ACO Algorithm for Path Planning Using Random Walks and PCA Evaluation
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This work proposes a random walk method to generate prior knowledge for an Ant Colony Optimization (ACO) algorithm. We assessed our work in a single agent path planning problem because it guarantees a minimal contextualization of a decision-making problem for tracking dilemmas such as state and action spaces and the trade-off between exploration and exploitation. Additionally, this work evaluates the algorithm through a Principal Component Analysis (PCA).
Random Walks, Ant Colony Optimization, Pheromones Initializers, Principal Component Analysis, Path Planning
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