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One way to improve ant colony algorithm path planning is by allowing it to reset map information. This can be achieved by incorporating a feedback mechanism that periodically updates the map with new information. Additionally, the algorithm can be extended to consider multiple paths and their corresponding costs, allowing for more comprehensive and accurate path planning. Another potential improvement could involve incorporating machine learning techniques to optimize the algorithm's performance based on historical data. By doing so, the algorithm can adapt and improve over time, leading to even more efficient path planning.