具有自学习能力的变异蝙蝠优化算法及性能仿真

doi:10.16182/j.issn1004731x.joss.201702009

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (2): 301-308.doi: 10.16182/j.issn1004731x.joss.201702009

具有自学习能力的变异蝙蝠优化算法及性能仿真

尚俊娜¹, 刘春菊^1*, 岳克强², 李林¹

1.杭州电子科技大学通信工程学院,浙江杭州 310018;
2.杭州电子科技大学电子信息学院,浙江杭州 310018

收稿日期:2015-05-07 修回日期:2015-10-08 出版日期:2017-02-08 发布日期:2020-06-01
作者简介:尚俊娜(1979-),女,河南开封,博士,副教授,研究方向为通信信号处理、智能算法。
基金资助:
浙江省自然科学基金青年基金(LQ13F 010010), 浙江省重点科技创新团队 (2013TD03)

Variation Bat Algorithm with Self-learning Capability and Its Property Analysis

Shang Junna¹, Liu Chunju^1*, Yue Keqiang², Li Lin¹

1. College of Telecommunication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China;
2. College of Electrical and Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China

Received:2015-05-07 Revised:2015-10-08 Online:2017-02-08 Published:2020-06-01

摘要/Abstract

摘要： 针对标准蝙蝠算法进化特点,提出了具有自学习能力和个体变异的蝙蝠优化算法,该算法中全局最优个体具有自学习能力,使全局最优解在小范围内进行自我优化,能够引导算法中个体进行深度搜索;同时算法中每个个体动态的成比例形成变异群,依据贪婪选择机制,保护了优良个体,避免个体退化。通过以上算子的融入来提高算法的优化精度和收敛速度,避免算法早熟。通过对基本标准函数的测试,验证了算法具有寻优能力强,搜索精度高的优点,能有效的跳出局部最优,一定程度上弥补了算法高维弱势的缺陷,对于工程中复杂的优化函数有较大的使用价值。

关键词: 蝙蝠算法, 自学习策略, 变异选择, 寻优精度, 多维函数优化

Abstract: Regarding to the evolution characteristics of standard bat algorithm, a bat algorithm with the capability of self-learning and individual variation was proposed. In this proposed algorithm, the best global individual with the self-learning capability could self-optimize within a small range of solutions and lead to other individuals develop deep searching. In addition, the each individual generated a dynamic number variation cluster in proportion its fitness value. According to the rule of greedy selection, the best individual in the variation cluster was selected which protected the excellent individual and avoided the individual degradation. The proposed algorithm made use of the self-learning and individual variation improved the optimization accuracy and convergence speed. The simulation results for the standard test functions show that the improved bat algorithm has significant advantage of high optimization ability and search precision, and can skip from local optimum effectively. The improved bat algorithm has great value to engineering of complex function optimization.

Key words: bat algorithm, self-learning, variation selection, optimization accuracy, multi-dimensional function optimization

中图分类号:

TP301.6

尚俊娜, 刘春菊, 岳克强, 李林. 具有自学习能力的变异蝙蝠优化算法及性能仿真[J]. 系统仿真学报, 2017, 29(2): 301-308.

Shang Junna, Liu Chunju, Yue Keqiang, Li Lin. Variation Bat Algorithm with Self-learning Capability and Its Property Analysis[J]. Journal of System Simulation, 2017, 29(2): 301-308.

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具有自学习能力的变异蝙蝠优化算法及性能仿真

Variation Bat Algorithm with Self-learning Capability and Its Property Analysis

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