Route Discovery Development for Multiple Destination Using Artificial Ant Colony
Google MAP Case Study
Smart cities need a smart applications for the citizen, not just digital devices. Smart applications will provide a decision-making to users by using artificial intelligence. Many real-world services for online shopping and delivery systems were used and attracted customers, especially after the Covid-19 pandemics when people prefer to keep social distance and minimize social places visiting. These services need to discover the shortest path for the delivery driver to visit multiple destinations and serve the customers. The aim of this research is to develop the route discovery for multiple-destination by using ACO Algorithm for Multiple destination route planning. ACO Algorithm for Multiple destination route planning develops the Google MAP application to optimize the route when it is used for multiple destinations and when the route is updated with a new destination. The results show improvement in the multiple destination route discovery when the shortest path and the sequence order of cities are found. In conclusion, the ACO Algorithm for Multiple destination route planning simulation results could be used with the Google Map application and provide an artificial decision for the citizen of Erbil city. Finally, we discuss our vision for future development.
S. Aslam and H. S. Ullah. A comprehensive review of smart cities components, applications, and technologies based on internet of things. ArXiv, vol. 2002, p. 01716, 2020.
A. Kousis and C. Tjortjis. Data mining algorithms for smart cities: A bibliometric analysis. Algorithms, vol. 14, p. 242, 2021.
A. T. Kareem, M. A. Alrawi and T. A. Israa. smart inventory control system based on wireless sensor network. International Journal of Engineering Research and Application, vol. 7, no. 8, pp. 40-47, 2017.
J. Zhang, J. Fan and Z. Luo. Generating multi-destination maps. IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 8, pp. 1964-1976, 2017.
H. Siam and M. B. Younes. Multi-destinations Round Trip Planner Protocol. 2018 5th International Symposium on Innovation in Information and Communication Technology (ISIICT), pp. 1-5, 2018.
A. Behrouz. Data Communications and Networking. 5th ed. McGraw-Hill, New York, pp. 601-602, 2013.
A. F. Alkhazraji, S. J. Ismail and Y. N. Abd. Iraqi national grid restoration strategy based on modified depth first search algorithm. Advances in Natural and Applied Sciences, vol. 10, no. 5. pp. 1-12, 2016.
A. F. Alkhazraji and S. J. Ismail. Restoring the Iraqi transmission network using k-shortest path algorithms. Advances in Natural and Applied Sciences, vol. 11, no. 4, pp. 146-158, 2017.
H. F. Hasan, A. A. Mahdi and M. Nat. A Recommendation of Information System Implementation to Support Decision-making Process of Top Management. In: Proceedings of the International Conference on Bioinformatics and Computational Intelligence, 2017.
C. Yang and K. Y. Szeto. Solving the Traveling Salesman Problem with a Multi-Agent System. 2019 IEEE Congress on Evolutionary Computation (CEC), 2019, pp. 158-165.
Y. Huang, J. C. Ying, P. S. Yu and V. Tseng. Dynamic graph mining for multi-weight multi-destination route planning with deadlines constraints. ACM Transactions on Knowledge Discovery from Data, vol. 15, no. 1, pp. 1- 2, 2021.
S. Kumar, V. Kumar-Solanki, S. K. Choudhary, A. Selamat and R. González-Crespo. Comparative study on ant colony optimization (ACO) and K-means clustering approaches for jobs scheduling and energy optimization model in internet of things (IoT). International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, no. 1, 2020.
H. Zhuang, K. Dong, Y. Qi, N. Wang and L. Dong. Multi-destination path planning method research of mobile robots based on goal of passing through the fewest obstacles. Applied Science, vol. 11, p. 7378, 2021.
M. Asaduzzaman, T. K. Geok, F. Hossain, S. Sayeed, A. Abdaziz, H. Y. Wong, C. P. Tso, S. Ahmed and B. A. Bari. An efficient shortest path algorithm: multi-destinations in an indoor environment. Symmetry, vol. 13, p. 421, 2021.
A. Hakeem, N. Gehani, X. Ding, R. Curtmola and C. Borcea. Multi-Destination Vehicular Route Planning with Parking and Traffic Constraints. In: MobiQuitous 19: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. pp. 298-307, 2019.
E. H. C. Lu, W. Lee and V. Tseng. Mining fastest path from trajectories with multiple destinations in road networks. Knowledge and Information Systems, vol. 29, no. 1. pp. 25-53. 2011.
Available from: https://en.wikipedia.org/wiki/google_maps [Last accessed on 2022 Feb 01].
A. M. Luthfi, N. Karna and R. Mayasari. Google Maps API Implementation on IOT Platform for Tracking an Object Using GPS. In: 2019 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob). 2019.
Available from: https://www.google.com/maps [Last accessed on 2022 Feb 01].
Copyright (c) 2022 Reem J. Ismail, Mohammed H. Shukur, Samar J. Ismael
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-ND 4.0] that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).