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A Multi-objective GA Based on Immune and Entropy Principle for FJSP

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Effective Methods for Integrated Process Planning and Scheduling

Part of the book series: Engineering Applications of Computational Methods ((EACM,volume 2))

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Abstract

This chapter presents a Multi-Objective Genetic Algorithm (MOGA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved MOGA, the fitness scheme based on Pareto-optimality is applied, and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Efficient crossover and mutation operators are proposed to adapt to the special chromosome structure. The proposed algorithm is evaluated on some representative instances, and the comparison with other approaches in the latest chapters validates the effectiveness of the proposed algorithm.

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Author information

Authors and Affiliations

  1. School of Mechanical Science and Engineering, HUST, State Key Laboratory of Digital Manufacturing and Equipment Technology, Wuhan, Hubei, China

    Xinyu Li

  2. School of Mechanical Science and Engineering, HUST, State Key Laboratory of Digital Manufacturing and Equipment Technology, Wuhan, Hubei, China

    Liang Gao

Authors
  1. Xinyu Li
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  2. Liang Gao
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Correspondence to Xinyu Li .

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Li, X., Gao, L. (2020). A Multi-objective GA Based on Immune and Entropy Principle for FJSP. In: Effective Methods for Integrated Process Planning and Scheduling. Engineering Applications of Computational Methods, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55305-3_14

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  • DOI: https://doi.org/10.1007/978-3-662-55305-3_14

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-55303-9

  • Online ISBN: 978-3-662-55305-3

  • eBook Packages: EngineeringEngineering (R0)

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