Hybrid Particle Swarm Optimization with Ant-Lion Optimization: Experimental in Benchmarks and Applications

Hassani, Z.; Alambardar Meybodi, M.

doi:10.22044/jadm.2021.10550.2195

Document Type : Original/Review Paper

Authors

Z. Hassani ¹
M. Alambardar Meybodi ²

¹ Department of Computer Science, Kosar University of Bojnord, Iran.

² Department of Applied Mathematics and Computer Science, University of Isfahan, P.O. Box 81746,73441, Isfahan, Iran, University.

https://doi.org/10.22044/jadm.2021.10550.2195

Abstract

A major pitfall in the standard version of Particle Swarm Optimization (PSO) is that it might get stuck in the local optima. To escape this issue, a novel hybrid model based on the combination of PSO and AntLion Optimization (ALO) is proposed in this study. The proposed method, called H-PSO-ALO, uses a local search strategy by employing the Ant-Lion algorithm to select the less correlated and salient feature subset. The objective is to improve the prediction accuracy and adaptability of the model in various datasets by balancing the exploration and exploitation processes. The performance of our method has been evaluated on 30 benchmark classification problems, CEC 2017 benchmark problems, and some well-known datasets. To verify the performance, four algorithms, including FDR-PSO, CLPSO, HFPSO, MPSO, are elected to be compared with the efficiency of H-PSO-ALO. Considering the experimental results, the proposed method outperforms the others in many cases, so it seems it is a desirable candidate for optimization problems on real-world datasets.

Keywords

20.1001.1.23225211.2021.9.4.14.1

References

[1] E. Ali, S. Abd-Elazim, and A. Abdelaziz, "Ant lion optimization algorithm for renewable distributed generations". Energy, vol. 116, pp. 445–458, 2016.

[2] Asuncion, A. and Newman, D. Uci machine learning repository, 2017.

[3] I.B. Aydilek, "A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems". Applied Soft Computing, vol. 66, pp. 232–249, 2018.

[4] W.-N. Chen, and D.-Z. Tan, "Set-based discrete particle swarm optimization and its applications: a survey". Frontiers of Computer Science, vol.12, no.2, pp.203–216, 2018.

[5] J. Ding, Q. Wang, Q. Zhang, Q. Ye, and Y. Ma, "A hybrid particle swarm optimization-cuckoo search algorithm and its engineering applications," Mathematical Problems in Engineering, 2019.

[6] S.-K. S. Fan, and C.-H. Jen, "An enhanced partial search to particle swarm optimization for unconstrained optimization". Mathematics, vol. 7, no. 4, pp. 357, 2019.

[7] M.M. Iqbal, and R. J. Xavier, "Development of optimal reduced-order model for gas turbine power plants using particle swarm optimization technique," International Transactions on Electrical Energy Systems, vol. 30, no. 4, 2020.

[8] J. Kennedy, and R. Eberhart, "Particle swarm optimization," In Proceedings of ICNN’95-International Conference on Neural Networks, Vol. 4, IEEE, pp. 1942–1948, 1995.

[9] S. Khunkitti, A. Siritaratiwat, S. Premrudeepreechacharn, R. Chatthaworn, and N. R. Watson, "A hybrid da-pso optimization algorithm for multiobjective optimal power flow problems". Energies vol. 11, no. 9, pp. 2270, 2018.

[10] J.J. Liang, A.K. Qin, P. N. Suganthan, and S. Baskar, "Comprehensive learning particle swarm optimizer for global optimization of multimodal functions," IEEE transactions on evolutionary computation, vol. 10, no. 3, pp. 281–295, 2006.

[11] H. Liu, X.-W. Zhang, and L.-P. Tu, "A modified particle swarm optimization using adaptive strategy". Expert Systems with Applications, vol. 152, 2020.

[12] M.M. Mafarja, and S. Mirjalili, "Hybrid whale optimization algorithm with simulated annealing for feature selection". Neurocomputing, vol. 260, pp. 302–312, 2017.

[13] S. Mirjalili, "The ant lion optimizer". Advances in engineering software, vol. 83, pp. 80–98, 2015.

[14] P. Moradi, and M. Gholampour, "A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy," Applied Soft Computing, vol. 43, pp. 117–130, 2016.

[15] I.-S. Oh, J.-S. Lee, and B.-R. Moon, "Hybrid genetic algorithms for feature selection," IEEE Transactions on pattern analysis and machine intelligence, vol. 26, no. 11, pp. 1424–1437, 2004.

[16] F. Omidinasab, and V. Goodarzimehr, "A hybrid particle swarm optimization and genetic algorithm for truss structures with discrete variables," Journal of Applied and Computational Mechanics, vol. 6, no. 3, pp. 593–604, 2020.

[17] Peram, T., Veeramachaneni, K., and Mohan, C.K. "Fitness-distance-ratio based particle swarm optimization. In Proceedings of the 2003 IEEE Swarm Intelligence Symposium". SIS’03 (Cat. No. 03EX706) (2003), IEEE, pp. 174–181.

[18] N. Qu, J. Chen, J. Zuo, and J. Liu, "Pso–som neural network algorithm for series arc fault detection". Advances in Mathematical Physics ,2020.

[19] E. Salehpour, J. Vahidi, and H. Hossinzadeh, "Solving optimal control problems by pso-svm". Computational Methods for Differential Equations, vol. 6, no. 3, pp. 312–325, 2018.

[20] Singh, N. and Singh, S. "Hybrid algorithm of particle swarm optimization and grey wolf optimizer for improving convergence performance," Journal of Applied Mathematics, 2017.

[21] N. Sunil, R. Ganesan, and B. Sankaragomathi," Analysis of osa syndrome from ppg signal using cart-pso classifier with time domain and frequency domain features," Computer Modeling in Engineering & Sciences vol. 118, no. 2, pp. 351–375, 2019.

[22] O. Tarkhaneh, and H. Shen, "Training of feedforward neural networks for data classification using hybrid particle swarm optimization," mantegna lévy flight and neighborhood search., vol. 5, no. 4, 2019.

[23] V.R. VC, et al. "Ant-lion optimization algorithm for optimal sizing of renewable energy resources for loss reduction in distribution systems," Journal of Electrical Systems and Information Technology, vol.5, no. 3, pp. 663–680, 2018.

[24] L. Wang, X. Liu, M. Sun, and J. Qu, "An extended clustering membrane system based on particle swarm optimization and cell-like p system with active membranes," Mathematical Problems in Engineering, 2020.

[25] A. Wan, L. Jiang, C. S. Sangeeth, and C.A. Nijhuis, "Reversible soft top-contacts to yield molecular junctions with precise and reproducible electrical characteristics, " Advanced Functional Materials, vol. 24, no. 28, pp. 4442–4456, 2014.

[26] X. Xu, H. Rong, M. Trovati, M. Liptrott, and N. Bessis, "Cs-pso: chaotic particle swarm optimization algorithm for solving combinatorial optimization problems, " Soft Computing, vol. 22, no.3, pp. 783–795, 2018.

[29] S. Hosseinirad, "Multi-layer Clustering Topology Design in Densely Deployed Wireless Sensor Network using Evolutionary Algorithms, " Journal of AI and Data Mining, vol.6, no.2, pp. 297-311, 2018.

Hybrid Particle Swarm Optimization with Ant-Lion Optimization: Experimental in Benchmarks and Applications

References

References

Volume 9, Issue 4November 2021Pages 583-595

Volume 9, Issue 4
November 2021
Pages 583-595