VNE Strategy based on Chaotic Hybrid Flower Pollination Algorithm Considering Multi-criteria Decision Making
This work addresses the complex optimization needs in network resource allocation for virtual networks, presenting an incremental improvement over existing meta-heuristic methods.
The paper tackles the Virtual Network Embedding (VNE) problem by proposing a hybrid flower pollination algorithm optimized for discrete optimization, combining genetic algorithm operations and introducing chaotic optimization and a life cycle mechanism to enhance search capabilities and avoid premature convergence.
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. Aiming at these problems, the design strategy of hybrid flower pollination algorithm for Virtual Network Embedding (VNE) problem is discussed. Combining the advantages of the Genetic Algorithm (GA) 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 chaotic 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.