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A pattern-driven solution for designing multi-objective evolutionary algorithms

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Abstract

Multi-objective evolutionary algorithms (MOEAs) have been widely studied in the literature, which led to the development of several frameworks and techniques to implement them. Consequently, the reusability, scalability and maintainability became fundamental concerns in the development of such algorithms. To this end, the use of design patterns (DPs) can benefit, ease and improve the design of MOEAs. DPs are reusable solutions for common design problems, which can be applied to almost any context. Despite their advantages to decrease coupling, increase flexibility, and allow an easier design extension, DPs have been underexplored for MOEA design. In order to contribute to this research topic, we propose a pattern-driven solution for the design of MOEAs. The MOEA designed with our solution is compared to another MOEA designed without it. The comparison considered: the Integration and Test Order (ITO) problem and the Traveling Salesman problem (TSP). Obtained results show that the use of this DP-driven solution allows the reuse of MOEA components, without decreasing the quality, in terms of hypervolume. This means that the developer can extend the algorithms to include other components using only object-oriented mechanisms in an easier way, while maintaining the expected results.

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Notes

  1. More info at http://www.sigevo.org/gecco-2014/workshops.html#mdp and http://www.sigevo.org/gecco-2015/workshops.html#wmdp.

  2. http://moeaframework.org/.

  3. The benchmarks for the Multi-Objective TSP were obtained at https://eden.dei.uc.pt/~paquete/tsp/.

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Acknowledgements

This work is supported by the Brazilian funding agencies CAPES and CNPq under the Grants: 307762/2015-7 and 473899/2013-2.

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  1. DInf - Federal University of Paraná, CP: 19081, Curitiba, PR, CEP 19031-970, Brazil

    Giovani Guizzo & Silvia R. Vergilio

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  1. Giovani Guizzo
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  2. Silvia R. Vergilio
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Correspondence to Giovani Guizzo.

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Guizzo, G., Vergilio, S.R. A pattern-driven solution for designing multi-objective evolutionary algorithms. Nat Comput 19, 481–494 (2020). https://doi.org/10.1007/s11047-018-9677-y

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