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A multi-objective membrane algorithm guided by the skin membrane

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Abstract

Multi-objective optimization problems exist widely in the field of engineering and science. Many nature-inspired methods, such as genetic algorithms, particle swarm optimization algorithms and membrane computing model based algorithms, were proposed to solve the problems. Among these methods, membrane computing model based algorithms, also termed membrane algorithms, are becoming a current research hotspot because the successful linkage of membrane computing and evolutionary algorithms. In the past years, a lot of effective multi-objective membrane algorithms have been designed, where the skin membrane was often only used as an archive to store good solutions. In this paper, we propose an effective multi-objective membrane algorithm guided by the skin membrane, named SMG-MOMA, where the information of solutions stored in the skin membrane is used to guide the evolution of internal membranes. A skin membrane guiding strategy is suggested by allocating the solutions in skin membrane to internal membranes. Experimental results on ZDT and DTLZ benchmark multi-objective problems show that the proposed algorithm outperforms the-state-of-the-art multi-objective optimization algorithms.

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Acknowledgments

This work was supported in part by the Natural Science Foundation of China (Grant Nos. 61272152, 61502004 and 61502001). This work was also partially supported by grants from the Academic and Technology Leader Imported Project of Anhui University (No. J10117700050) and Information Assurance technology Collaborative Innovation Center (No. y01008409).

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Authors and Affiliations

  1. School of Computer Science and Technology, Institute of Bio-inspired Intelligence and Mining Knowledge, Anhui University, Hefei, 230039, China

    Xingyi Zhang, Jun Li & Lei Zhang

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  1. Xingyi Zhang
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  2. Jun Li
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  3. Lei Zhang
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Correspondence to Lei Zhang.

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Zhang, X., Li, J. & Zhang, L. A multi-objective membrane algorithm guided by the skin membrane. Nat Comput 15, 597–610 (2016). https://doi.org/10.1007/s11047-016-9572-3

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