计算机科学 ›› 2012, Vol. 39 ›› Issue (10): 187-189.

• 人工智能 • 上一篇    下一篇

基于反馈机制的克隆反馈优化算法的稳定性研究

舒万能,丁立新,汪慎文   

  1. (武汉大学计算机学院 武汉430072) (中南民族大学计算机科学学院 武汉430074) (武汉大学软件工程国家重点实验室 武汉430072)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Study on Stability of Clonal Feedback Optimization Algorithm Based on Feedback Mechanism

  • Online:2018-11-16 Published:2018-11-16

摘要: 克隆选择算法是一种基于克隆选择原理的进化优化算法,但是它因受抗体浓度的影响而稳定性较差。在传统的克隆选择算法的基础上,充分考虑抗体的浓度和种群多样性两方面因素,提出了一种新的基于反馈机制的克隆反馈优化算法。该算法融入了一种进化反馈深度模型和种群生存度设计理念,有效提高了算法的稳定性。最后,将该算法应用到网格计算独立任务调度中,取得了较理想的实验结果。

关键词: 免疫系统,克隆选择算法,反馈机制,克隆反馈优化算法,网格计算

Abstract: The clonal selection algorithm is an evolutionary optimization algorithm on the basis of clonal selection theory of the immune system, but it is subject to antibody concentration, leading less stableness. Based on the traditional clonal selection algorithm and consideration of the antibody concentration and diversity of the population, we proposed a novel clonal feedback optimization algorithm. The proposed algorithm is integrated into an evolution feedback depth model and population survivability idea, to effectively improve the stability of the algorithm. Finally, the proposed algorithm is applied to the task scheduling in grid computing,and achieves satisfactory results.

Key words: Immune system, Clonal selection algorithm, Feedback mechanism, Clonal feedback optimization algorithm,Grid computing

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