A Swarm Intelligence Based Algorithm for Proteomic Pattern Detection of Ovarian Cancer

2006 ◽  
Author(s):  
Yan Meng
Keyword(s):  
Ovarian Cancer ◽  
Swarm Intelligence ◽  
Pattern Detection ◽  
Swarm Intelligence Based Algorithm

scholarly journals Swarm Intelligence Based Algorithm for Efficient Routing in VANET

2020 ◽  
Vol 9 (5) ◽  
pp. 1124-1136
Keyword(s):  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Vehicular Networks ◽  
Complete Solution ◽  
Research Work ◽  
Mobility Model ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Intelligence Based Algorithm

Many recent researchers are working to optimize solutions in the field of Vehicular Adhoc Network. However, none of them has yet claimed that it will fulfill all the challenges of such a dynamic region. VANET in itself is a complete area of study, research and improvements. Most of the researchers and industry consortiums has given their hypothesis and solution that depends on their predefined scenarios but no complete solution has designed until yet. Through this research work, the authors concluded that bioinspired solutions can be used to integrate along with VANET for a much accurate and optimized solution. The performance of VANET depends on various scenarios and due to the unpredictable behavior of the vehicle movement, no concrete solution can be claimed as of now. We incorporated Swarm Intelligence in VANET through the Ant Colony Optimization algorithm and found that the performance of VANET has enhanced by avoiding the entire congested path as it senses the pheromone trail. We have implemented and tested the results using open source software like Instant Veins, Simulation of Urban MObility (SUMO) and MObility model generator for VEhicular networks (MOVE). SUMO has used for testing the traffic simulation and MOVE is used to design model. Python for the script. The OSM used to take a map of Dehradun city. When we performed the experimental setup and found that the result confirms in reducing the traveling time of the nodes, which makes nodes faster and managed even it helps in saving the hydrocarbon fuels. During our approach, we have devised our own algorithm that has improvised the present Ant Colony Optimization algorithm and has concluded that the average traveling time of the nodes minimized through our approach.

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