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A Multi-objective Genetic Algorithm Based on Individual Density Distance

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Data Science (ICPCSEE 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 728))

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Abstract

The uniform and extension distribution of the optimal solution are very important criterion for the quality evaluation of the multi-objective programming problem. A genetic algorithm based on agent and individual density is used to solve the multi-objective optimization problem. In the selection process, each agent is selected according to the individual density distance in its neighborhood, and the crossover operator adopts the simulated binary crossover method. The self-learning behavior only applies to the individuals with the highest energy in current population. A few classical multi-objective function optimization examples were used tested and two evaluation indexes U-measure and S-measure are used to test the performance of the algorithm. The experimental results show that the algorithm can obtain uniformity and widespread distribution Pareto solutions.

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Acknowledgment

This work was supported by Tianjin Research Program Application Foundation and Advanced Technology (14JCYBC15400).

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Authors and Affiliations

  1. School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China

    Lianshuan Shi & Huahui Wang

Authors
  1. Lianshuan Shi
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  2. Huahui Wang
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Corresponding author

Correspondence to Lianshuan Shi .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Beiji Zou

  2. Harbin Engineering University, Harbin, China

    Qilong Han

  3. Harbin University of Science and Technology, Harbin, China

    Guanglu Sun

  4. Northeast Forestry University, Harbin, China

    Weipeng Jing

  5. Huaihua University, Huaihua, Hunan, China

    Xiaoning Peng

  6. Sciences of Country Tripod Institute of Data Science, Harbin, China

    Zeguang Lu

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© 2017 Springer Nature Singapore Pte Ltd.

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Shi, L., Wang, H. (2017). A Multi-objective Genetic Algorithm Based on Individual Density Distance. In: Zou, B., Han, Q., Sun, G., Jing, W., Peng, X., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-10-6388-6_36

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  • DOI: https://doi.org/10.1007/978-981-10-6388-6_36

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6387-9

  • Online ISBN: 978-981-10-6388-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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