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VNE strategy based on chaos hybrid flower pollination algorithm considering multi-criteria decision making

  • S. I : Hybridization of Neural Computing with Nature Inspired Algorithms
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Abstract

With the development of science and technology and the need for multi-criteria decision making (MCDM), the optimization problem to be solved becomes extremely complex. The theoretically accurate and optimal solutions are often difficult to obtain. Therefore, meta-heuristic algorithms based on multi-point search have received extensive attention. The flower pollination algorithm (FPA) is a new swarm intelligence meta-heuristic algorithm, which can effectively control the balance between global search and local search through a handover probability, and gradually attracts the attention of researchers. However, the algorithm still has problems that are common to optimization algorithms. For example, the global search operation guided by the optimal solution is easy to lead the algorithm into local optimum, and the vector-guided search process is not suitable for solving some problems in discrete space. Moreover, the algorithm does not consider dynamic multi-criteria decision problems well. Aiming at these problems, the design strategy of hybrid flower pollination algorithm for virtual network embedding problem is discussed. Combining the advantages of the genetic algorithm and FPA, the algorithm is optimized for the characteristics of discrete optimization problems. The cross-operation is used to replace the cross-pollination operation to complete the global search and replace the mutation operation with self-pollination operation to enhance the ability of local search. Moreover, a life cycle mechanism is introduced as a complement to the traditional fitness-based selection strategy to avoid premature convergence. A chaos optimization strategy is introduced to replace the random sequence-guided crossover process to strengthen the global search capability and reduce the probability of producing invalid individuals. In addition, a two-layer BP neural network is introduced to replace the traditional objective function to strengthen the dynamic MCDM ability. Simulation results show that the proposed method has good performance in link load balancing, revenue–cost ratio, VN requests acceptance ratio, map** average quotation, average time delay, average packet loss rate, and the average running time of the algorithm.

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Acknowledgements

This work is supported by “the Fundamental Research Funds for the Central Universities” of China University of Petroleum (East China) (Grant No. 18CX02139A), the Shandong Provincial Natural Science Foundation, China (Grant No. ZR2014FQ018), and the Demonstration and Verification Platform of Network Resource Management and Control Technology (Grant No. 05N19070040). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

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Authors and Affiliations

  1. College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, People’s Republic of China

    Peiying Zhang & Fanglin Liu

  2. School of Computing, Newcastle University, Newcastle Upon Tyne, UK

    Gagangeet Singh Aujla

  3. Computer Science and Engineering Department, Chandigarh University, Gharuan, Mohali, Punjab, India

    Gagangeet Singh Aujla

  4. Research and Development Department, CGC College of Engineering, Mohali, Punjab, 140307, India

    Sahil Vashisht

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  1. Peiying Zhang
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Correspondence to Peiying Zhang or Gagangeet Singh Aujla.

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Zhang, P., Liu, F., Aujla, G.S. et al. VNE strategy based on chaos hybrid flower pollination algorithm considering multi-criteria decision making. Neural Comput & Applic 33, 10673–10684 (2021). https://doi.org/10.1007/s00521-020-04827-5

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