masterThesis
Uma colônia de formigas para o caminho mais curto multiobjetivo
Fecha
2011-02-07Registro en:
BEZERRA, Leonardo Cesar Teonácio. Uma colônia de formigas para o caminho mais
curto multiobjetivo. 2011. 104 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal do Rio Grande do Norte, Natal, 2011.
Autor
Bezerra, Leonardo Cesar Teonácio
Resumen
Multi-objective combinatorial optimization problems have peculiar characteristics that
require optimization methods to adapt for this context. Since many of these problems are
NP-Hard, the use of metaheuristics has grown over the last years. Particularly, many
different approaches using Ant Colony Optimization (ACO) have been proposed. In this
work, an ACO is proposed for the Multi-objective Shortest Path Problem, and is compared
to two other optimizers found in the literature. A set of 18 instances from two
distinct types of graphs are used, as well as a specific multiobjective performance assessment
methodology. Initial experiments showed that the proposed algorithm is able
to generate better approximation sets than the other optimizers for all instances. In the
second part of this work, an experimental analysis is conducted, using several different
multiobjective ACO proposals recently published and the same instances used in the first
part. Results show each type of instance benefits a particular type of instance benefits a
particular algorithmic approach. A new metaphor for the development of multiobjective
ACOs is, then, proposed. Usually, ants share the same characteristics and only few works
address multi-species approaches. This works proposes an approach where multi-species
ants compete for food resources. Each specie has its own search strategy and different
species do not access pheromone information of each other. As in nature, the successful
ant populations are allowed to grow, whereas unsuccessful ones shrink. The approach introduced
here shows to be able to inherit the behavior of strategies that are successful
for different types of problems. Results of computational experiments are reported and
show that the proposed approach is able to produce significantly better approximation
sets than other methods