Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (6): 1588-1593.DOI: 10.11772/j.issn.1001-9081.2016.06.1588

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Improved self-organized criticality optimized gray wolf optimizer metaheuristic algorithm

XU Dayu1,2, LIU Renping2   

  1. 1. Zhejiang Provincial Key Laboratory of Intelligent Monitoring in Forestry and Information Technology(Zhejiang A & F University), Hangzhou Zhejiang 311300, China;
    2. Computer and Communication Technologies Centre, Commonwealth Scientific and Industrial Research Organization, Sydney 2122, Australia
  • Received:2015-10-19 Revised:2016-01-28 Online:2016-06-10 Published:2016-06-08
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (71131002), the Talents Startup Project of Zhejiang A&F University (2014FR082).


徐达宇1,2, LIU Renping2   

  1. 1. 浙江省林业智能监测与信息技术研究重点实验室(浙江农林大学), 杭州 311300;
    2. 澳大利亚联邦科学与工业研究组织 计算机与通信技术研究中心, 悉尼 2122
  • 通讯作者: 徐达宇
  • 作者简介:徐达宇(1985-),男,浙江杭州人,讲师,博士,主要研究方向:云计算、数据挖掘;LIU Renping(1963-),男,澳大利亚人,教授,博士,主要研究方向:无线网络、感知网络、网络安全。
  • 基金资助:

Abstract: Focusing on the issue that the novel metaheuristic optimization algorithm-Gray Wolf Optimizer (GWO) is easy to fall into local optimum when it is searching for the global optimal solution, thereby its ability was enhanced to obtain the global optimal solution. The fundamental principles and modeling processes of GWO were introduced firstly. On this basis, combined with the advantages of self-organized criticality theory, the Improved Extremes Optimization (IEO) algorithm was proposed. Then the IEO was integrated into the GWO model to construct the Self-Organized Critical (SOC) optimization algorithm named IEO-GWO. By adopting 23 benchmark test functions to implement a comprehensive comparison with traditional optimization algorithms in optimization performance, the superior ability of IEO-GWO model in searching global optimal values was verified.

Key words: metaheuristic algorithm, Grey Wolf Optimizer (GWO) algorithm, Self-Organized Criticality (SOC), global optimization

摘要: 针对新型元启发式算法灰狼优化(GWO)算法在寻优过程中易陷入局部最优这一问题,提升该算法获取全局最优解的能力。介绍了该算法的基本原理和建模过程,并在此基础上,结合自组织临界性理论的优点,提出了改进的极值优化(IEO)算法,将IEO融入到GWO模型中,构建基于自组织临界(SOC)优化的改进GWO算法(IEO-GWO)。通过与传统优化算法对于23个基准测试函数在寻优性能上的综合比较,验证了IEO-GWO模型在获取全局最优解性能上的优越性。

关键词: 元启发式算法, 灰狼优化算法, 自组织临界, 全局最优化

CLC Number: