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Improved Interval Multi-objective Evolutionary Optimization Algorithm Based on Directed Graph

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Advances in Swarm Intelligence (ICSI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10386))

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Abstract

Multi-objective evolutionary algorithm for optimizing objectives with interval parameters is becoming more and more important in practice. The efficient comparison metrics on interval values and the associated offspring generations are critical. We first present a neighboring dominance metric for interval numbers comparisons. Then, the potential dominant solutions are predicted by constructing a directed graph with the neighboring dominance. We design a directed graph using those competitive solutions sorted with NSGA-II, and predict the possible evolutionary paths of next generation. A PSO mechanic is applied to generate the potential outstanding solutions in the paths, and these solutions are further used to improve the crossover efficiency. The experimental results demonstrate the performance of the proposed algorithm in improving the convergence of interval multi-objective evolutionary optimization.

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Acknowledgments

This paper is supported by the National Natural Science Foundation of China with granted No. 61473298 and 61473299.

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Authors and Affiliations

  1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China

    Xiaoyan Sun, Pengfei Zhang, Yang Chen & Yong Zhang

Authors
  1. Xiaoyan Sun
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  2. Pengfei Zhang
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  3. Yang Chen
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  4. Yong Zhang
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Corresponding author

Correspondence to Pengfei Zhang .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  4. Shenzhen University, Shenzhen, China

    Ben Niu

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Sun, X., Zhang, P., Chen, Y., Zhang, Y. (2017). Improved Interval Multi-objective Evolutionary Optimization Algorithm Based on Directed Graph. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-61833-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61832-6

  • Online ISBN: 978-3-319-61833-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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