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

Shaoqiang YE; Kaiqing ZHOU; Azlan Mohd ZAIN; Fangling WANG; Yusliza YUSOFF

doi:10.1631/FITEE.2200334

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

Regular Papers | Updated：2023-12-06

- A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction
  Enhanced Publication
- 一种改进的和声搜索算法及其在权重模糊产生式规则获取中的应用
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 11, Pages: 1574-1590(2023)
- Affiliations：
  
  1.School of Communication and Electronic Engineering, Jishou University, Jishou 416000, China
  2.Faculty of Computing, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia
- Author bio：
  
  E-mail: shaoq_ye@163.com
  ‡Corresponding author
  azlanmz@utm.my
  fanglingong@163.com
  yusliza@utm.my
- Funds：
- DOI：10.1631/FITEE.2200334
  CLC： TP181
- Published：0 November 2023，
  
  Received：03 August 2022，
  
  Accepted：2023-01-08
- Accepted：
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SHAOQIANG YE, KAIQING ZHOU, AZLAN MOHD ZAIN, et al. A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction. [J]. Frontiers of information technology & electronic engineering, 2023, 24(11): 1574-1590.
DOI：

SHAOQIANG YE, KAIQING ZHOU, AZLAN MOHD ZAIN, et al. A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction. [J]. Frontiers of information technology & electronic engineering, 2023, 24(11): 1574-1590. DOI： 10.1631/FITEE.2200334.

摘要

和声搜索算法（harmony search，HS）是一种随机元启发式算法，其灵感来自于音乐家的即兴创作过程。针对HS在求解中易陷入局部极值等不足，本文提出一种混合布谷鸟算子的改进的和声布谷鸟搜索算法（modified HS with a hybrid cuckoo search（CS）operator，HS-CS）增强全局搜索能力。该算法首先对HS音高扰动调整方法的随机性进行分析，根据和声库中解的质量生成自适应惯性权重，并重构微调带宽寻优，提升HS的寻优效率及精度。其次，引入CS算子扩大解空间的搜索范围和提高种群密度，从而能够在随机生成和声和更新阶段快速跳出局部极值。最后，构建动态参数调整机制以提高算法寻优的效率。通过证明3个定理揭示HS-CS是一种全局收敛的元启发式算法。在实验部分，选取12种经典的测试函数优化求解以验证HS-CS算法的性能。数值分析结果表明，HS-CS在处理高维函数优化问题上显著优于其他算法，表现出较强鲁棒性、高收敛速度以及收敛精度。为进一步验证算法在实际问题求解中的有效性，将HS-CS用于优化BP神经网络进行加权模糊产生式的规则抽取。仿真实验结果表明，HS-CS优化后的BP神经网络能够获得较高的规则分类精度。因此，从理论和应用方面都证明了HS-CS是行之有效的。

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.

关键词

和声搜索算法布谷鸟搜索算法全局收敛函数优化权重模糊产生式规则抽取

Keywords

Harmony search algorithmCuckoo search algorithmGlobal convergenceFunction optimizationWeighted fuzzy production rule extraction

references

Abbasi M, Abbasi E, Mohammadi-Ivatloo B, 2021. Single and multi-objective optimal power flow using a new differential-based harmony search algorithm. J Amb Intell Human Comput, 12(1):851-871. https://doi.org/10.1007/s12652-020-02089-6https://doi.org/10.1007/s12652-020-02089-6

Al-Shaikh A, Mahafzah BA, Alshraideh M, 2023. Hybrid harmony search algorithm for social network contact tracing of COVID-19. Soft Comput, 27:3343-3365. https://doi.org/10.1007/s00500-021-05948-2https://doi.org/10.1007/s00500-021-05948-2

Costa A, Fernandez-Viagas V, 2022. A modified harmony search for the T-single machine scheduling problem with variable and flexible maintenance. Expert Syst Appl, 198:116897. https://doi.org/10.1016/j.eswa.2022.116897https://doi.org/10.1016/j.eswa.2022.116897

Geem ZW, Kim JH, Loganathan GV, 2001. A new heuristic optimization algorithm: harmony search. Simulation, 76(2):60-68. https://doi.org/10.1177/003754970107600201https://doi.org/10.1177/003754970107600201

Gong JH, Zhang ZQ, Liu JQ, et al., 2021. Hybrid algorithm of harmony search for dynamic parallel row ordering problem. J Manuf Syst, 58:159-175. https://doi.org/10.1016/j.jmsy.2020.11.014https://doi.org/10.1016/j.jmsy.2020.11.014

Gupta S, 2022. Enhanced harmony search algorithm with non-linear control parameters for global optimization and engineering design problems. Eng Comput, 38(4):‍‍3539-3562. https://doi.org/10.1007/s00366-021-01467-8https://doi.org/10.1007/s00366-021-01467-8

Jagatheesan K, Anand B, Samanta S, et al., 2019. Design of a proportional-integral-derivative controller for an automatic generation control of multi-area power thermal systems using firefly algorithm. IEEE/CAA J Autom Sin, 6(2):503-515. https://doi.org/10.1109/JAS.2017.7510436https://doi.org/10.1109/JAS.2017.7510436

Kamoona AM, Patra JC, 2019. A novel enhanced cuckoo search algorithm for contrast enhancement of gray scale images. Appl Soft Comput, 85:105749. https://doi.‍org/10.1016/j.asoc.2019.105749https://doi.‍org/10.1016/j.asoc.2019.105749

Karaboga D, Gorkemli B, Ozturk C, et al., 2014. A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev, 42(1):21-57. https://doi.org/10.1007/s10462-012-9328-0https://doi.org/10.1007/s10462-012-9328-0

