Hybridizing Evolutionary Negative Selection Algorithm and Local Search for Large-Scale Satisfiability Problems

  • Authors:

  • Peng Guo;Wenjian Luo;Zhifang Li;Houjun Liang;Xufa Wang

  • Affiliations:

  • Nature Inspired Computation and Applications Laboratory (NICAL), School of Computer Science and Technology, University of Science and Technology of China, Hefei, China 230027 and Anhui Key Laborat ...;Nature Inspired Computation and Applications Laboratory (NICAL), School of Computer Science and Technology, University of Science and Technology of China, Hefei, China 230027 and Anhui Key Laborat ...;Nature Inspired Computation and Applications Laboratory (NICAL), School of Computer Science and Technology, University of Science and Technology of China, Hefei, China 230027 and Anhui Key Laborat ...;Nature Inspired Computation and Applications Laboratory (NICAL), School of Computer Science and Technology, University of Science and Technology of China, Hefei, China 230027 and Anhui Key Laborat ...;Nature Inspired Computation and Applications Laboratory (NICAL), School of Computer Science and Technology, University of Science and Technology of China, Hefei, China 230027 and Anhui Key Laborat ...

  • Venue:
  • ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper introduces a hybrid algorithm called as the HENSA-SAT for the large-scale Satisfiability (SAT) problems. The HENSA-SAT is the hybrid of Evolutionary Negative Selection Algorithm (ENSA), the Flip Heuristic, the BackForwardFlipHeuristic procedure and the VerticalClimbing procedure. The Negative Selection (NS) is called twice for different purposes. One is used to make the search start in as many different areas as possible. The other is used to restrict the times of calling the BackForwardFlipHeuristic for local search. The Flip Heuristic, the BackForwardFlipHeuristic procedure and the VerticalClimbing procedure are used to enhance the local search. Experiment results show that the proposed algorithm is competitive with the GASAT that is the state-of-the-art algorithm for the large-scale SAT problems.