| 基于模糊控制的权重决策灰狼优化算法 |
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| 引用本文： | 邢燕祯,王东辉.基于模糊控制的权重决策灰狼优化算法[J].计算机系统应用,2018,27(10):202-208. |
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| 作者姓名： | 邢燕祯 王东辉 |
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| 作者单位： | 中国科学院 声学研究所, 北京 100190;中国科学院 声学研究所 中国科学院水下航行器信息技术重点实验室, 北京 100190;中国科学院大学, 北京 100049,中国科学院 声学研究所 中国科学院水下航行器信息技术重点实验室, 北京 100190;中国科学院大学, 北京 100049 |
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| 基金项目： | 国家自然科学基金（61274025）；中国科学院声学研究所青年英才计划项目（QNYC201622） |
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| 摘 要： | 针对灰狼优化算法后期收敛速度慢，求解精度低等问题，提出一种基于模糊控制的权重决策灰狼优化算法.首先，提出一种新的非线性收敛因子，以提高算法的全局搜索能力及收敛速度；其次，提出一种基于模糊控制的权重决策策略，通过模糊控制器对决策层的个体赋予不同权重进行种群位置更新的决策，增强算法的寻优能力.选取23个标准测试函数对该算法及对比算法进行数值实验，实验结果表明，本文提出的改进的灰狼优化算法在求解精度和算法稳定性等指标优于对比算法.
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| 关 键 词： | 灰狼优化算法 (GWO) 模糊控制 启发算法 权重因子 群体优化算法 |
| 收稿时间： | 2018/2/26 0:00:00 |
| 修稿时间： | 2018/3/19 0:00:00 |
Hybrid Grey Wolf Optimizer Algorithm with Fuzzy Weight Strategy |
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| XING Yan-Zhen and WANG Dong-Hui.Hybrid Grey Wolf Optimizer Algorithm with Fuzzy Weight Strategy[J].Computer Systems& Applications,2018,27(10):202-208. |
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| Authors: | XING Yan-Zhen and WANG Dong-Hui |
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| Affiliation: | Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China;CAS Key laboratory of Technology for Autonomous Underwater Vehicles, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China;University of Chinese Academy of Sciences, Beijing 100049, China and CAS Key laboratory of Technology for Autonomous Underwater Vehicles, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China;University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | To solve the problem of slow convergence speed before reaching the global optimum and low precision of optimization in Grey Wolf Optimizor (GWO), a hybrid GWO algorithm based on fuzzy weight strategy is proposed. By replacing the linear convergence factor in original algorithm with a new non-linear convergence factor, global search ability is improved. Furthermore, the algorithm employs a fuzzy weight strategy to offer discrepant weight to agents who are responsible for the decision, which will enhance the optimizing ability therefore. Numberical experiments are conducted in 23 standard test functions. Experimental results show that the proposed FWGWO algorithm has better performance compared with other algorithms. |
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| Keywords: | Grey Wolf Optimizor (GWO) fuzzy control heuristic algorithm weight factor population optimization algorithm |
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