TY - CHAP
A1 - Jaqueline S. Angelo
A2 - Helio J.C. Barbosa
ED1 - Avi Ostfeld
Y1 - 2011-02-04
PY - 2011
T1 - Ant Colony Algorithms for Multiobjective Optimization
N2 - Ants communicate information by leaving pheromone tracks. A moving ant leaves, in varying quantities, some pheromone on the ground to mark its way. While an isolated ant moves essentially at random, an ant encountering a previously laid trail is able to detect it and decide with high probability to follow it, thus reinforcing the track with its own pheromone. The collective behavior that emerges is thus a positive feedback: where the more the ants following a track, the more attractive that track becomes for being followed; thus the probability with which an ant chooses a path increases with the number of ants that previously chose the same path. This elementary ant's behavior inspired the development of ant colony optimization by Marco Dorigo in 1992, constructing a meta-heuristic stochastic combinatorial computational methodology belonging to a family of related meta-heuristic methods such as simulated annealing, Tabu search and genetic algorithms. This book covers in twenty chapters state of the art methods and applications of utilizing ant colony optimization algorithms. New methods and theory such as multi colony ant algorithm based upon a new pheromone arithmetic crossover and a repulsive operator, new findings on ant colony convergence, and a diversity of engineering and science applications from transportation, water resources, electrical and computer science disciplines are presented.
BT - Ant Colony Optimization
SP - Ch. 5
UR - https://doi.org/10.5772/14237
DO - 10.5772/14237
SN -
PB - IntechOpen
CY - Rijeka
Y2 - 2020-11-27
ER -