Applied Intelligence

, Volume 41, Issue 1, pp 145–166 | Cite as

Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts

  • E. Osaba
  • F. Diaz
  • E. Onieva


In this paper, a new multiple population based meta-heuristic to solve combinatorial optimization problems is introduced. This meta-heuristic is called Golden Ball (GB), and it is based on soccer concepts. To prove the quality of our technique, we compare its results with the results obtained by two different Genetic Algorithms (GA), and two Distributed Genetic Algorithms (DGA) applied to two well-known routing problems, the Traveling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP). These outcomes demonstrate that our new meta-heuristic performs better than the other techniques in comparison. We explain the reasons of this improvement.


Meta-heuristics Golden ball Distributed genetic algorithm Routing problems Combinatorial optimization Intelligent transportation systems 



This work is an extension of the two-page late-breaking abstract presented in the fifteenth annual conference on genetic and evolutionary computation (GECCO)[82]. In that short abstract we introduce a preliminary version of our technique in a very concise way.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Deusto Institute of Technology (DeustoTech)University of DeustoBilbaoSpain

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