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Discrete Cuckoo Search for Combinatorial Optimization pp 45–70Cite as

DCS Applications

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Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

Discrete versions of CS are applied to solve several combinatorial optimization problems (Yang and Deb 2014; Fister et al. 2014; Shehab et al. 2017). In all these applications, the real constraint is not enhancing the robustness of CS and solve efficiently these problems, but keeping its robustness in the adaptation and implementation phases for each specific problem such as optimal Distributed Generation (DG) allocation in a smart distribution grid (Buaklee and Hongesombut 2013), parallel machine scheduling (Guo et al. 2015), flow shop scheduling (Wang et al. 2017), optimal power flow (Mishra et al. 2015), graph coloring problem (Mahmoudi and Lotfi 2015), vehicle routing problem (Zheng et al. 2013), and reliability optimization problems (Valian et al. 2013). However, even if these phases are validated, generalizing DCS to solve all these problems and others is stilling an independent issue.

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Authors and Affiliations

  1. Ecole Supérieure de Technologie d’Essaouira, Essaouira, Morocco

    Aziz Ouaarab

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  1. Aziz Ouaarab
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Correspondence to Aziz Ouaarab .

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Ouaarab, A. (2020). DCS Applications. In: Discrete Cuckoo Search for Combinatorial Optimization. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-3836-0_5

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