What question did this study set out to answer?

To develop a hybrid heuristic ant colony optimization algorithm (HHACO) that improves path quality, convergence speed, and search guidance for mobile robot path planning in known static grid environments.

August 20, 2026International Journal of Intelligent Computing and Cybernetics

Hybrid heuristic ant colony optimization algorithm for mobile robot path planning

View Full Paper

Ask AI

Bookmark

Authors

SWShuai WuCity College of Dongguan University of Technology JXJinzhou XuArtificial Intelligence in Medicine (Canada)ZYZijing YeChongqing University

Discussion

Member takes

Implication

Simulation study demonstrates improved path length, convergence speed, and turning points in static grid environments, highlighting enhanced efficiency for mobile robot navigation.

Key Points

To develop a hybrid heuristic ant colony optimization algorithm (HHACO) that improves path quality, convergence speed, and search guidance for mobile robot path planning in known static grid environments.
Integrated weighted Euclidean distance, Manhattan distance, and historical global-best path proximity into multi-source heuristic information.
Implemented a dynamic balance factor for node selection, dual-path bounded pheromone updating (iteration-best and global-best), and post-processing local line-of-sight optimization for redundant node removal.
Evaluated algorithm performance using parameter tuning, ablation studies, and comparative simulations across multiple static grid maps against existing multi-strategy ant colony optimization methods.
HHACO generated shorter feasible paths with fewer turning points compared to benchmark algorithms across tested static grid scenarios.
The algorithm achieved faster convergence iterations and superior result stability relative to conventional and multi-strategy ant colony optimization variants.

Cite This Study

Wu et al. (2026) studied this question.

synapsesocial.com/papers/6a86b56c8a91293e6a1ccb8e https://doi.org/10.1108/ijicc-04-2026-0362

View Full Paper

Ask AI

Bookmark

Implication

Simulation study demonstrates improved path length, convergence speed, and turning points in static grid environments, highlighting enhanced efficiency for mobile robot navigation.

Hybrid heuristic ant colony optimization algorithm for mobile robot path planning

Discussion

Member takes

Implication

Key Points

Cite This Study

Hybrid heuristic ant colony optimization algorithm for mobile robot path planning

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider