当前位置: X-MOL 学术Neural Comput. & Applic. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
VNE strategy based on chaos hybrid flower pollination algorithm considering multi-criteria decision making
Neural Computing and Applications ( IF 6 ) Pub Date : 2020-04-04 , DOI: 10.1007/s00521-020-04827-5
Peiying Zhang , Fanglin Liu , Gagangeet Singh Aujla , Sahil Vashisht

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, mapping average quotation, average time delay, average packet loss rate, and the average running time of the algorithm.



中文翻译:

考虑多准则决策的基于混沌混合花授粉算法的VNE策略

随着科学技术的发展和对多准则决策(MCDM)的需求,要解决的优化问题变得极其复杂。理论上准确的最优解通常很难获得。因此,基于多点搜索的元启发式算法受到了广泛的关注。花授粉算法(FPA)是一种新的群体智能元启发式算法,可以通过切换概率有效控制全局搜索和局部搜索之间的平衡,逐渐受到研究人员的关注。但是,该算法仍然存在优化算法常见的问题。例如,以最优解为指导的全局搜索操作很容易将算法引入局部最优,而向量引导的搜索过程不适合解决离散空间中的一些问题。此外,该算法没有很好地考虑动态多准则决策问题。针对这些问题,讨论了虚拟网络嵌入问题的混合花授粉算法的设计策略。结合遗传算法和FPA的优点,针对离散优化问题的特点对算法进行了优化。交叉操作用来代替异花授粉操作完成全局搜索,用自花操作代替变异操作来增强局部搜索的能力。此外,引入了生命周期机制作为对传统的基于适应度的选择策略的补充,以避免过早收敛。引入混沌优化策略来代替随机序列引导的交叉过程,以增强全局搜索能力并降低产生无效个体的概率。此外,引入两层BP神经网络来代替传统的目标函数,以加强动态MCDM能力。仿真结果表明,该方法在链路负载均衡、收益成本比、VN请求接受率、映射平均报价、平均时延、平均丢包率、算法平均运行时间等方面具有良好的性能。引入两层BP神经网络来代替传统的目标函数,以加强动态MCDM能力。仿真结果表明,该方法在链路负载均衡、收益成本比、VN请求接受率、映射平均报价、平均时延、平均丢包率、算法平均运行时间等方面具有良好的性能。引入两层BP神经网络来代替传统的目标函数,以加强动态MCDM能力。仿真结果表明,该方法在链路负载均衡、收益成本比、VN请求接受率、映射平均报价、平均时延、平均丢包率、算法平均运行时间等方面具有良好的性能。

更新日期:2020-04-04
down
wechat
bug