作　　者： (王齐）; (陈娟）; (李全善）; (刘继超）;

机构地区： 北京化工大学信息科学与技术学院,北京100029

出　　处： 《南京理工大学学报》 2017年第4期519-525,共7页

摘　　要： 为了解决生物地理学优化(Biogeography-based optimization,BBO)算法在收敛过程容易陷入早熟的问题,提出了一种改进的生物地理学优化(Improved biogeography-based optimization,I-BBO)算法。该算法是在生物地理学的基础上,引入了人工驯养的概念,把人工驯化与生物地理学算法相结合,解决了BBO算法在后期存在搜索动力不足的问题。仿真实验表明:I-BBO算法提高了物种的多样性,增强了算法的搜索能力,加快了寻优速度。将该文提出的I-BBO算法应用到比例积分微分(Proportion integration differentiation,PID)控制器的参数整定中,通过两个例子的仿真,结果表明,I-BBO算法在优化PID控制器参数上比原有的BBO算法更加迅速。 An improved biogeography-based optimization（ I-BBO） algorithm is presented in order to solve the problem of biogeography-based optimization（ BBO） algorithm, which has the phenomenon of easily precocious convergence in the process of optimization. Based on the biogeography, the algorithm introduces the concept of artificial domestication and the integrating artificial domestication in biogeography algorithm to solve the problem of the less ability to explore in the end of the process. Simulation experiments prove that the I-BBO algorithm improves the species diversity and enhancesthe search abi lity of the algorithm. The I-BBO algorithm is applied to set the proportion - integration- differentiation （ PID ） controller parameters. The simulation results of the two examples show that the I - BBO algorithm is more rapid than the BBO algorithm in the PID controller parameters optimization.

关 键 词： 人工驯养 生物地理学 优化算法 参数优化