Abstract
Population-based ant colony optimization (PACO) is one of the most efficient ant colony optimization (ACO) algorithms. Its strength results from a pheromone memory model in which pheromone values are calculated based on a population of solutions. In each iteration an iteration-best solution may enter the population depending on an update strategy specified. When a solution enters or leaves the population the corresponding pheromone trails are updated. The article shows that the PACO pheromone memory model can be utilized to speed up the process of selecting a new solution component by an ant. Depending on the values of parameters, it allows for an implementation which is not only memory efficient but also significantly faster than the standard approach.
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Skinderowicz, R. (2014). Implementing Population-Based ACO. In: Hwang, D., Jung, J.J., Nguyen, NT. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. Lecture Notes in Computer Science(), vol 8733. Springer, Cham. https://doi.org/10.1007/978-3-319-11289-3_61
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DOI: https://doi.org/10.1007/978-3-319-11289-3_61
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