融合正弦余弦和无限折叠迭代混沌映射的蝴蝶优化算法

doi:10.16451/j.cnki.issn1003-6059.202007008

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摘要

针对蝴蝶优化算法求解精度不高、收敛速度较慢等问题,提出融合正弦余弦和无限折叠迭代混沌映射(ICMIC)的蝴蝶优化算法.首先,采用ICMIC映射对蝴蝶个体状态进行初始化,避免算法陷入局部最优.然后,在自身认知飞行部分引入正弦余弦算子,平衡算法的局部搜索能力与全局搜索能力.最后,通过改进依赖香味大小的幂指数调整吸收程度,获取更好的最优解.在8个基准函数上的实验表明,文中算法具有较优的全局搜索能力和求解鲁棒性,寻优精度较高,收敛速度较快.

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	王依柔
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关键词 ：正弦余弦算子, 无限折叠迭代混沌映射(ICMIC), 蝴蝶优化算法

Abstract：

To solve the problem of low precision and convergence speed of butterfly optimization algorithm, a butterfly optimization algorithm combining sine cosine and iterative chaotic map with infinite collapses(SIBOA) is proposed. Firstly, iterative chaotic map with infinite collapses is utilized to initialize the individual state of butterflies to avoid the algorithm falling into local optimum. Secondly, sine and cosine operators are introduced into the cognitive flight part to balance local and global search capability of the algorithm. Finally, the power index dependent on the size of fragrance is improved to adjust its absorption degree to obtain a better optimal solution. The results on 8 benchmark functions show that SIBOA achieves better global search capability, solution robustness and optimization accuracy with a higher convergence speed.

Key words： Sine Cosine Operator Iterative Chaotic Map with Infinite Collapses(ICMIC) Butterfly Optimization Algorithm

收稿日期: 2020-01-03

ZTFLH:

TP 301.6

基金资助:

贵州省科学技术基金项目(No.黔科合基础[2020]1Y254)资助

通讯作者: 张达敏,博士,教授,主要研究方向为认知无线电、优化计算.E-mail:1203813362@qq.com.

作者简介: 王依柔,硕士研究生,主要研究方向为认知无线电、优化计算.E-mail:2570329488@qq.com.

引用本文:

王依柔, 张达敏. 融合正弦余弦和无限折叠迭代混沌映射的蝴蝶优化算法[J]. 模式识别与人工智能, 2020, 33(7): 660-669. WANG Yirou, ZHANG Damin. Butterfly Optimization Algorithm Combining Sine Cosine and Iterative Chaotic Map with Infinite Collapses. , 2020, 33(7): 660-669.

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