Manuscript Number : SHISRRJ247234

A* Star Algorithm Visualization

Authors(2) :-Dr. K. Shanmugam, D. Durgabhavani

By natural phenomena, offer innovative approaches for optimization and problem-solving in various domains. The implementation encompasses features such as color-coded spot states (open, closed, path, etc.) and user-driven interaction through mouse clicks and key presses. The key commitments of this extend incorporate the visualization of the A* algorithm's conduct, which helps clients in understanding how the calculation navigates through impediments and makes choices. Additionally, the project demonstrates the significance of heuristic functions in guiding the algorithm's search efficiently. By presenting various scenarios of pathfinding problems and their corresponding solutions, the project showcases the algorithm's ability to find optimal paths while avoiding obstacles. Algorithms, fundamental to computer science and information processing, are step-by-step procedures or sets of rules for solving problems. They are crucial in various fields including mathematics, computer science, engineering, and more recently, in machine learning and artificial intelligence. The abstraction of algorithms allows for the generalization of problem-solving approaches, making them applicable across diverse domains. Algorithms can be classified based on their design paradigms as greedy methodologies, adaptive programming, divide and conquer, and others. nation paradigm possesses its own negatives plus drawbacks and unveils an individual strategy for mitigating problems. Furthermore, algorithms can be categorized based on their complexity, measured in terms of time and space requirements, which helps in evaluating their efficiency and scalability.

Authors and Affiliations

Dr. K. Shanmugam
Assistant Professor, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra Pradesh, India
D. Durgabhavani
Post Graduate, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra Pradesh, India

Heuristic Search, Pathfinding, Graph Search, Admissible Heuristic, Open List, Closed List, Manhattan Distance, Optimality, Computational Complexity.

  1. Citations: Raphael, B., Nilsson, N. J., and Hart, P. E. (1968). An Official Foundation for the Heuristic Calculation of the Lowest Cost Routes. IEEE Transactions on Cybernetics and Systems Science, 4(2), 100–107.
  2. Russell, S., & Norvig, P. (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall. (Chapter 3 covers A* algorithm extensively.)
  3. Cormen,T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to Algorithms (3rd ed.). MIT Press. (Chapter 4 includes discussions on A* algorithm.)
  4. Nilsson, N. J. (1982). Principles of Artificial Intelligence. Tioga Publishing Company. (Chapter 10 provides insights into A* algorithm.)
  5. Laurikkala, J. (2000). A* Algorithm for Learning in Feedforward Neural Networks. Neurocomputing, 32-33, 413-419. Pearl, J. (1984).
  6. Heuristics: Intelligent Search Techniques for Resolving Computer Issues. Wesley-Adobe. (The application of the A* algorithm is covered in Chapter 4.) R. E. Korf (1985). Depth-First Iterative-Deepening: An Ideal Acceptable Tree Search Method. 27(1), Artificial Intelligence, 97-109.

Publication Details

Published in : Volume 7 | Issue 2 | March-April 2024
Date of Publication : 2024-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 225-231
Manuscript Number : SHISRRJ247234
Publisher : Shauryam Research Institute

ISSN : 2581-6306

Cite This Article :

Dr. K. Shanmugam, D. Durgabhavani, "A* Star Algorithm Visualization ", Shodhshauryam, International Scientific Refereed Research Journal (SHISRRJ), ISSN : 2581-6306, Volume 7, Issue 2, pp.225-231, March-April.2024
URL : https://shisrrj.com/SHISRRJ247234

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