Cuckoo search algorithm with dimension by dimension improvement
M1 Lijin Wang, Yilong Yin, Yiwen Zhong. M2 ., Journal of Software, 24(11):2687-2698, 2013(in Chinese).
王李进,尹义龙,钟一文.逐维改进的布谷鸟搜索算法.软件学报,2013,24(11):2687-2698
逐维改进的布谷鸟搜索算法
Cuckoo Search Algorithm with Dimension by Dimension Improvement
投稿时间:2013-04-27 修订日期:2013-07-17
DOI:10.3724/SP.J.1001.2013.04476
中文关键词: 布谷鸟搜索算法 逐维改进 函数优化 多维函数 干扰现象
英文关键词:cuckoo search algorithm dimension by dimension improvement function optimization multi-dimension function interference phenomena
基金项目:新世纪优秀人才支持计划(NCET-11-0315);NSFC-广东联合基金重点支持项目(U1201258);福建省自然科学基金(2011J05044,2013J01216);山东省自然科学杰出青年基金(2013JQE27038)
作者单位E-mail
王李进 福建农林大学 计算机与信息学院, 福建 福州 350002;山东大学 计算机科学与技术学院, 山东 济南 250101
尹义龙 山东大学 计算机科学与技术学院, 山东 济南 250101 ylyin@sdu.edu.cn
钟一文 福建农林大学 计算机与信息学院, 福建 福州 350002
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中文摘要:
布谷鸟搜索(cuckoo search,简称CS)算法是一种新兴的仿生智能算法,对解采用整体更新评价策略.在求解多维函数优化问题时,由于各维之间相互干扰,采用整体更新评价策略将恶化算法的收敛速度和解的质量.为了弥补此缺陷,提出了基于逐维改进的布谷鸟搜索算法.在改进算法的迭代过程中,针对解采用逐维更新评价策略.该策略将各维的更新值与其他维的值组合成新的解,并采用贪婪方式接受能够改善解质量的更新值.实验结果说明,改进策略能够有效地提高CS 算法的收敛速度并改善解的质量.与相关的改进布谷鸟搜索算法以及其他演化算法的比较结果表明,改进算法在求解连续函数优化问题上是具有竞争力的.
英文摘要:
Cuckoo search (CS) is a new nature-inspired intelligent algorithm which uses the whole update and evaluation strategy on solutions. For solving multi-dimension function optimization problems, this strategy may deteriorate the convergence speed and the quality of solution of algorithm due to interference phenomena among dimensions. To overcome this shortage, a dimension by dimension improvement based cuckoo search algorithm is proposed. In the progress of iteration of improved algorithm, a dimension by dimension based update and evaluation strategy on solutions is used. The proposed strategy combines an updated value of one dimension with values of other dimensions into a new solution, and greedily accepts any updated values that can improve the solution. The simulation experiments show that the proposed strategy can improve the convergence speed and the quality of the solutions effectively. Meanwhile, the results also reveal the proposed algorithm is competitive for continuous function optimization problems compared with other improved cuckoo search algorithms and other evolution algorithms.
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