GRAND TOUR ALGORITHM: NEW SWARM-BASED OPTIMIZATION
Gustavo Meirelles 1*, Bruno Brentan2, Joaquín Izquierdo 3, Edevar
Luvizotto Jr. 4
1 Department of Hydraulic Engineering and Water
Resources - ERH, Universidade Federal de Minas Gerais, 31270-901, Belo
Horizonte, Brazil;
gustavo.meirelles@ehr.ufmg.br
2 Department of Hydraulic Engineering and Water
Resources - ERH, Universidade Federal de Minas Gerais, 31270-901;
brentan@ehr.ufmg.br
3 Fluing-Institute for Multidisciplinary Mathematics,
Universitat Politècnica de València, Camino de Vera s/n, 46022,
Valencia, Spain;
jizquier@upv.es
4 Department of Water Resources - DRH, Universidade
Estadual de Campinas, 13083-889, Campinas, Brazil;
edevar@fec.unicamp.br
ABSTRACT: Metaheuristic algorithms based on the collective
behavior of nature social groups, such as ants and bees, have been
widely explored to solve many optimization problems in engineering and
other sciences. The processing time and the chance to end up in a local
optimal solution are drawbacks of these algorithms, and none has proved
to outperform the others. In this paper, an improved swarm optimization
technique, named Grand Tour Algorithm (GTA), based on the behavior of a
peloton of cyclists, is introduced and applied to sixteen benchmarking
optimization problems in the literature to evaluate its performance in
comparison to the original particle swarm optimization (PSO) algorithm.
Most of these benchmarking problems are tackled with a number of 20,000
variables, a really huge number inspired in the human genome. Under
these conditions, GTA clearly outperforms the classical PSO. In
addition, various sensitivity analyses are performed to verify the
influence of the initial parameters in the GTA efficiency. It can be
demonstrated that the GTA fulfils such coveted main aspects of an
optimization algorithm as ease of implementation, speed of convergence,
and reliability, thus confirming GTA’s improved performance.
KEYWORDS: optimization, swarm optimization, benchmarking
problems.