  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.
