计算机工程与应用 ›› 2013, Vol. 49 ›› Issue (11): 50-53.

• 理论研究、研发设计 • 上一篇    下一篇

种群自适应调整的克隆多峰函数优化

裴  芳1,2,张  洁1,2,唐  俊3   

  1. 1.湖南机电职业技术学院 信息工程系,长沙 410151
    2.华中科技大学 计算机科学系,武汉 430074
    3.同济大学 软件学院,上海 230021
  • 出版日期:2013-06-01 发布日期:2013-06-14

Multi-model function optimization based on clonal optimization with self-adaptive population size

PEI Fang1,2, ZHANG Jie1,2, TANG Jun3   

  1. 1.Department of Information Engineering, Hunan Mechanical & Electrical Polytechnic, Changsha 410151, China
    2.Department of Computer Science, Huazhong University of Science and Technology, Wuhan 430074, China
    3.School of Software, Tongji University, Shanghai 230021, China
  • Online:2013-06-01 Published:2013-06-14

摘要: 为了尽可能求得多峰函数的最优解,提出了一种种群规模自适应调整的克隆算法。实现了种群规模根据进化过程自适应的变化,平衡了种群规模对算法效率的影响。此外,结合多峰函数优化的特点,为了增强算法搜索最优解的能力,采用Larmack学习策略作为局部搜索机制。实验结果表明,该算法求解效果较好。

关键词: 克隆优化, 多峰函数, 种群规模, 局部搜索

Abstract: In order to get the best solutions of the multi-model function, an immune clonal algorithm with self-adaptive population size is proposed. The self-adaptive population size changes with the evolutionary process are achieved to balance the impact of population size on the efficiency of the algorithm. In addition, in terms of multi-modal function optimization characteristics, Larmark learning is used as a local search strategy to enhance the search ability for the optimal solution. The experimental results show that the algorithm has better performances.

Key words: clone optimization, multi-model function, population size, local search