计算机应用 ›› 2021, Vol. 41 ›› Issue (2): 470-478.DOI: 10.11772/j.issn.1001-9081.2020060974

所属专题: 先进计算

• 先进计算 • 上一篇    下一篇

受春秋战国史实启发的帝国竞争改进算法

王贵林, 李斌   

  1. 福建工程学院 交通运输学院, 福州 350118
  • 收稿日期:2020-07-07 修回日期:2020-09-24 出版日期:2021-02-10 发布日期:2020-12-18
  • 通讯作者: 李斌
  • 作者简介:王贵林(1985-),男,福建南平人,硕士研究生,主要研究方向:群集智能、计算机应用;李斌(1979-),男,湖北武汉人,教授,博士,CCF会员,主要研究方向:复杂服务系统的控制决策、群集智能、深度学习。
  • 基金资助:
    教育部人文社会科学研究规划基金资助项目(19YJA630031);福建省自然科学基金资助项目(2017J01496);福建工程学院科研发展基金资助项目(GY-Z160125);山东省交通科技项目(2016B35)。

Improved imperialist competitive algorithm inspired by historical facts of Spring and Autumn Period

WANG Guilin, LI Bin   

  1. School of Transportation, Fujian University of Technology, Fuzhou Fujian 350118, China
  • Received:2020-07-07 Revised:2020-09-24 Online:2021-02-10 Published:2020-12-18
  • Supported by:
    This work is partially supported by the Humanities and Social Sciences Research Programming Foundation of Ministry of Education in China (19YJA630031), the Natural Science Foundation of Fujian Province (2017J01496), the Scientific Research Development Foundation of Fujian University of Technology (GY-Z160125), the Traffic Science and Technology Project of Shandong Province (2016B35).

摘要: 针对帝国竞争算法过早收敛导致的求解高维函数时易陷入维数灾难的问题,受我国春秋战国时期诸侯国争雄称霸史实启发,提出了一种改进的帝国竞争算法。首先,在初始化国家阶段引入“合纵连横”竞争机制,以增强信息交互,保留较优种群;其次,在帝国同化过程中借鉴由国家各层面逐步渗透同化的殖民统治策略,以提升算法的开发能力;最后,加入判断并跳出局部最优的机制,避免“早熟”影响寻优性能。仿真实验中,利用8个经典标准函数验证改进算法的寻优能力、收敛速度及高维函数适用性,并对比分析三种跳出局部最优的方案;此外进行CEC2017测试函数实验,选取近年来在算法改进研究领域具有代表性的5种先进算法和所提改进算法进行比较,结果显示改进算法的寻优精度较高并且稳定性较强;而经Kendall相关系数分析可知,改进算法与原始算法在寻优性能上具有显著差异并且同化改进措施在性能提高中的贡献度最大。

关键词: 帝国竞争算法, 春秋战国, 史实, 帝国同化, 跳出局部最优

Abstract: Aiming at the problem that imperialist competitive algorithm falls into the curse of dimensionality easily when used for solving high-dimensional functions because of premature convergence, an improved imperialist competitive algorithm inspired by the historical facts of the vassal states vying for power in the Spring and Autumn Period of China was proposed. Firstly, the competition strategy of "Cooperative Confrontation" was introduced in the process of country initialization to enhance the information interaction, so as to preserve the high-quality populations. Secondly, learnt from the colonial rule strategy of gradual infiltration and assimilation at all levels of state was used in the empire assimilation process to improve the development ability of the algorithm. Finally, the mechanism of judging and jumping out of local optimum was added to avoid the impact of "premature" on the optimization performance. In the simulation experiment, through 8 classic benchmark functions, the optimization ability, convergence speed and high-dimensional function applicability of the improved algorithm were verified, and three schemes for jumping out of local optimum were compared and analyzed. Furthermore, CEC2017 test function experiment was carried out, and the comparison of the proposed improved algorithm with five representative advanced algorithms in the field of algorithm improvement in recent years showed that the improved algorithm had higher optimization accuracy and stronger stability. And the Kendall correlation coefficient verification showed the significant difference between the improved algorithm and the original algorithm on optimization performance, and that the improvement of assimilation mechanism played a key role in the performance improvement.

Key words: Imperialist Competitive Algorithm (ICA), Spring and Autumn Period, historical fact, empire assimilation, jump out of local optimum

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