discovery, protein-protein interaction and communication networks.
Bibliography
- Chandra S S, Vinod; Anand Hareendran (2020). Machine Learning: A Practitioners Approach. Prentice Hall. ISBN 978-93-893-4746-3. Archived from the original on 2020-10-16. Unknown parameter
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- Chandra S S, Vinod; Anand Hareendran (2020). Artificial Intelligence: Principles and Applications. Prentice Hall. ISBN 978-93-893-4783-8. Archived from the original on 2020-10-10. Unknown parameter
|url-status=ignored (help) Search this book on
References
- ↑ Vinod, Chandra S S (2014). "Smell Detection Agent Based Optimization Algorithm". Journal of The Institution of Engineers (India): Series B. 97 (3): 431–436. doi:10.1007/s40031-014-0182-0.
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