Skip to main content
SpringerLink
Log in

Dynamic Individual Selection and Crossover Boosted Forensic-based Investigation Algorithm for Global Optimization and Feature Selection

  • Research Article
  • Published:
Journal of Bionic Engineering Aims and scope Submit manuscript

Abst

The advent of Big Data has rendered Machine Learning tasks more intricate as they frequently involve higher-dimensional data. Feature Selection (FS) methods can abate the complexity of the data and enhance the accuracy, generalizability, and interpretability of models. Meta-heuristic algorithms are often utilized for FS tasks due to their low requirements and efficient performance. This paper introduces an augmented Forensic-Based Investigation algorithm (DCFBI) that incorporates a Dynamic Individual Selection (DIS) and crisscross (CC) mechanism to improve the pursuit phase of the FBI. Moreover, a binary version of DCFBI (BDCFBI) is applied to FS. Experiments conducted on IEEE CEC 2017 with other metaheuristics demonstrate that DCFBI surpasses them in search capability. The influence of different mechanisms on the original FBI is analyzed on benchmark functions, while its scalability is verified by comparing it with the original FBI on benchmarks with varied dimensions. BDCFBI is then applied to 18 real datasets from the UCI machine learning database and the Wieslaw dataset to select near-optimal features, which are then compared with six renowned binary metaheuristics. The results show that BDCFBI can be more competitive than similar methods and acquire a subset of features with superior classification accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Availability of Data and Materials

The data involved in this study are all public data, which can be downloaded through public channels.

References

  1. Kotsiantis, S. B., Kanellopoulos, D., & Pintelas, P. E. (2006). Data preprocessing for supervised leaning. International Journal of Computer Science, 1, 111–117.

    Google Scholar 

  2. Alelyani, S., Tang, J. L., & Liu, H. (2018). Feature selection for clustering: a review. Data Clustering (pp. 29–60). Routledge.

    Google Scholar 

  3. Zhou, X. X., & Zhang, L. (2022). Sa-fpn: An effective feature pyramid network for crowded human detection. Applied Intelligence, 52, 12556–12568.

    Google Scholar 

  4. Bermingham, M. L., Pong-Wong, R., Spiliopoulou, A., Hayward, C., Rudan, I., Campbell, H., Wright, A. F., Wilson, J. F., Agakov, F., & Navarro, P. (2015). Application of high-dimensional feature selection: Evaluation for genomic prediction in man. Scientific Reports, 5, 1–12.

    Google Scholar 

  5. Nadimi-Shahraki, M. H., BanaiE–Dezfouli, M., Zamani, H., Taghian, S., & Mirjalili, S. (2021). B-mfo: A binary moth-flame optimization for feature selection from medical datasets. Computers, 10, 136.

    Google Scholar 

  6. Anter, A. M., & Ali, M. (2020). Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems. Soft Computing, 24, 1565–1584.

    Google Scholar 

  7. Saeys, Y., Inza, I., & Larranaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23, 2507–2517.

    Google Scholar 

  8. Wang, L., Wang, Y. L., & Chang, Q. (2016). Feature selection methods for big data bioinformatics: A survey from the search perspective. Methods, 111, 21–31.

    Google Scholar 

  9. Afza, F., Khan, M. A., Sharif, M., Kadry, S., Manogaran, G., Saba, T., Ashraf, I., & Damaševičius, R. (2021). A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection. Image and Vision Computing, 106, 104090.

    Google Scholar 

  10. Ma, B. T., & Xia, Y. (2017). A tribe competition-based genetic algorithm for feature selection in pattern classification. Applied Soft Computing, 58, 328–338.

    Google Scholar 

  11. Barbu, A., She, Y., Ding, L., & Gramajo, G. (2016). Feature selection with annealing for computer vision and big data learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 272–286.

    Google Scholar 

  12. Khan, M. A., Zhang, Y. D., Allison, M., Kadry, S., Wang, S. H., Saba, T., & Iqbal, T. (2021). A fused heterogeneous deep neural network and robust feature selection framework for human actions recognition. Arabian Journal for Science and Engineering, 48, 2609.

