Improved artificial electric field algorithm based on multi-strategy and its application

Yongqing Tian, Libo Liu, Xiaolei Wang, Lin Dong, Rana Gill, Ravi Tomar

Abstract


Artificial electric field algorithm is a new swarm bionic optimization algorithm, which uses the interaction force of charged particles to create a mathematical model to solve the problem. In order to improve the global exploration ability and local development ability of artificial electric field algorithm, an artificial electric field algorithm based on opposition learning is proposed. The chaos strategy is used to strengthen the quality of the initial population, and the opposition learning strategy is used to increase the diversity of the population and the development ability of the algorithm. The excellent performance of the algorithm is proved by simulation experiments. The improved artificial electric field algorithm was combined with SVM to construct the sand liquefaction identification model by selecting seven measured indexes, including intensity, underground water level, overlying effective pressure, standard penetration hit number, average particle size, non-uniformity coefficient and shear stress ratio. Compared with traditional methods such as standard method and seed simplification method, the results show that IAEFA-SVM model has high prediction accuracy and provides an effective method for sand liquefaction identification


Full Text:

PDF

References


Yadav, A. (2019). AEFA: Artificial electric field algorithm for global optimization. Swarm and Evolutionary Computation, 48, 93-108.

https://doi.org/10.1016/j.swevo.2019.03.013

Demirören, A., Ekinci, S., Hekimoğlu, B., & Izci, D. (2021). Opposition-based artificial electric field algorithm and its application to FOPID controller design for unstable magnetic ball suspension system. Engineering Science and Technology, an International Journal, 24(2), 469-479.

https://doi.org/10.1016/j.jestch.2020.08.001

Yadav, A. (2020). Discrete artificial electric field algorithm for high-order graph matching. Applied Soft Computing, 92, 106260.

https://doi.org/10.1016/j.asoc.2020.106260

Yadav, A., & Kumar, N. (2020). Artificial electric field algorithm for engineering optimization problems. Expert Systems with Applications, 149, 113308.

https://doi.org/10.1016/j.eswa.2020.113308

Hassan, M. H., Kamel, S., El-Dabah, M. A., Khurshaid, T., & Domínguez-García, J. L. (2021). Optimal reactive power dispatch with time-varying demand and renewable energy uncertainty using Rao-3 algorithm. IEEE Access, 9, 23264-23283.

https://ieeexplore.ieee.org/document/9344706

Sheikh, K. H., Ahmed, S., Mukhopadhyay, K., Singh, P. K., Yoon, J. H., Geem, Z. W., & Sarkar, R. (2020). EHHM: Electrical harmony based hybrid meta-heuristic for feature selection. IEEE Access, 8, 158125-158141.

https://ieeexplore.ieee.org/document/9178740

Xu, H., Zhai, X., Wang, Z., Cui, Z., Fu, Z., & Lu, Y. (2019). An epitaxial synaptic device made by a band-offset BaTiO3/Sr2IrO4 bilayer with high endurance and long retention. Applied Physics Letters, 114(10), 102904.

https://doi.org/10.1063/1.5085126

Singh, P. K., & Sharma, A. (2022). An intelligent WSN-UAV-based IoT framework for precision agriculture application. Computers and Electrical Engineering, 100, 107912.

https://doi.org/10.1016/j.compeleceng.2022.107912

Zeng, H., Dhiman, G., Sharma, A., Sharma, A., & Tselykh, A. (2021). An IoT and Blockchain‐based approach for the smart water management system in agriculture. Expert Systems, e12892.

https://doi.org/10.1111/exsy.12892

Sharma, A., & Singh, P. K. (2021). UAV‐based framework for effective data analysis of forest fire detection using 5G networks: An effective approach towards smart cities solutions. International Journal of Communication Systems, e4826.

https://doi.org/10.1002/dac.4826

Sharma, A., Singh, P. K., & Kumar, Y. (2020). An integrated fire detection system using IoT and image processing technique for smart cities. Sustainable Cities and Society, 61, 102332.

https://doi.org/10.1016/j.scs.2020.102332

Tizhoosh, H. R. (2005, November). Opposition-based learning: a new scheme for machine intelligence. In International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC'06) (Vol. 1, pp. 695-701). IEEE.

https://ieeexplore.ieee.org/document/1631345/

Karimi, F., Attarpour, A., Amirfattahi, R., & Nezhad, A. Z. (2019). Computational analysis of non-invasive deep brain stimulation based on interfering electric fields. Physics in Medicine & Biology, 64(23), 235010.

