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A multi-objective genetic algorithm for the hot mix asphalt problem

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Abstract

It is desirable for the work done in any construction process to be both cost-effective and durable. A thorough consideration of the matter reveals that the optimization of real-world problems involves multiple objectives. Bituminous hot mixtures, which are widely used in motorway construction, consist of aggregate and bitumen. The ratio between the different types of aggregate and bitumen forms the input to the real-world problem defined in this article, and the results of a test of the obtained asphalt in three different fields form the output. Our aim is to optimize these three outputs simultaneously to obtain a solution space with the most appropriate inputs. To optimize this problem, a new multi-objective optimization approach is proposed and tested in various ways and is finally adapted to the hot mix asphalt problem. Since the mathematical model of the objective function for this problem is fairly difficult, a fuzzy logic expert system is developed to act as the objective function. We believe that our approach to solving complex problems such as these forms a significant contribution to the literature.

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On reasonable request, the corresponding author will provide the datasets created or used in the current work.

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Acknowledgements

We would like to thank the Scientific Research Projects Unit of Tokat Gaziosmanpaşa University, which provided financial means under project 2019/65 for the realization of this study.

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Authors and Affiliations

  1. Department of Computer Engineering and Civil Engineering, Tokat Gaziosmanpaşa University, Taşlıçiftlik Campus, 60250, Tokat, Turkey

    Mustafa Altiok, Erdinç Halis Alakara & Melih Naci Ağaoğlu

  2. Department of Computer Engineering, Konya Technical University, Alaaddin Keykubat Campus, 42250, Konya, Turkey

    Mesut Gündüz

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  1. Mustafa Altiok
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  2. Erdinç Halis Alakara
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  3. Mesut Gündüz
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Correspondence to Mustafa Altiok.

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Altiok, M., Alakara, E.H., Gündüz, M. et al. A multi-objective genetic algorithm for the hot mix asphalt problem. Neural Comput & Applic 35, 8197–8225 (2023). https://doi.org/10.1007/s00521-022-08095-3

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