Data di Pubblicazione:
2006
Abstract:
Ant colony optimization metaheuristic (ACO) represents
a new class of algorithms particularly suited to solve
real-world combinatorial optimization problems. ACO algorithms,
published for the first time in 1991 by M. Dorigo
[Optimization, learning and natural algorithms (in Italian).
Ph.D. Thesis, Dipartimento di Elettronica, Politecnico di
Milano, Milan, 1992] and his coworkers, have been applied,
particularly starting from 1999 (Bonabeau et al., Swarm intelligence:
from natural to artificial systems, Oxford University
Press, New York, 1999; Dorigo et al., Artificial life
5(2):137–172, 1999; Dorigo and Di Caro, Ant colony optimization:
a new metaheuristic, IEEE Press, Piscataway, NJ,
1999; Dorigo et al., Ant colony optimization and swarm intelligence,
Springer, Berlin Heidelberg NewYork, 2004; Dorigo
and Stutzle, Ant colony optimization, MIT Press, Cambridge,
MA, 2004), to several kinds of optimization problems such as
the traveling salesman problem, quadratic assignment problem,
vehicle routing, sequential ordering, scheduling, graph
coloring, management of communications networks, and so
on. The ant colony optimization metaheuristic takes inspiration
from the studies of real ant colonies’ foraging behavior.
The main characteristic of such colonies is that individuals
have no global knowledge of problem solving but communicate
indirectly among themselves, depositing on the ground
a chemical substance called pheromone, which influences
probabilistically the choice of subsequent ants, which tend to
follow paths where the pheromone concentration is higher.
Such behavior, called stigmergy, is the basic mechanism that
controls ant activity and permits them to take the shortest
path connecting their nest to a food source. In this paper, it is
shown how to convert natural ant behavior to algorithms able
to escape from local minima and find global minimum solutions
to constrained combinatorial problems. Some examples
on plane trusses are also presented
a new class of algorithms particularly suited to solve
real-world combinatorial optimization problems. ACO algorithms,
published for the first time in 1991 by M. Dorigo
[Optimization, learning and natural algorithms (in Italian).
Ph.D. Thesis, Dipartimento di Elettronica, Politecnico di
Milano, Milan, 1992] and his coworkers, have been applied,
particularly starting from 1999 (Bonabeau et al., Swarm intelligence:
from natural to artificial systems, Oxford University
Press, New York, 1999; Dorigo et al., Artificial life
5(2):137–172, 1999; Dorigo and Di Caro, Ant colony optimization:
a new metaheuristic, IEEE Press, Piscataway, NJ,
1999; Dorigo et al., Ant colony optimization and swarm intelligence,
Springer, Berlin Heidelberg NewYork, 2004; Dorigo
and Stutzle, Ant colony optimization, MIT Press, Cambridge,
MA, 2004), to several kinds of optimization problems such as
the traveling salesman problem, quadratic assignment problem,
vehicle routing, sequential ordering, scheduling, graph
coloring, management of communications networks, and so
on. The ant colony optimization metaheuristic takes inspiration
from the studies of real ant colonies’ foraging behavior.
The main characteristic of such colonies is that individuals
have no global knowledge of problem solving but communicate
indirectly among themselves, depositing on the ground
a chemical substance called pheromone, which influences
probabilistically the choice of subsequent ants, which tend to
follow paths where the pheromone concentration is higher.
Such behavior, called stigmergy, is the basic mechanism that
controls ant activity and permits them to take the shortest
path connecting their nest to a food source. In this paper, it is
shown how to convert natural ant behavior to algorithms able
to escape from local minima and find global minimum solutions
to constrained combinatorial problems. Some examples
on plane trusses are also presented
Tipologia CRIS:
1.1 Articolo in rivista
Keywords:
Structural optimization
Elenco autori:
Venini, Paolo; Mauro, Serra
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