1088/1361-6560/ab5229

Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S., & Al-Atabany, W. (2021). Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51(3), 1531-1551.

https://doi.org/10.1007/s10489-020-01893-z

Alsattar, H. A., Zaidan, A. A., & Zaidan, B. B. (2020). Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 53(3), 2237-2264.

https://doi.org/10.1007/s10462-019-09732-5

Sayed, G. I., Khoriba, G., & Haggag, M. H. (2018). A novel chaotic salp swarm algorithm for global optimization and feature selection. Applied Intelligence, 48(10), 3462-3481.

https://doi.org/10.1007/s10489-018-1158-6

Ge, Q., Li, A., Li, S., Du, H., Huang, X., & Niu, C. (2021). Improved Bidirectional RRT Path Planning Method for Smart Vehicle. Mathematical Problems in Engineering, 2021.

https://doi.org/10.1155/2021/6669728

Jeong, W., Jeong, S. M., Lim, T., Han, C. Y., Yang, H., Lee, B. W., & Ju, S. (2019). Self-emitting artificial cilia produced by field effect spinning. ACS applied materials & interfaces, 11(38), 35286-35293.

https://doi.org/10.1021/acsami.9b09571

Petwal, H., & Rani, R. (2020). An improved artificial electric field algorithm for multi-objective optimization. Processes, 8(5), 584.

https://doi.org/10.3390/pr8050584

Selem, S. I., El‐Fergany, A. A., & Hasanien, H. M. (2021). Artificial electric field algorithm to extract nine parameters of triple‐diode photovoltaic model. International Journal of Energy Research, 45(1), 590-604.

https://doi.org/10.1002/er.5756

Naderipour, A., Abdul-Malek, Z., Mustafa, M. W. B., & Guerrero, J. M. (2021). A multi-objective artificial electric field optimization algorithm for allocation of wind turbines in distribution systems. Applied Soft Computing, 105, 107278.

https://doi.org/10.1016/j.asoc.2021.107278

Yadav, A. (2021). An intelligent model for the detection of white blood cells using artificial intelligence. Computer methods and programs in biomedicine, 199, 105893.

https://doi.org/10.1016/j.cmpb.2020.105893

Sharma, A., & Jain, S. K. (2021). Day-ahead optimal reactive power ancillary service procurement under dynamic multi-objective framework in wind integrated deregulated power system. Energy, 223, 120028.

https://doi.org/10.1016/j.energy.2021.120028

Wang, H., Sharma, A., & Shabaz, M. (2022). Research on digital media animation control technology based on recurrent neural network using speech technology. International Journal of System Assurance Engineering and Management, 13(1), 564-575.

https://doi.org/10.1007/s13198-021-01540-x

Sharma, P., Mishra, A., Saxena, A., & Shankar, R. (2021). A novel hybridized fuzzy PI-LADRC based improved frequency regulation for restructured power system integrating renewable energy and electric vehicles. IEEE Access, 9, 7597-7617.

https://ieeexplore.ieee.org/document/9312597

Alihodzic, A., Mujezinovic, A., & Turajlic, E. (2021). Electric and Magnetic Field Estimation Under Overhead Transmission Lines Using Artificial Neural Networks. IEEE Access, 9, 105876-105891.

