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Evolutionary and Ant Colony Optimization Based Approaches for a Two-Dimensional Strip Packing Problem

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Natural Intelligence for Scheduling, Planning and Packing Problems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 250))

Abstract

In the last few years, metaheuristic approaches have shown an important development in many application areas. This situation has turned them into one of the more appropriate candidates when dealing with difficult real-world problems for which timely, good-quality solutions are necessary. Furthermore, the class of metaheuristic approaches includes a large number of variants and designs which mainly depend on the concepts from which they are inspired. This chapter aims at giving an overview of Evolutionary Algorithms and Ant Colony Optimization when applied to the two-dimensional strip packing problem. The respective performance of these two metaheuristics are analyzed and compared from different perspectives by implementing a Genetic Algorithm and an Ant Colony System.

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Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Nacional de La Pampa, Calle 110 y 9, General Pico, La Pampa, (6360), Argentina

    Carolina Salto

  2. LIDIC, Universidad Nacional de San Luis, Ejército de Los Andes 950, San Luis, (5700), Argentina

    Guillermo Leguizamón

  3. E.T.S.I. Informática, Universidad de Málaga, 29071, Málaga, España

    Enrique Alba & Juan M. Molina

Authors
  1. Carolina Salto
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  2. Guillermo Leguizamón
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  3. Enrique Alba
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  4. Juan M. Molina
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Editors and Affiliations

  1. School of Computing & Design, Swinburne University of Technology (Sarawak Campus), Jalan Simpang Tiga, 93350, Kuching, Sarawak, Malaysia

    Raymond Chiong  & Sandeep Dhakal  & 

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Salto, C., Leguizamón, G., Alba, E., Molina, J.M. (2009). Evolutionary and Ant Colony Optimization Based Approaches for a Two-Dimensional Strip Packing Problem. In: Chiong, R., Dhakal, S. (eds) Natural Intelligence for Scheduling, Planning and Packing Problems. Studies in Computational Intelligence, vol 250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04039-9_10

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  • DOI: https://doi.org/10.1007/978-3-642-04039-9_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04038-2

  • Online ISBN: 978-3-642-04039-9

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