Smell Detection Agent Optimization Algorithm: Difference between revisions
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{{Article for deletion/dated|page=Smell Detection Agent Optimization Algorithm|timestamp=20210403095033|year=2021|month=April|day=3|substed=yes|help=off}} | {{Article for deletion/dated|page=Smell Detection Agent Optimization Algorithm|timestamp=20210403095033|year=2021|month=April|day=3|substed=yes|help=off}} | ||
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== SDA Optimization Algorithm (2014) == | == 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 | 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 [[dog|canine]] trained behaviour. The algorithm creates a Cartesian 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 | * The algorithm is an intelligent multi-agent algorithm that can be used in any optimal path identification. This path should be the shortest path among all paths. | ||
* Multi-objective optimization: Multiple SDAs with different smell | * Multi-objective optimization: Multiple SDAs with different smell capacities (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 the SDA algorithm. Using the 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. | * SDN controllers: An SDA-based algorithm can be implemented in a 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 | * Subgraph networks: Subgraph identification is an important application in many sciences. The SDA algorithm is used for subgraph identification from a large complex network. This is especially in social media applications, drug discovery, protein-protein interaction and communication networks. | ||
==Bibliography== | ==Bibliography== | ||
*{{cite book | *{{cite book | ||
|title | |title = Machine Learning: A Practitioner’s Approach | ||
|last | |last = Chandra S S | ||
|first | |first = Vinod | ||
|author2 | |author2 = Anand Hareendran | ||
|url | |url = https://www.phindia.com/Books/BookDetail/9789389347463/machine-learning-chandra-hareendran | ||
|year | |year = 2020 | ||
|publisher | |publisher = [[Prentice Hall]] | ||
|isbn | |isbn = 978-93-893-4746-3 | ||
|url-status | |url-status = dead | ||
|archive-url = https://www.phindia.com/Books/BookDetail/9789389347463/machine-learning-chandra- | |archive-url = https://web.archive.org/web/20210319010111/https://www.phindia.com/Books/BookDetail/9789389347463/machine-learning-chandra-hareendran | ||
|archive-date = | |archive-date = 2021-03-19 | ||
|access-date = 2021-04-03 | |||
|dead-url = unfit | |||
}} | }} | ||
*{{cite book | *{{cite book | ||
|title | |title = Artificial Intelligence: Principles and Applications | ||
|last | |last = Chandra S S | ||
|first | |first = Vinod | ||
|author2 | |author2 = Anand Hareendran | ||
|url | |url = https://www.phindia.com/Books/BookDetail/9789389347838/artificial-intelligence-chandra-hareendran | ||
|year | |year = 2020 | ||
|publisher | |publisher = [[Prentice Hall]] | ||
|isbn | |isbn = 978-93-893-4783-8 | ||
|url-status | |url-status = dead | ||
|archive-url = https://www.phindia.com/Books/BookDetail/9789389347838/artificial-intelligence-chandra-hareendran | |archive-url = https://web.archive.org/web/20210411045252/https://www.phindia.com/Books/BookDetail/9789389347838/artificial-intelligence-chandra-hareendran | ||
|archive-date = | |archive-date = 2021-04-11 | ||
|access-date = 2021-04-03 | |||
|dead-url = unfit | |||
}} | }} | ||
| Line 54: | Line 57: | ||
{{reflist}} | {{reflist}} | ||
[[Category:Database algorithms]] | |||
{{Source Wikipedia}} | {{Source Wikipedia}} | ||
Latest revision as of 06:59, 30 May 2026
SDA Optimization Algorithm (2014)
Smell Detection Agent (SDA)[1] is an algorithm proposed by Vinod that employs canine trained behaviour. The algorithm creates a Cartesian 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 the shortest path among all paths.
- Multi-objective optimization: Multiple SDAs with different smell capacities (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 the SDA algorithm. Using the SDA algorithm, an optimised path is obtained in a multi-hop environment.
- SDN controllers: An SDA-based algorithm can be implemented in a 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. The SDA algorithm is used for subgraph 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 Practitioner’s Approach. Prentice Hall. ISBN 978-93-893-4746-3. Archived from the original on 2021-03-19. Retrieved 2021-04-03. Unknown parameter
|url-status=ignored (help)CS1 maint: Unfit url (link) Search this book on
- 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 2021-04-11. Retrieved 2021-04-03. Unknown parameter
|url-status=ignored (help)CS1 maint: Unfit url (link) 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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