计算机应用 ›› 2016, Vol. 36 ›› Issue (6): 1583-1587.DOI: 10.11772/j.issn.1001-9081.2016.06.1583

• 人工智能 • 上一篇    下一篇

正态变异优胜劣汰的混合蛙跳算法

张明明, 戴月明, 吴定会   

  1. 江南大学 物联网工程学院, 江苏 无锡 214122
  • 收稿日期:2015-11-24 修回日期:2016-01-19 出版日期:2016-06-10 发布日期:2016-06-08
  • 通讯作者: 张明明
  • 作者简介:张明明(1991-),女,江苏沛县人,硕士研究生,主要研究方向:人工智能、软件测试;戴月明(1964-),男,江苏常熟人,副教授,硕士,主要研究方向:软件测试、人工智能、模式识别;吴定会(1970-),男,安徽庐江人,副教授,博士,主要研究方向:新能源控制。
  • 基金资助:
    国家863计划项目(2013AA040405);江苏省产学研联合创新基金资助项目(BY2012055)。

Novel survival of the fittest shuffled frog leaping algorithm with normal mutation

ZHANG Mingming, DAI Yueming, WU Dinghui   

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China
  • Received:2015-11-24 Revised:2016-01-19 Online:2016-06-10 Published:2016-06-08
  • Supported by:
    This work is partially supported by the National High Technology Research and Development Program (863 Program) of China (2013AA040405), and the Production-Study-Research Joint Innovation Foundation of Jiangsu Province (BY2012055).

摘要: 针对基本混合蛙跳算法收敛速度慢、求解精度不高且易陷入局部最优的缺陷,提出了一种新的正态变异优胜劣汰的混合蛙跳算法。该算法在局部搜索策略中,对子群内最差个体的更新融入了服从正态分布的变异扰动,可有效避免青蛙个体向局部最优聚集,扩大搜索空间,增加种群的多样性;同时对子群内少量的较差青蛙进行变异选择,摒弃不利的变异,继承有用的变异,优胜劣汰,整体提高种群的质量,减少算法寻优过程的盲目性,提高算法的寻优速度。对每个子群内的最优个体引入精英变异机制以获得更优秀的个体,进一步提升算法的全局寻优能力,避免陷入局部最优,引领种群向更好的方向进化。实验独立运行30次,所提算法在Sphere、Rastrigrin、Griewank、Ackley和Quadric函数中均能收敛到最优解0,优于其他对比算法。实验结果表明,所提算法可有效避免算法陷入早熟收敛,提高了算法的收敛速度和精度。

关键词: 混合蛙跳算法, 正态变异, 优胜劣汰, 精英变异机制, 种群多样性

Abstract: To overcome the demerits of basic Shuffled Frog Leaping Algorithm (SFLA), such as slow convergence speed, low optimization precision and falling into local optimum easily, a novel survival of the fittest SFLA with normal mutation was proposed. In the local search strategy of the proposed algorithm, the normal mutations for updating strategy of the worst frog individuals in the subgroup were introduced to avoid the algorithm falling into local convergence effectively, expand the searching space and increase the diversity of population. Meanwhile, the mutations were selected for a small number of worse frog individual in the subgroup to inherit the useful mutations instead of the bad mutations. The survival of the fittest was implemented, the quality of the population was improved, the blindness of the algorithm optimization process was reduced and the algorithm optimization was speeded up. The elite mutation mechanism for the best frog individuals in each subgroup was introduced for obtaining better individuals to enhance the global optimization ability of the algorithm further, avoid falling into local convergence, and lead the whole population evolution to the better. The experimental results of 30 independent runs indicate that the proposed algorithm can converge to the optimal solution of 0 in Sphere, Rastrigrin, Griewank, Ackley and Quadric, which is better than the other contrastive algorithms. The experimental results show that the proposed algorithm can avoid falling into premature convergence effectively, improve the convergence speed and convergence precision.

Key words: Shuffled Frog Leaping Algorithm (SFLA), normal mutation, survival of the fittest, elite mutation mechanism, population diversity

中图分类号: