From nonlinearity to optimality: pheromone trail foraging by ants

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Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach (2016, unknown): “Such nonlinearity has important implications for how an ant colony distributes its workforce, when confronted with a choice of food sources [29].”
Self-organized structures in a superorganism: do ants "behave" like molecules? (2006, background): “For instance, if the ants first discover and establish a trail to a low energy source, the recruitment to this poor source may be too strong for more profitable sources to compete even though individual foragers increase their trail-laying intensity [100,117, 126].”
The principles of collective animal behaviour (2006, result): “Sumpter & Beekman (2003) reported similar results for Pharaoh’s ants when they offered the ants two identical feeders in opposite directions from the nest.”
The biological principles of swarm intelligence (2007, background): “1993) and Monomorium pharaonis (Sumpter and Beekman 2003) leads the colony to efficiently select the most rewarding food source if several opportunities are discovered simultaneously.”