Ant Colony based model selection for functional-input Gaussian process regression

An algorithm of multi-granularity Learning Preference Mining based on Ant Colony Optimization (ACO-LPM) was proposed to address the problems about too many learning

Surrogate Model and Hyperparameters As a surrogate model, the DTS- CMA-ES uses Gaussian processes [31] due to their ability to estimate the whole distribution of the fitness

This paper presents a hybridization of the Ant Colony Algorithm and a Complete Local search with Memory heuristic, in order to maximize as much as possible; the free time

Initially, an ant colony algorithm is developed for automatic hose routing with a single objective (minimizing the length of the pipes) (with the intention of extending it to

MMAS has been improved with the integration of a local search. The 2-opt operator has been applied to the best ant before the pheromone update [13]. This method has also improved

The elitism-based immigrants ACO (EIACO) algorithm uses an immigrants scheme where ants are generated by mutating the best ant of the previous iter- ation. These immigrants also

We propose an approach based on Swarm Intelligence — more specifically on Ant Colony Optimization (ACO) — to improve search engines’ perfor- mance and reduce information overload

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