1109/ACCESS.2021.3099760

Chen, M., Sharma, A., Bhola, J., Nguyen, T. V., & Truong, C. V. (2022). Multi-agent task planning and resource apportionment in a smart grid. International Journal of System Assurance Engineering and Management, 13(1), 444-455.

https://doi.org/10.1007/s13198-021-01467-3

Kharrich, M., Kamel, S., Abdeen, M., Mohammed, O. H., Akherraz, M., Khurshaid, T., & Rhee, S. B. (2021). Developed approach based on equilibrium optimizer for optimal design of hybrid PV/Wind/Diesel/Battery microgrid in Dakhla, Morocco. IEEE Access, 9, 13655-13670.

1109/ACCESS.2021.3051573

Chen, Y., Zhang, W., Dong, L., Cengiz, K., & Sharma, A. (2021). Study on vibration and noise influence for optimization of garden mower. Nonlinear Engineering, 10(1), 428-435.

https://doi.org/10.1515/nleng-2021-0034

Youd, T. L., & Idriss, I. M. (2001). Liquefaction resistance of soils: summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils. Journal of geotechnical and geoenvironmental engineering, 127(4), 297-313.

https://doi.org/10.1061/(ASCE)1090-0241(2001)127:4(297)

Chopra, S., Dhiman, G., Sharma, A., Shabaz, M., Shukla, P., & Arora, M. (2021). Taxonomy of adaptive neuro-fuzzy inference system in modern engineering sciences. Computational Intelligence and Neuroscience, 2021.

https://doi.org/10.1155/2021/6455592

Zhan, X., Mu, Z. H., Kumar, R., & Shabaz, M. (2021). Research on speed sensor fusion of urban rail transit train speed ranging based on deep learning. Nonlinear Engineering, 10(1), 363-373. https://doi.org/10.1515/nleng-2021-0028

Han, Z., Chen, M., Shao, S., & Wu, Q. (2022). Improved artificial bee colony algorithm-based path planning of unmanned autonomous helicopter using multi-strategy evolutionary learning. Aerospace Science and Technology, 122, 107374.

https://doi.org/10.1016/j.ast.2022.107374

Liu, C., Lin, M., Rauf, H. L., & Shareef, S. S. (2021). Parameter simulation of multidimensional urban landscape design based on nonlinear theory. Nonlinear Engineering, 10(1), 583-591. https://doi.org/10.1515/nleng-2021-0049

Sharma, A., Singh, P. K., Hong, W. C., Dhiman, G., & Slowik, A. (2021). Introduction to the Special Issue on Artificial Intelligence for Smart Cities and Industries. Scalable Computing: Practice and Experience, 22(2), 89-91. https://doi.org/10.12694/scpe.v22i2.1939

Wang, H., Wu, Z., Rahnamayan, S., Sun, H., Liu, Y., & Pan, J. S. (2014). Multi-strategy ensemble artificial bee colony algorithm. Information Sciences, 279, 587-603.

https://doi.org/10.1016/j.ins.2014.04.013

Lu, H., Sun, S., Cheng, S., & Shi, Y. (2021). An adaptive niching method based on multi-strategy fusion for multimodal optimization. Memetic Computing, 13(3), 341-357.

https://doi.org/10.1007/s12293-021-00338-5

Zhang, X., Rane, K. P., Kakaravada, I., & Shabaz, M. (2021). Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology. Nonlinear Engineering, 10(1), 245-254.

https://doi.org/10.1515/nleng-2021-0019

Sharma, A., Georgi, M., Tregubenko, M., Tselykh, A., & Tselykh, A. (2022). Enabling Smart Agriculture by Implementing Artificial Intelligence and Embedded Sensing. Computers & Industrial Engineering, 107936. https://doi.org/10.1016/j.cie.2022.107936

Zhuang, D. Y., Ma, K., Tang, C. A., Liang, Z. Z., Wang, K. K., & Wang, Z. W. (2019). Mechanical parameter inversion in tunnel engineering using support vector regression optimized by multi-strategy artificial fish swarm algorithm. Tunnelling and underground space technology, 83, 425-436.

https://doi.org/10.1016/j.tust.2018.09.027




DOI: https://doi.org/10.31449/inf.v46i3.3929

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.