Acta Scientific Computer Sciences

Research Article Volume 4 Issue 6

An Artificial Intelligence Based Tour Planner

Hassan Mohamed Hassan Al Ali, Mohamed Saeed Alnaqbi, Abdullah Mohamed Alkharji, Abdullah Saeed Alyamahi, Yousef Saeed AlZaabi and Yasir Hamid*

Abu Dhabi Polytechnic, United Arab Emirates

*Corresponding Author: Yasir Hamid, Abu Dhabi Polytechnic, United Arab Emirates.

Received: December 15, 2021; Published: May 30, 2022


The issue of hard planning for your trip or your vacation is paramount for most tourists, especially when they have some preferences that they want to do without tourism offices. Therefore, we intended to aid this issue by bringing in our smart trip advisor. It is a tourist industry application in the United Arab Emirates. The system operates based on the tourist's or user's preferences, such as religious tourism, luxury, shopping, adventures, and so on. The user will be able to create an account that will have his preferences and some of his information. With this, the user can log in to the application and start planning for the trip. The user will choose the city of arrival. After that, the application will suggest to him the main places that he may choose, depending on his preferences. The application will create a trip plan that has the shortest path and time for the user and also ask the user to choose the restaurants, cafes, hotels, and gift shops near each place that he or she planned to visit with the highest ratings. When the user finishes the trip, he will be able to share the trip with his friends, so they can have the same fun that he had, with the ability to edit it to be suitable for them. From this application, we hope that tourists have a wonderful experience in the United Arab Emirates with their preferences and the things that they most like.

Keywords: Ant Colony Algorithm; Algorithm; Artificial Intelligence; Heuristics; Tour Planner


  2. Z Wu., et al. “Optimization for multi-resource allocation and leveling based on a self-adaptive ant colony algorithm”. in 2008 International Conference on Computational Intelligence and Security 1 (2008): 47-51.
  3. Ramlakhan Singh Jadon., et al. “Modified Ant Colony Optimization Algorithm with Uniform Mutation using Self-Adaptive Approach” (2013).
  4. Zbigniew Swiatnicki. “Application of ant colony optimization algorithms for transportation problems using the example of the traveling salesman problem” (2015).
  5. R Montemanni., et al. “Sequential ordering problems for crane scheduling in port terminals”. International Journal of Simulation and Process Modelling 4 (2009): 348-361.
  6. R Montemanni., et al. “A heuristic manipulation technique for the sequential ordering problem”. Computers and Operations Research12 (2008): 3931-3944.
  7. Mohammed Faisal., et al. “AntStar: enhancing optimization problems by integrating an Ant System and A* algorithm”. Scientific Programming (2016).
  8. HC Huang. “The application of ant colony optimization algorithm in tour route planning”. Journal of Theoretical and Applied Information Technology 3 (2013): 343-347.
  9. Y Zhou., et al. “Tour Route Planning Based on Ant Colony System”. in 2009 WRI Global Congress on Intelligent Systems 1 (2009): 218-221.
  10. Rongwei Gan., et al. “Improved Ant Colony Optimization Algorithm for the Traveling Salesman Problem”. Journal of Systems Engineering2 (2010).
  11. X Deng., et al. “An Improved Ant Colony Optimization Applied in Robot Path Planning Problem”. Journal of Computational 3 (2013): 585-593.
  12. S K Tyagi., et al. “Non-discrete ant colony optimisation (NdACO) to optimise the development cycle time and cost in overlapped product development”. International Journal of Production Research 2 (2013): 346-361.
  13. “Retrieving data. Wait a few seconds and try to cut or copy again”.
  14. D Wang., et al. “Assembly sequence planning for panels of reflector antenna based on hybrid algorithm”. Computer Integrated Manufacturing Systems 6 (2017): 1243-1252.
  15. A H Hasan. “Robot path planning based on improved max-min ant colony optimization algorithm in dynamic environment”. Research Journal of Applied Sciences 10 (2016): 1060-1068.
  16. Z Jiang., et al. “Comparing an ant colony algorithm with a genetic algorithm for replugging tour planning of seedling transplanter”. Computers and Electronics in Agriculture 113 (2015): 225-233.
  17. Z Xiao., et al. “A path planning algorithm for PCB surface quality automatic inspection”. Journal of Intelligent Manufacturing (2021): 1-13.
  18. Z Qu. “Construction of Tourism Planning Information System Based on Ant Colony Algorithm”. in Journal of Physics: Conference Series 1533 (2020): 022101.
  19. YB Cheng., et al. “Multi-population ant colony system for multiple path planning of food delivery applications”. in 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (2018): 68-73.
  20. W Shi., et al. “Hybrid PACO with enhanced pheromone initialization for solving the vehicle routing problem with time windows”. in 2015 IEEE Symposium Series on Computational Intelligence (2015): 1735-1742.
  21. “Ant colony optimization algorithms – Wikipedia” (2021).
  22. Y Wang., et al. “An improved ant colony optimization algorithm to the periodic vehicle routing problem with time window and service choice”. Swarm and Evolutionary Computation 55 (2020): 100675.
  23. Y Mei. “Study on the application and improvement of ant colony algorithm in terminal tour route planning under Android platform”. Journal of Intelligent and Fuzzy Systems3 (2018): 2761-2768.
  24. A Sarkar and D Sormaz. “Application of Ant Colony System Meta-heuristic Algorithm in Manufacturing Process Sequencing Problem”. in IIE Annual Conference. Proceedings (2018): 1474-1479.
  25. Y Wang., et al. “Design and implementation of global path planning system for unmanned surface vehicle among multiple task points”. International Journal of Vehicle Autonomous Systems (IJVAS) 1 (2018): 82-105.
  26. D Meier., et al. “A novel backup path planning approach with ACO”. In 2017 5th International Symposium on Computational and Business Intelligence (ISCBI) (2017): 50-56.


Citation: Yasir Hamid., et al. “An Artificial Intelligence Based Tour Planner". Acta Scientific Computer Sciences 4.6 (2022): 04-22.


Copyright: © 2022 Yasir Hamid., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Acceptance rate35%
Acceptance to publication20-30 days

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