A quantum genetic simulated annealing algorithm for task scheduling

  • Authors:

  • Wanneng Shu;Bingjiao He

  • Affiliations:

  • College of Computer Science, South-Central University for Nationalities, Wuhan;College of Computer Science, South-Central University for Nationalities, Wuhan

  • Venue:
  • ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
  • Year:
  • 2007

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Abstract

Based on quantum computing, a Quantum Genetic Simulated Annealing Algorithm (QGSAA) is proposed. With the condition of preserving Quantum Genetic Algorithm (QGA) advantages, QGSAA takes advantage of simulated annealing algorithm so as to avoid premature convergence. When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole. Comparing experiments have been conducted on task scheduling in grid computing. Experimental results have shown that QGSAA is superior to QGA and Genetic Algorithm (GA) on performance.