| 基于自适应步长的改进蝙蝠算法 |
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| 引用本文： | 吕石磊,黄永霖,陈海强,李震,王卫星.基于自适应步长的改进蝙蝠算法[J].控制与决策,2018,33(3):557-564. |
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| 作者姓名： | 吕石磊 黄永霖 陈海强 李震 王卫星 |
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| 作者单位： | 华南农业大学电子工程学院，广州510642;广东省农情信息监测工程技术研究中心，广州510642,华南农业大学电子工程学院，广州510642,华南农业大学电子工程学院，广州510642,华南农业大学电子工程学院，广州510642;广东省农情信息监测工程技术研究中心，广州510642,华南农业大学电子工程学院，广州510642;广东省农情信息监测工程技术研究中心，广州510642 |
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| 基金项目： | 国家自然科学基金项目(61601189)；现代农业产业技术体系建设专项资金(CARS-26)；广东省科技计划项目(2015A020209161, 2016A020210088, 2016A020210093)；广州市科技计划项目(201605030013). |
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| 摘 要： | 针对基本蝙蝠算法存在容易过早陷入局部最优以及求解精度低的问题,提出一种改进的蝙蝠算法(SABA),加入自适应的步长控制机制和变异机制.通过对12个单峰/多峰函数的测试表明,与粒子群算法、蝙蝠算法相比,SABA算法能够有效解决算法陷入局部最优的问题,从而具有较高的求解精度.
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| 关 键 词： | 蝙蝠算法 自适应 步长控制机制 变异机制 |
Improved bat algorithm using self-adaptive step |
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| LV Shi-lei,HUANG Yong-lin,CHEN Hai-qiang,LI Zhen and WANG Wei-xing.Improved bat algorithm using self-adaptive step[J].Control and Decision,2018,33(3):557-564. |
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| Authors: | LV Shi-lei HUANG Yong-lin CHEN Hai-qiang LI Zhen and WANG Wei-xing |
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| Affiliation: | College of Electronic Engineering,South China Agricultural University,Guangzhou 510642,China;Guangdong Engineering Research Center for Monitoring Agricultural Information,Guangzhou 510642,China,College of Electronic Engineering,South China Agricultural University,Guangzhou 510642,China,College of Electronic Engineering,South China Agricultural University,Guangzhou 510642,China,College of Electronic Engineering,South China Agricultural University,Guangzhou 510642,China;Guangdong Engineering Research Center for Monitoring Agricultural Information,Guangzhou 510642,China and College of Electronic Engineering,South China Agricultural University,Guangzhou 510642,China;Guangdong Engineering Research Center for Monitoring Agricultural Information,Guangzhou 510642,China |
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| Abstract: | For the problems of low solution precision by the initial bat algorithm and falling into local optimum easily, an improved self-adaptive bat algorithm(SABA) is proposed, which combines the mechanisms of step-control and variation. Experiments are conducted on a set of 12 benchmark functions, and the results show that the proposed SABA has better performance than the particle swarm optimization(PSO) algorithm and initial bat algorithm(BA) in terms of accuracy and convergence speed. |
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