Li HC, Zhou KQ, Mo LP, et al., 2020. Weighted fuzzy production rule extraction using modified harmony search algorithm and BP neural network framework. IEEE Access, 8:186620-186637. https://doi.org/10.1109/ACCESS.2020.3029966https://doi.org/10.1109/ACCESS.2020.3029966

Mirjalili S, 2015. Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst, 89:228-249. https://doi.org/10.1016/j.knosys.2015.07.006https://doi.org/10.1016/j.knosys.2015.07.006

Mousavi SM, Abdullah S, Niaki STA, et al., 2021. An intelligent hybrid classification algorithm integrating fuzzy rule-based extraction and harmony search optimization: medical diagnosis applications. Knowl-Based Syst, 220:106943. https://doi/org/10.1016/j.knosys.2021.106943https://doi/org/10.1016/j.knosys.2021.106943

Ong P, Zainuddin Z, 2019. Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction. Appl Soft Comput, 80:‍374-386. https://doi.org/10.1016/j.asoc.2019.04.016https://doi.org/10.1016/j.asoc.2019.04.016

Ouyang HB, Gao LQ, Li S, 2018. Amended harmony search algorithm with perturbation strategy for large-scale system reliability problems. Appl Intell, 48(11):‍3863-3888. https://doi.org/10.1007/s10489-018-1175-5https://doi.org/10.1007/s10489-018-1175-5

Qin AK, Forbes F, 2011. Harmony search with differential mutation based pitch adjustment. Proc 13th Annual Conf on Genetic and Evolutionary Computation, p.545-552. https://doi.org/10.1145/2001576.2001651https://doi.org/10.1145/2001576.2001651

Qin F, Zain AM, Zhou KQ, 2022. Harmony search algorithm and related variants: a systematic review. Swarm Evol Comput, 74:101126. https://doi.‍org/10.1016/j.‍swevo.‍2022.101126https://doi.‍org/10.1016/j.‍swevo.‍2022.101126

Shaffiei ZA, Abas ZA, Yunos NM, et al., 2019. Constrained self-adaptive harmony search algorithm with 2-opt swapping for driver scheduling problem of university shuttle bus. Arab J Sci Eng, 44(4):3681-3698. https://doi.‍org/10.1007/s13369-018-3628-xhttps://doi.‍org/10.1007/s13369-018-3628-x

Singh N, Kaur J, 2021. Hybridizing sine‍–‍cosine algorithm with harmony search strategy for optimization design problems. Soft Comput, 25(16):‍11053-11075. https://doi.org/10.1007/s00500-021-05841-yhttps://doi.org/10.1007/s00500-021-05841-y

Solis FJ, Wets RJB, 1981. Minimization by random search techniques. Math Oper Res, 6(1):‍19-30. https://doi.‍org/10.1287/moor.6.1.19https://doi.‍org/10.1287/moor.6.1.19

Tang J, Liu G, Pan QT, 2021. A review on representative swarm intelligence algorithms for solving optimization problems: applications and trends. IEEE/CAA J Autom Sin, 8(10):1627-1643. https://doi.org/10.1109/JAS.2021.1004129https://doi.org/10.1109/JAS.2021.1004129

Tu Q, Chen XC, Liu XC, 2019. Multi-strategy ensemble grey wolf optimizer and its application to feature selection. Appl Soft Comput, 76:16-30. https://doi.‍org/10.1016/j.asoc.2018.11.047https://doi.‍org/10.1016/j.asoc.2018.11.047

Valaei MR, Behnamian J, 2017. Allocation and sequencing in 1-out-of-N heterogeneous cold-standby systems: multi-objective harmony search with dynamic parameters tuning. Reliab Eng Syst Saf, 157:78-86. https://doi.org/10.1016/j.ress.2016.08.022https://doi.org/10.1016/j.ress.2016.08.022

Wang L, Hu HL, Liu R, et al., 2019. An improved differential harmony search algorithm for function optimization problems. Soft Comput, 23(13):‍4827-4852. https://doi.‍org/10.1007/s00500-018-3139-4https://doi.‍org/10.1007/s00500-018-3139-4

Wang YR, Gao SC, Zhou MC, et al., 2021. A multi-layered gravitational search algorithm for function optimization and real-world problems. IEEE/CAA J Autom Sin, 8(1):94-109. https://doi.org/10.1109/JAS.2020.1003462https://doi.org/10.1109/JAS.2020.1003462

Yang XS, Deb S, 2009. Cuckoo search via Lévy flights. World Congress on Nature & Biologically Inspired Computing, p.210-214. https://doi.org/10.1109/NABIC.2009.5393690https://doi.org/10.1109/NABIC.2009.5393690

Ye SQ, Zhou KQ, Zhang CX, et al., 2022. An improved multi-objective cuckoo search approach by exploring the balance between development and exploration. Electronics, 11(5):704. https://doi.org/10.3390/electronics11050704https://doi.org/10.3390/electronics11050704

Zhao ZY, Liu SX, Zhou MC, et al., 2021. Dual-objective mixed integer linear program and memetic algorithm for an industrial group scheduling problem. IEEE/CAA J Autom Sin, 8(6):1199-1209. https://doi.org/10.1109/JAS.2020.1003539https://doi.org/10.1109/JAS.2020.1003539

Zhu QD, Tang XM, Elahi A, 2021. Application of the novel harmony search optimization algorithm for DBSCAN clustering. Expert Syst Appl, 178:115054. https://doi.org/10.1016/j.eswa.2021.115054https://doi.org/10.1016/j.eswa.2021.115054

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