改进迁移算子的BBO算法及其在PID参数中的优化

doi:10.11772/j.issn.1001-9081.2019081337

计算机应用 ›› 2020, Vol. 40 ›› Issue (3): 728-734.DOI: 10.11772/j.issn.1001-9081.2019081337

改进迁移算子的BBO算法及其在PID参数中的优化

裴沛¹, 李彩伟², 吕波特³

1. 北京中油瑞飞信息技术有限责任公司, 北京 100029;
2. 国家工业信息安全发展研究中心, 北京 100040;
3. 北京博华信智科技股份有限公司, 北京 100029

收稿日期:2019-08-01 修回日期:2019-10-22 出版日期:2020-03-10 发布日期:2019-11-18
通讯作者: 李彩伟
作者简介:裴沛(1983-),男,北京人,工程师,主要研究方向:工业互联网信息化项目规划;李彩伟(1984-),女,河北石家庄人,博士,主要研究方向:工业信息化、智能制造模式、智能计算;吕波特(1993-),男,北京人,硕士,主要研究方向:群智能优化算法、软测量建模。

Improved migration operator biogeography-based optimization algorithm and its application in PID parameter tuning

PEI Pei¹, LI Caiwei², LYU Bote³

1. Beijing Richfit Information Technology Company Limited, Beijing 100029, China;
2. China Industrial Control Systems Cyber Emergency Response Team, Beijing 100040, China;
3. Beijing Bohua Xinzhi Technology Company Limited, Beijing 100029, China

Received:2019-08-01 Revised:2019-10-22 Online:2020-03-10 Published:2019-11-18

摘要/Abstract

摘要： 针对生物地理学优化（BBO）算法寻优过程中易陷入搜索动力不足、收敛精度不高等问题，提出一种基于改进迁移算子的生物地理学优化算法（IMO-BBO）。在BBO算法基础上，结合“优胜劣汰”的进化思想，将迁移距离作为影响因素对迁移算子进行改进，并用差分策略将不适宜迁移的个体进行替换，以增加算法的局部探索能力。同时为丰富物种的多样性，引入多种群概念。利用IMO-BBO算法分别对13个基准测试函数进行测试，与基于协方差迁移算子和混合差分策略的BBO （CMM-DE/BBO）算法和BBO算法相比，改进算法提高了对全局最优解的搜索能力，在收敛速度和精确度上也都有显著提高；将IMO-BBO算法应用到PID参数整定中，仿真结果表明，所提算法优化后的控制器具有更快的响应速度和更稳定的精度。

关键词: 生物地理学优化算法, 迁移算子, 迁移距离, 自适应, 双种群, 协作算子, PID

Abstract: To solve the problems of insufficient search power and low convergence accuracy in the optimization process of Biogeography-Based Optimization (BBO) algorithm, an Improved Migration Operator BBO (IMO-BBO) algorithm was proposed. On the basis of BBO algorithm and combining with the evolution thinking of “survival of the fittest”, the migration operator was improved by taking migration distance into consideration, and the differential strategy was used to replace individuals unsuitable to migration, so as to increase the local exploration ability of the algorithm. At the same time, the concept of multi-population was introduced to enrich the species diversity. IMO-BBO algorithm was tested on 13 benchmark functions. The results show that compared with the Covariance Matrix based Migration BBO hybrid with Differential Evolution (CMM-DE/BBO) algorithm and the original BBO algorithm, the improved algorithm enhances the search ability for global optimal solutions and simultaneously improves the convergence speed and the accuracy significantly. IMO-BBO was applied to PID parameter tuning, the results show that the controller optimized by this algorithm has faster response speed and more stabile accuracy.

Key words: Biogeography-Based Optimization (BBO) algorithm, migration operator, migration distance, self-adaption, dual-population, cooperative operator, PID

中图分类号:

裴沛, 李彩伟, 吕波特. 改进迁移算子的BBO算法及其在PID参数中的优化[J]. 计算机应用, 2020, 40(3): 728-734.

PEI Pei, LI Caiwei, LYU Bote. Improved migration operator biogeography-based optimization algorithm and its application in PID parameter tuning[J]. Journal of Computer Applications, 2020, 40(3): 728-734.

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改进迁移算子的BBO算法及其在PID参数中的优化

Improved migration operator biogeography-based optimization algorithm and its application in PID parameter tuning

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