| 引用本文: | 石建平,李培生,刘国平,刘鹏.双层协同进化克隆选择算法及其应用[J].哈尔滨工业大学学报,2019,51(11):174.DOI:10.11918/j.issn.0367-6234.201812176 |
| SHI Jianping,LI Peisheng,LIU Guoping,LIU Peng.Bilevel coevolutionary clonal selection algorithm and its application[J].Journal of Harbin Institute of Technology,2019,51(11):174.DOI:10.11918/j.issn.0367-6234.201812176 |
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| 摘要: |
| 为解决克隆选择算法收敛速度慢、收敛精度低等问题,提出了双层协同进化克隆选择算法,该算法的每一层使用不同的进化方案进行寻优搜索,并通过信息共享实现了层间的协同进化,形成层内竞争与层间协作的进化模式.通过构建基于多种进化策略的混合协同进化机制,实现了不同进化策略在优化过程中的优势互补与信息增值,达到有效平衡算法的全局探索与局部开发的目的,同时也较好避免了算法的早熟收敛问题.用10个标准测试函数来验证所提出算法的可行性与有效性,仿真实验结果表明:相比克隆选择算法及其两个改进的算法,本文提出的优化算法具有全局搜索能力强、稳定性好、收敛速度快、收敛精度高等优势,且测试函数维度的增加对本文算法的收敛性能影响不大,其优势更加凸显.针对混沌系统控制与同步中的系统参数估计问题,以Lorenz混沌系统的参数估计为例,进行了未知参数估计的数值仿真,结果显示本文算法实现了混沌系统参数的高精度估计,是一种有效的混沌系统参数估计方法. |
| 关键词: 克隆选择算法 协同进化 多策略 混沌系统 参数估计 |
| DOI：10.11918/j.issn.0367-6234.201812176 |
| 分类号:TP301.6 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(51566012)；贵州省联合基金资助项目(黔科合LH字[2015]7302号) |
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| Bilevel coevolutionary clonal selection algorithm and its application |
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SHI Jianping1,2,LI Peisheng1,LIU Guoping1,LIU Peng3
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(1.School of Mechanical & Electrical Engineering, Nanchang University, Nanchang 330031,China; 2.School of Electronic & Communication Engineering, Guiyang University, Guiyang 550005, China; 3.School of Gems and Materials Technology, Hebei GEO University, Shijiazhuang 050031, China）
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| Abstract: |
| In order to solve the problem of slow convergence speed and low convergence precision inherent in the clone selection algorithm, a bilevel coevolutionary clonal selection algorithm is proposed. The algorithm used different evolutionary schemes for each level of evolution to search for optimization. Through information sharing, co-evolution between levels was realized. Moreover, an evolutionary model of intra-level competition and inter-level cooperation was formed. By constructing a hybrid co-evolution mechanism of multi-evolutionary strategies, it could realize the complementary advantages and information increment of different evolutionary strategies in the optimization process. Thus, the purpose of effectively balancing the global exploration and local exploitation of the algorithm was achieved. Simultaneously, the premature convergence problem of the algorithm could also be better avoided. The feasibility and effectiveness of the proposed algorithm were verified by 10 benchmarks. Experimental results show that the proposed algorithm had obvious advantages such as stronger global search ability, better stability, faster convergence speed, higher convergence accuracy, and so on. Furthermore, these advantages became more prominent with the increase of testing dimensions. Lorenz chaotic system was taken as an example to test the algorithm in estimating the parameters. Simulation results confirmed that the proposed algorithm can be used for high-precision estimation of system parameters, and it is an effective parameter estimation method for chaotic systems. |
| Key words: clonal selection algorithm co-evolution multi-strategy chaotic system parameter estimation |
