7 October 2015

Animal algorithms

“What’s in a name?” asks Shakespeare’s Juliet of her beloved Romeo when sworn family enemies, the Montagues and the Capulets, forbid their affair. But, while a rose by any other name may indeed smell as sweet, there are certain areas of human endeavour that pick their names very carefully. I’m thinking of the likes of the world of genetic science wherein exist the likes of Sonic Hedgehog genes and in magnetic resonance spectroscopy with its bizarrely named COSY, NOESY and SLITDRESS techniques…then there is the world of algorithms where the behaviour or social, swarming and foraging animals are used to add a clue as to the nature of the process in question, Forget “Blum Blum Shub” and the lagged Lagged Fibonacci generator, forget the Travelling salesman problem and Lexicographic breadth-first search, here we have:

“Shuffled Frog Leaping” is one technique that features in a forthcoming paper in the International Journal of Bio-inspired Computation from Farzan Rashidi, Ebrahim Abiri, Taher Niknam, Mohammad Reza Salehi on optimising power plants.

The pros and cons of “Bat Foraging” and “Cuckoo Search” in spam filtering are compared in a paper from Arulanand Natarajan, S. Subramanian, and K. Premalatha in the International Journal of Bio-Inspired Computation

Mohammad Reza Jabbarpour, Hossein Malakooti, Rafidah Md Noor, Nor Badrul Anuar, Norazlina Khamis discussed “Ant Colony Optimization” in the context of road traffic also in the International Journal of Bio-Inspired Computation.

One usually thinks of shoals of fish and schools of whales, but “Artificial Fish Swarm” optimisation is the order of the day on this paper on natural language processing published in the International Journal of Artificial Intelligence and Soft Computing.

The “Artificial Bee Colony Algorithm” is widely used and comes into its own in a paper on profitable undirected routing published in the International Journal of Operational Research

In 2009, “Glow-worms Swarm Optimization” was a new method for optimising multi-modal functions according to work published in the International Journal of Computational Intelligence Studies by K.N. Krishnanand and D. Ghose. Xiangqin Xiang reports on an improvement to the related “Firefly Algorithm” for numerical optimization in the International Journal of Computing Science and Mathematics

And, the “Eagle Strategy” soars in a paper from Xin-She Yang and Suash Deb published in the International Journal of Bio-Inspired Computation.

Then there is “Bacterial Colony Foraging” “Bug Triaging Based on Ant Systems”, the “Krill Herd Algorithm”, “Bee-inspired Metaheuristics”, “Bee Royalty Offspring Algorithm”, “Slime Mould Computers”. The list goes on and they’re all bio-inspirational…

Original article: Animal algorithms.
via Science Spot » Inderscience http://ift.tt/1VDP7Dt

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