Application of The Spider Monkey Optimization Algorithm In A Class Of Traffic Delay Problem
|International Journal of Computer Science and Engineering|
|© 2020 by SSRG - IJCSE Journal|
|Volume 7 Issue 2|
|Year of Publication : 2020|
|Authors : Sonny E. Ezekwere, V.I.E Anireh, Matthias Daniel|
How to Cite?
Sonny E. Ezekwere, V.I.E Anireh, Matthias Daniel, "Application of The Spider Monkey Optimization Algorithm In A Class Of Traffic Delay Problem," SSRG International Journal of Computer Science and Engineering , vol. 7, no. 2, pp. 48-56, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I2P106
Nature inspired algorithms have gained some level of popularity amongst researchers in recent times. They possess the ability to search and discover solutions to real-world optimization problems, which may have been difficult to solve using deterministic techniques. Spider Monkey Optimization (SMO) is in a class of such algorithm. It is one of the most recent Swarm Intelligence (SI) based algorithm, that was developed through the study of the food foraging behavior of a group of spider monkeys that mimic the Fission-Fusion Social System (FFSS) behavior. This study applies SMO to traffic delay minimization problem. Experiment includes simulation of 4-legged intersection and the result showed minimization of total travel time. This result was compared to the Artificial Bee Colony (ABC) algorithm. The SMO outperformed the ABC algorithm because of its decentralized, stochastic and self-organizational attribute that makes it suitable for the nature of traffic networks. Computer simulation results show that this method performs better when compared with conventional fully actuated control, especially under the condition of fairly saturated traffic condition.
Spider Monkey Optimization algorithm, Artificial Bee Colony optimization, traffic delay problem, Fission-fusion social structure.
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