.png){kind=icon}
.png){kind=icon}
Return
Niching Pareto Ant Colony Optimization Algorithm for Bi-Objective Pathfinding Problem
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2018.2822824.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we propose a niching Pareto ant colony optimization (NPACO) algorithm to solve the bi-objective pathfinding problem. First, based on a planar navigable data model, three different searching area restricted methods are proposed and compared. In addition, a node simplification strategy is introduced to simplify nodes that exist in network branch loops, eliminating the redundant search time in the branch loops. Afterward, we propose the elitist ants and weakened strategy for an ACO to overcome the problem caused by the impact of accumulated pheromone on the suboptimal path and apply the strategy to a PACO for urban city pathfinding. Finally, the niching method is adopted to simultaneously locate and maintain multiple optimal solutions to increase search robustness. The experimental results show that the NPACO with a restricted and simplified search area returns a Pareto optimal solution set that is uniformly distributed along the Pareto frontier with low computational complexity.
Keywords:
Pathfinding problem
Ant colony optimization
Pareto optimal solution
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.