Learning from ants
Learning from ants: Ant colony optimization algorithms are versatile and useful for several real-world applications. These applications usually center on complex optimization problems. Here are three uses for the algorithm.
If you haven't already, gain the intuition of the ant colony optimization algorithm here: https://rhurbans.com/ant-colony-optimization-for-beginners/
In a logistics example, perhaps the distance between destinations, traffic conditions, types of packages being delivered, and times of day are important constraints to optimize the operations of the business. ACOs can help with that.
Job scheduling is required in almost any industry. Nurse shifts are important to ensure that good health care can be provided while keeping nurses healthy. Computational jobs on servers must be scheduled in an optimal manner to maximize the use of the hardware.11
Although there are better ways, ACOs can be used for edge detection in image processing. An image is composed of several adjacent pixels, and the ants can move from pixel to pixel, leaving behind pheromone trails that would trace an object.
If you're interested in more details about ant colony optimization and AI, see Grokking AI Algorithms with Manning Publications: http://bit.ly/gaia-book, consider following me - @RishalHurbans, or join my mailing list for infrequent knowledge drops: https://rhurbans.com/subscribe.
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