id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
cuesj-730	Ismail, Reem J.; Shukur, Mohammed H.; Ismael, Samar J.	Route Discovery Development for Multiple Destination Using Artificial Ant Colony: Google MAP Case Study	2022	8	.pdf	application/pdf	4676	194	51	Table 5: The error percentage of Google MAP compared to the result of ACO Algorithm for multiple destination route planning with five cities Routes when the source node is Family Mall Multiple destination route Total Distance/ km Error Related to ACO=26 Google Map path 1 Havalan, Hawleri New, Shariy ANDAZYARAN, Hewa City 31.9 23% Google Map path 2 Shariy ANDAZYARAN, Hawleri New, Hewa City, Havalan 29.6 14% Google Map path 3 Hawleri New, Shariy ANDAZYARAN, Hewa City, Havalan 27.4 5% Table 7: The error percentage of Google MAP compared to the result of ACO Algorithm for Multiple destination route planning with four cities Routes when the source node is Family Mall Multiple destination route Total Distance/ km Error Related to ACO=27.1 Google Map path 5 Italian City 2, Shaways, Zin city 30.2 12% Google Map path 6 Zin city, Shaways, Italian City 2 40.5 50% Google Map path 7 Italian City 2, Zin city, Shaways 42.4 57% Table 6: Compare ACO algorithm for multiple destination of cities in Erbil with Google map Routes when the source node is Family Mall Multiple destination route Total distance/km Google Map path 5 Italian City 2, Shaways, Zin city 30.2 Google Map path 6 Zin city, Shaways, Italian City 2 40.5 Google Map path 7 Italian City 2, Zin city, Shaways 42.4 ACO Algorithm for Multiple destination route planning path 8 Shaways, Zin city, Italian CiVty 2 27.1 Figure 22: ACO Algorithm for multiple destination route planning develops the Google Map application to optimize the route when it is used for multiple destination and when the route is updated with a new destination.	cache/cuesj-730.pdf	txt/cuesj-730.txt