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带邻域搜索的动态惯性权重二进制蝙蝠算法

Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search.

作者信息

Huang Xingwang, Zeng Xuewen, Han Rui

机构信息

National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.

University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

Comput Intell Neurosci. 2017;2017:3235720. doi: 10.1155/2017/3235720. Epub 2017 May 28.

DOI:10.1155/2017/3235720
PMID:28634487
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5467396/
Abstract

Binary bat algorithm (BBA) is a binary version of the bat algorithm (BA). It has been proven that BBA is competitive compared to other binary heuristic algorithms. Since the update processes of velocity in the algorithm are consistent with BA, in some cases, this algorithm also faces the premature convergence problem. This paper proposes an improved binary bat algorithm (IBBA) to solve this problem. To evaluate the performance of IBBA, standard benchmark functions and zero-one knapsack problems have been employed. The numeric results obtained by benchmark functions experiment prove that the proposed approach greatly outperforms the original BBA and binary particle swarm optimization (BPSO). Compared with several other heuristic algorithms on zero-one knapsack problems, it also verifies that the proposed algorithm is more able to avoid local minima.

摘要

二进制蝙蝠算法（BBA）是蝙蝠算法（BA）的二进制版本。已经证明，与其他二进制启发式算法相比，BBA具有竞争力。由于该算法中速度的更新过程与BA一致，在某些情况下，该算法也面临早熟收敛问题。本文提出一种改进的二进制蝙蝠算法（IBBA）来解决此问题。为了评估IBBA的性能，采用了标准基准函数和0-1背包问题。基准函数实验获得的数值结果证明，所提出的方法大大优于原始的BBA和二进制粒子群优化算法（BPSO）。在0-1背包问题上与其他几种启发式算法相比，也验证了所提出的算法更能避免局部最小值。

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Contrast Media Mol Imaging. 2022 Feb 28;2022:5143757. doi: 10.1155/2022/5143757. eCollection 2022.