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Evaluation of the Performance of Ant Colony Optimization over Particle Swarm Optimization

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 38 - Number 6
Year of Publication: 2012
Dr. V.Srinivasa Raman

S.Padmanabhan, Dr.M.Chandrasekaran and Dr. V.Srinivasa Raman. Article: Evaluation of the Performance of Ant Colony Optimization over Particle Swarm Optimization. International Journal of Computer Applications 38(6):12-18, January 2012. Full text available. BibTeX

	author = {S.Padmanabhan and Dr.M.Chandrasekaran and Dr. V.Srinivasa Raman},
	title = {Article: Evaluation of the Performance of Ant Colony Optimization over Particle Swarm Optimization},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {38},
	number = {6},
	pages = {12-18},
	month = {January},
	note = {Full text available}


Traditional mathematical algorithms are incapable of solving real time engineering design problems because of its rigid procedure mainly due to discrete or random data and multi-objective functions in a problem. An optimization algorithm is a procedure which is executed iteratively by comparing various solutions till the optimum or a satisfactory solution is found. There are two population based Swarm inspired methods in computational intelligence areas: Ant colony optimization (ACO) and Particle swarm optimization (PSO). This paper made an attempt to evaluate their performance of these two swarm intelligence techniques. A real engineering application of bevel gear design optimization is considered and results are analyzed with respect to the context.


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