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Int. J. Bio Inspired Comput.最新文献

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Alligator optimisation algorithm for solving unconstrainted optimisation problems 求解无约束优化问题的短吻鳄优化算法
Pub Date : 2023-01-01 DOI: 10.1504/ijbic.2022.10049859
Weng-Hooi Tan, J. Mohamad-Saleh
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引用次数: 0
Coke price prediction approach based on dense GRU and opposition-based learning salp swarm algorithm 基于密集GRU和基于对立学习的salp群算法的焦炭价格预测方法
Pub Date : 2023-01-01 DOI: 10.1504/ijbic.2022.10047653
Xuhui Zhu, Pingfan Xia, Qizhi He, Zhiwei Ni, Liping Ni
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引用次数: 8
An empirical study of improved ant colony clustering algorithm in English composition review 改进蚁群聚类算法在英语作文复习中的实证研究
Pub Date : 2023-01-01 DOI: 10.1504/ijbic.2023.10055786
Xia Chang, Jianguang Sun
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引用次数: 0
Multi-modal MRI image fusion of the brain based on joint bilateral filter and non-subsampled shearlet transform 基于联合双侧滤波和非下采样剪切波变换的脑MRI多模态图像融合
Pub Date : 2023-01-01 DOI: 10.1504/ijbic.2023.10054703
Changhan Meng, Mengxing Huang, Yuchun Li, Yu Zhang, Siling Feng, Yuanyuan Wu
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引用次数: 0
A novel binary multi-swarms fruit fly optimisation algorithm for the 0-1 multidimensional knapsack problem 一种新的二元多群果蝇0-1多维背包问题优化算法
Pub Date : 2023-01-01 DOI: 10.1504/ijbic.2021.10047361
Xin Du, Jiawei Zhou, Youcong Ni, Wentao Liu, Ruliang Xiao, Xiuli Wu
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引用次数: 1
Research on emergence mechanism of collective intelligence from the complexity perspective 复杂性视角下的集体智慧产生机制研究
Pub Date : 2023-01-01 DOI: 10.1504/ijbic.2023.10054716
R. Xiao, Zhenhui Feng, Bowen Wu
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引用次数: 0
Chaotic resonance in discrete fractional-order LIF neural network motifs 离散分数阶LIF神经网络基元的混沌共振
Pub Date : 2023-01-01 DOI: 10.1504/ijbic.2023.10054455
Hao Yin, Jiacheng Tang, Bo Liu, Shuaiyu Yao, Qi Kang
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引用次数: 0
A novel rough semi-supervised k-means algorithm for text clustering 一种新的粗糙半监督k-means文本聚类算法
Pub Date : 2023-01-01 DOI: 10.1504/ijbic.2023.130548
Lei Tang, Zhen Wang, Shu dong Wang, Jian-cong Fan, G. Yue
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引用次数: 0
On the effect of particle update modes in particle swarm optimisation 粒子群优化中粒子更新模式的影响
Pub Date : 2023-01-01 DOI: 10.1504/ijbic.2023.132784
Nan-jiang Dong, Rui Wang, Zhang Tao, Junwei Ou
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引用次数: 0
An algorithm of finding rules for a class of cellular automata 一类元胞自动机的规则查找算法
Pub Date : 2022-09-26 DOI: 10.1504/IJBIC.2022.10050857
Lei Kou, Fangfang Zhang, Luobing Chen, Wende Ke, Quande Yuan, Junhe Wan, Zhen Wang
Cellular automata (CA) is an important modelling paradigm for complex systems. In the design of cellular automata, the most difficult task is to find the transformation rules that describe the temporal evolution or pattern of a modelled system. A CA with weights(CAW) yields transition rules algorithm is proposed in this paper, which have ample physical meanings and extend the category of CA. Firstly, the weights are increased to connect the updated cell and its neighbours, and the output of each cell depends on the states of cells in the neighbourhood and their respective weights. Secondly, the error correction algorithm is adopted to find correct transition rules by adjusting weights. When the error is zero, the required transition rules with correct weights will be found to describe the fixed configuration. The CAW with the correct rules will relax to the fixed configuration regardless of the initial states. Finally, the mathematical analysis and simulation are carried out with one-dimensional CAW, and the results show that the proposed algorithm has the ability to find correct transition rules as the error converges exponentially.
元胞自动机(CA)是一种重要的复杂系统建模范式。在元胞自动机的设计中,最困难的任务是找到描述被建模系统的时间演化或模式的转换规则。本文提出了一种带有权重的CA (CAW)生成转换规则算法,该算法具有丰富的物理意义,扩展了CA的范畴。首先,增加权重以连接更新后的单元格与其相邻单元格,每个单元格的输出取决于相邻单元格的状态和各自的权重。其次,采用纠错算法,通过调整权重找到正确的过渡规则;当误差为零时,将找到具有正确权重的所需转换规则来描述固定配置。无论初始状态如何,具有正确规则的CAW都将放松到固定配置。最后,利用一维CAW进行了数学分析和仿真,结果表明,该算法在误差呈指数收敛的情况下能够找到正确的过渡规则。
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引用次数: 0
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Int. J. Bio Inspired Comput.
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