VNE Strategy based on Chaotic Hybrid Flower Pollination Algorithm Considering Multi-criteria Decision Making

TL;DR

This paper proposes a novel hybrid flower pollination algorithm incorporating chaos optimization and genetic algorithm techniques to improve virtual network embedding solutions for complex multi-criteria decision-making problems.

Contribution

It introduces a hybrid algorithm combining GA and FPA with chaos optimization and a lifecycle mechanism to enhance global search and prevent premature convergence in VNE optimization.

Findings

Enhanced global search capability due to chaos optimization

Improved local search through self-pollination operations

Reduced invalid solutions with lifecycle mechanism

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. 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.