document: Optimization of the memory weighting function in stochastic functional self-organized sorting performed by a team of autonomous mobile agents The activity of a team of autonomous mobile agents formed by identical `` robot-like-ant '' individuals capable of performing a random walk through an environment that are able to recognize and move different `` objects '' is modeled . The emergent desired behavior is a distributed sorting and clustering based only on local information and a memory register that records the past objects encountered . An optimum weighting function for the memory registers is theoretically derived . The optimum time-dependent weighting function allows sorting and clustering of the randomly distributed objects in the shortest time . By maximizing the average speed of a texture feature -LRB- the contrast -RRB- we check the central assumption , the intermediate steady-states hypothesis , of our theoretical result . It is proved that the algorithm optimization based on maximum speed variation of the contrast feature gives relationships similar to the theoretically derived annealing law
doc_bio_tags: O O O B I I O O O O B O O O O O B I I O O O O O O B I I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O
extractive_keyphrases: autonomous mobile agents random walk memory weighting function sorting clustering algorithm optimization