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  • Gustavo Meirelles,
  • Bruno Brentan,
  • Joaquín Izquierdo,
  • Edevar Luvizotto Junior
Gustavo Meirelles
Universidade Federal de Minas Gerais

Corresponding Author:[email protected]

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Bruno Brentan
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Joaquín Izquierdo
Universitat Politecnica de Valencia
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Edevar Luvizotto Junior
Universidade Estadual de Campinas - Campus Cidade Universitaria Zeferino Vaz
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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.