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

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Abstract

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.

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Acknowledgement

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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  1. Deusto Institute of Technology (DeustoTech), University of Deusto, Av. Universidades 24, Bilbao, 48007, Spain

    E. Osaba, F. Diaz & E. Onieva

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  1. E. Osaba
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  3. E. Onieva
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Correspondence to E. Onieva.

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Osaba, E., Diaz, F. & Onieva, E. Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Appl Intell 41, 145–166 (2014). https://doi.org/10.1007/s10489-013-0512-y

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