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Image-based extension to ant colony algorithm for path finding in gird-based environments

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Abstract

We propose a technique (IPEAC) that extends the Ant Colony Optimization (ACO) for shortest path finding. In a grid-based environment, when the ACO converges, the optimal path needs to be identified among other emerging paths. Our proposed approach utilizes an image processing algorithm named Connected Component Analysis (CCA). In our implementation, the result of the ACO is an image that models the system elements of source, destination, obstacles, background and agents. This image is fed into CCA which applies a sequence of operators to find the optimal path and calculates its coordinates so that it can be traversed. IPEAC was tested against Dijkstra and A* algorithms. Our experimental work showed that IPEAC is effective and produced an accuracy of 97.8% compared to the A* and 91.8% compared to Dijkstra, however the A* was superior in terms of time efficiency and IPEAC was 60% more efficient that Dijkstra.

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All data used in this study is available through the corresponding author of this work.

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  1. College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait

    Aaron Rasheed Rababaah

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  1. Aaron Rasheed Rababaah
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Correspondence to Aaron Rasheed Rababaah.

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Rababaah, A.R. Image-based extension to ant colony algorithm for path finding in gird-based environments. Int J Syst Assur Eng Manag 15, 2853–2867 (2024). https://doi.org/10.1007/s13198-024-02281-3

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