TY - JOUR
T1 - Image-based extension to ant colony algorithm for path finding in gird-based environments
AU - Rababaah, Aaron Rasheed
N1 - Publisher Copyright:
© The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024.
PY - 2024/3/2
Y1 - 2024/3/2
N2 - 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.
AB - 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.
KW - Ant colony optimization
KW - Dijkstra and a path-finding algorithms
KW - Graph algorithms
KW - Grid-based path finding
KW - Image processing
KW - Machine vision
KW - Mobile robotics navigation
UR - http://www.scopus.com/inward/record.url?scp=85186463276&partnerID=8YFLogxK
U2 - 10.1007/s13198-024-02281-3
DO - 10.1007/s13198-024-02281-3
M3 - Article
AN - SCOPUS:85186463276
SN - 0975-6809
VL - 15
SP - 2853
EP - 2867
JO - International Journal of System Assurance Engineering and Management
JF - International Journal of System Assurance Engineering and Management
IS - 7
ER -