Ant colony

Beckers, et al. [beckers94], have introduced a simple distributed sorting algorithm that is inspired by the self-organized way in which ant colonies sort their brood. 

Maier HR, Simpson AR, Zecchin AC, Foong WK, Phang KY, Seah HY, Tan CL (2003) Ant colony optimization for design of water distribution systems. J Water Resour Plan Manage 129 200-209... 

Swarm intelligence is a term that is applied to two rather different techniques — ant colony or pheromone trail optimization and particle swarm optimization. We deal here briefly with the latter. 

Figure 4.2. An ant colony A is soil brought to surface by ants B is ant holes in the subsurface C is a grub found at about 10 cm.

For this lifestyle to succeed, the wasp larvae obviously must avoid being recognized. If the ants were to detect them, they would suffer the fate of other intruders discovered in the nest expulsion or death. Although some intruders escape detection in ant nests because they resemble their ant hosts, Orasema larvae, pupae, and adults all are easily distinguishable from their hosts, at least by the human eye. Fire ants, however, are much more concerned with smell than physical appearance. Fire-ant odor and local colony odor are what matter to them. By these scents, ants identify their own nestmates as distinct from other species and even from fire ants belonging to rival colonies. The wasps strategy to avoid discovery is to smell just like fire ants. Larvae, pupae, and even adult wasps carry the distinctive scent of their particular fire ant colony. Shortly after the adult wasps leave the ant nest, this ant odor begins to fade. 

There are methods that deliberately avoid the use of gradient and Hessian information. Such approaches typically require many more iterations but can nevertheless save overall on computation. Some popular ones are the Simplex Method, Genetic Algorithms, Simulated Annealing, Particle Swarm and Ant Colony Optimization, and variants thereof. 

Gunturi, S.B., Narayanan, R. and Khandelwal, A. (2006) In silico ADME modelling 2 Computational models to predict human serum albumin binding affinity using ant colony systems. Bioorganic and Medicinal Chemistry, 14, 4118 129. 

In Proust s novel, the town of Balbec, with its slow pace of life and attractive landmarks, seems trapped in time, like an ancient ant trapped in amber. I like to think of In Search of Lost Time as a collection of chambers connected by tunnels in a dense ant colony. In one chamber is an endless party taking place at one point in the narrator s life—and if we escape through a tunnel, we are quickly whisked away to another chamber in which time congeals yet again. 

Porter, S. D. (1989). Effects of diet on the growth of laboratory fire ant colonies (Hymenoptera, Formicidae). Journal of the Kansas Entomological Society 62 288-291. 

Akino T., Knapp J. J., Thomas J. A. and Elmes G. W. (1999) Chemical mimicry and host specificity in the butterfly Maculinea rebeli, a social parasite of Myrmica ant colonies. Proc. R. Soc. London Ser. B, 266, 1419-1426. 

Millor, J., Halloy, J., Ame, J.-M. and Deneubourg, J.-L. (2006). Individual discrimination capability and collective choice in social insects. In Ant Colony Optimization and Swarm Intelligence, Lecture Notes in Computer Science. Berlin Springer, pp. 167-178. 

Figure 12.1 Harvester ant foragers are stimulated to leave the nest in search of seeds in response to cues present in the task-specific cuticular hydrocarbon profile of patrollers. After inhibition of foragers at harvester ant colonies by removal of patrollers, colony foraging behavior was rescued by the addition of ant mimics, glass beads coated with task-specific cuticular hydrocarbons from patrollers, to the nest entrance at a rate of 1 bead every 10 seconds. Data were normalized to account for differences in the absolute number of foragers active each day. The same letters above bars denote statistical significance using a Tukey s post-hoc test. From Greene and Gordon, 2003.

Figure 12.2 Foraging activity at harvester ant colonies was affected by the simulated return rate of patrollers back to the colony. Letters above bars denote differences in statistical significance among treatments (LSD, p < 0.05). Error bars denote standard error of the mean. From Greene and Gordon, 2007a.

Gordon, D.M. (1999). Interaction patterns and task allocation in ant colonies. In Information Processing in Social Insects, ed. C. Detrain, J.M. Pasteels and J.L. Deneubourg. Basel, Switzerland Birkhauser, pp.51-67. 

Pep3DSearch Implementation of ant colony optimization algorithm http //kyc.nenu.edu.cn/ Pep3DSearch/ Huang et al. (56)... 

Korb O, StU tzle T, Exner TE. 2006. PLANTS application of ant colony optimization to structure-based drug design. In Proc. Ant Colony Optimization and Swarm Intelligence, 5th International Workshop, ANTS 2006 LNCS 4150. pp. 247-258. 

When ants sense danger to the ant colony, they emit a substance called formic acid that alerts the entire colony. Acids in rainwater hollow out enormous limestone caverns and destroy valuable buildings and statues. Acids flavor many of the beverages and foods you like, and it s an acid in your stomach that helps digest what you eat. Bases also play a role in your life. The soap you use and the antacid tablet you may take for an upset stomach are bases. Perhaps you have already concluded that the household products you used in the DISCOVERY LAB are acids and bases. 

Some researchers have combined various optimization algorithms to improve the search efficiency and computational effort, including evolutionary algorithms (EA), simulated annealing (SA), particle swarm optimization (PSO), ant colony optimization (AGO), hybrid PSO-SQP, hybrid GA-ACO. Nevertheless, the combination of the GA and SQP algorithms is reported only in a few works [1,2]. 

A Posteriori Methods Using Multi-Objective Approach (many based on evolutionary algorithms, simulated annealing, ant colony techniques etc.) These relatively recent methods have found many applications in chemical engineering. They provide many Pareto-optimal solutions and thus more information useful for decision making is available. Role of the DM is after finding optimal solutions, to review and select one of them. Many optimal solutions found will not be used for implementation, and so some may consider it as a waste of computational time. 

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