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A skin membrane-driven membrane algorithm for many-objective optimization

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Abstract

Many-objective optimization problems refer to problems that hold more than three conflicting objectives, which are more challenging than those with two or three objectives. Membrane computing models, usually termed P systems, are a class of living cell-inspired computing models, which provide a rich framework for solving a variety of challenging problems. In this paper, a membrane computing model-based algorithm is proposed for many-objective optimization. Specifically, the population in the skin membrane is divided into two subpopulations, one used for guiding the convergence of populations in the internal membrane, while the other taking charge of the diversity of populations. Experimental results on benchmark test problems for many-objective optimization indicate the superiority of the developed algorithm over existing evolutionary many-objective optimization algorithms and P systems based multi-objective optimization algorithms.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (61402187, 61502001, 61502004, 61502535, 61502532 and 61502012), Beijing Natural Science Foundation (4164096) and the Fundamental Research Funds for the Central Universities (2652015340), China Postdoctoral Science Foundation funded Project (2016M592267).

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Authors and Affiliations

  1. Department of Accounting, Huishang Vocational College, Hefei, 230032, China

    Zhangxiao Li

  2. School of Computer Science and Technology, Anhui University, Hefei, 230039, China

    Lei Zhang, Yansen Su & Jun Li

  3. College of Computer and Communication Engineering, China University of Petroleum, Qingdao, 266580, China

    Xun Wang

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  1. Zhangxiao Li
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  2. Lei Zhang
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  4. Jun Li
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Correspondence to Xun Wang.

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Li, Z., Zhang, L., Su, Y. et al. A skin membrane-driven membrane algorithm for many-objective optimization. Neural Comput & Applic 30, 141–152 (2018). https://doi.org/10.1007/s00521-016-2675-z

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