A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction

IF 2.7 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shaoqiang Ye, Kaiqing Zhou, Azlan Mohd Zain, Fangling Wang, Yusliza Yusoff
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引用次数: 0

Abstract

Harmony search (HS) is a form of stochastic meta-heuristic inspired by the improvisation process of musicians. In this study, a modified HS with a hybrid cuckoo search (CS) operator, HS-CS, is proposed to enhance global search ability while avoiding falling into local optima. First, the randomness of the HS pitch disturbance adjusting method is analyzed to generate an adaptive inertia weight according to the quality of solutions in the harmony memory and to reconstruct the fine-tuning bandwidth optimization. This is to improve the efficiency and accuracy of HS algorithm optimization. Second, the CS operator is introduced to expand the scope of the solution space and improve the density of the population, which can quickly jump out of the local optimum in the randomly generated harmony and update stage. Finally, a dynamic parameter adjustment mechanism is set to improve the efficiency of optimization. Three theorems are proved to reveal HS-CS as a global convergence meta-heuristic algorithm. In addition, 12 benchmark functions are selected for the optimization solution to verify the performance of HS-CS. The analysis shows that HS-CS is significantly better than other algorithms in optimizing high-dimensional problems with strong robustness, high convergence speed, and high convergence accuracy. For further verification, HS-CS is used to optimize the back propagation neural network (BPNN) to extract weighted fuzzy production rules. Simulation results show that the BPNN optimized by HS-CS can obtain higher classification accuracy of weighted fuzzy production rules. Therefore, the proposed HS-CS is proved to be effective.

改进的和谐搜索算法及其在加权模糊生产规则提取中的应用
和谐搜索(HS)是一种随机元启发式,其灵感来自音乐家的即兴创作过程。本研究提出了一种带有混合布谷鸟搜索(CS)算子 HS-CS 的改进 HS,以增强全局搜索能力,同时避免陷入局部最优。首先,分析了 HS 音高干扰调整方法的随机性,根据和声记忆中解的质量生成自适应惯性权重,并重建微调带宽优化。从而提高 HS 算法优化的效率和精度。其次,引入 CS 算子,扩大解空间范围,提高种群密度,在随机生成的和谐与更新阶段,能快速跳出局部最优。最后,设置了动态参数调整机制,以提高优化效率。三个定理揭示了 HS-CS 是一种全局收敛元启发式算法。此外,还选取了 12 个基准函数作为优化解,以验证 HS-CS 的性能。分析表明,在优化高维问题时,HS-CS 的鲁棒性强、收敛速度快、收敛精度高,明显优于其他算法。为了进一步验证,HS-CS 被用于优化反向传播神经网络(BPNN)以提取加权模糊生产规则。仿真结果表明,经 HS-CS 优化的 BPNN 可以获得更高的加权模糊生产规则分类精度。因此,所提出的 HS-CS 被证明是有效的。
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来源期刊
Frontiers of Information Technology & Electronic Engineering
Frontiers of Information Technology & Electronic Engineering COMPUTER SCIENCE, INFORMATION SYSTEMSCOMPU-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
6.00
自引率
10.00%
发文量
1372
期刊介绍: Frontiers of Information Technology & Electronic Engineering (ISSN 2095-9184, monthly), formerly known as Journal of Zhejiang University SCIENCE C (Computers & Electronics) (2010-2014), is an international peer-reviewed journal launched by Chinese Academy of Engineering (CAE) and Zhejiang University, co-published by Springer & Zhejiang University Press. FITEE is aimed to publish the latest implementation of applications, principles, and algorithms in the broad area of Electrical and Electronic Engineering, including but not limited to Computer Science, Information Sciences, Control, Automation, Telecommunications. There are different types of articles for your choice, including research articles, review articles, science letters, perspective, new technical notes and methods, etc.
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