A Novel Artificial Bee Colony Optimization Algorithm with SVM for Bio-inspired Software-Defined Networking

Hsiu Sen Chiang, Arun Kumar Sangaiah, Mu Yen Chen, Jia Yu Liu

研究成果: Article同行評審

16 引文 斯高帕斯(Scopus)


In recent years, artificial intelligence and bio-inspired computing methodologies have risen rapidly and have been successfully applied to many fields. Bio-inspired network systems are a field of biology and computer science, it has the high relation to the bio-inspired computing and bio-inspired system. It has the self-organizing and self-healing characteristics that help them in achieving complex tasks with much ease in the network environment. Software-defined networking provides a breakthrough in network transformation. However, increasing network requirement and focus on the controller for determining the network functionality and resources allocations aims at self-management capabilities. More recently, the artificial bee colony (ABC) algorithm has been used to solve the issues of parameter optimization. In this paper, a discretized food source for an artificial bee colony (DfABC) optimization algorithm is proposed and applied to optimize the kernel parameters of a support vector machine (SVM) model, creating a new hybrid. In order to further improve prediction accuracy, the proposed DfABC algorithm is applied to six popular UCI datasets. We also compare the DfABC algorithm to particle swarm optimization (PSO), the genetic algorithm (GA), and the original ABC algorithm. The experimental results show that the proposed DfABC-SVM model achieves better classification accuracy with a shorter convergence time, outperforming the other hybrid artificial intelligence models.

頁(從 - 到)310-328
期刊International Journal of Parallel Programming
出版狀態Published - 2020 4月 1

All Science Journal Classification (ASJC) codes

  • 軟體
  • 理論電腦科學
  • 資訊系統


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