    Google Scholar 

  13. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.

    MATH  Google Scholar 

  14. Alshaer, H. N., Otair, M. A., Abualigah, L., Alshinwan, M., & Khasawneh, A. M. (2021). Feature selection method using improved chi square on arabic text classifiers: Analysis and application. Multimedia Tools and Applications, 80, 10373–10390.

    Google Scholar 

  15. Jebli, I., Belouadha, F. Z., Kabbaj, M. I., & Tilioua, A. (2021). Prediction of solar energy guided by pearson correlation using machine learning. Energy, 224, 120109.

    Google Scholar 

  16. Hu, H. Y., Shan, W. F., Tang, Y. T., Heidari, A. A., Chen, H. L., Liu, H. J., Wang, M. F., Escorcia-Gutierrez, J., Mansour, R. F., & Chen, J. (2022). Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection. Journal of Computational Design and Engineering, 9, 2524–2555.

    Google Scholar 

  17. Bianchi, L., Dorigo, M., Gambardella, L. M., & Gutjahr, W. J. (2009). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8, 239–287.

    MathSciNet  MATH  Google Scholar 

  18. Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys (CSUR), 35, 268–308.

    Google Scholar 

  19. Li, R. H., Wu, X. L., Tian, H., Yu, N. A., & Wang, C. (2022). Hybrid memetic pretrained factor analysis-based deep belief networks for transient electromagnetic inversion. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–20.

    Google Scholar 

  20. Lin, Y., Song, H., Ke, F., Yan, W. Z., Liu, Z. K., & Cai, F. M. (2022). Optimal caching scheme in d2d networks with multiple robot helpers. Computer Communications, 181, 132–142.

    Google Scholar 

  21. Holland, J. H. (1992). Genetic algorithms. Scientific American, 267, 66–73.

    Google Scholar 

  22. Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359.

    MathSciNet  MATH  Google Scholar 

  23. Tang, D. (2019). Spherical evolution for solving continuous optimization problems. Applied Soft Computing, 81, 105499.

    Google Scholar 

  24. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95-International Conference on Neural Networks, 4 (pp. 1942–1948). Geneva: IEEE.

    Google Scholar 

  25. Tian, J., Hou, M. D., Bian, H. L., & Li, J. Q. (2022). Variable surrogate model-based particle swarm optimization for high-dimensional expensive problems. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-022-00910-7

    Article  Google Scholar 

  26. Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1, 28–39.

    Google Scholar 

  27. Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel naturE–inspired heuristic paradigm. KnowledgE–Based Systems, 89, 228–249.

    Google Scholar 

  28. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.

    Google Scholar 

  29. Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X. F., & Chen, H. L. (2021). Run beyond the metaphor: An efficient optimization algorithm based on runge kutta method. Expert Systems with Applications, 181, 115079.

    Google Scholar 

  30. Yang, Y. T., Chen, H. L., Heidari, A. A., & Gandomi, A. H. (2021). Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, 114864.

    Google Scholar 

  31. Chen, H. L., Li, C. Y., Mafarja, M., Heidari, A. A., Chen, Y., & Cai, Z. N. (2022). Slime mould algorithm: A comprehensive review of recent variants and applications. International Journal of Systems Science, 54, 1–32.

    MATH  Google Scholar 

  32. Li, S. M., Chen, H. L., Wang, M. J., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300–323.

    Google Scholar 

  33. Tu, J. Z., Chen, H. L., Wang, M. J., & Gandomi, A. H. (2021). The colony predation algorithm. Journal of Bionic Engineering, 18, 674–710.

    Google Scholar 

  34. Ahmadianfar, I., Asghar Heidari, A., Noshadian, S., Chen, H. L., & Gandomi, A. H. (2022). Info: An efficient optimization algorithm based on weighted mean of vectors. Expert Systems with Applications, 195, 116516.

    Google Scholar 

  35. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. L. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872.

    Google Scholar 

  36. Zhang, Y. N., Liu, R. J., Heidari, A. A., Wang, X., Chen, Y., Wang, M. J., & Chen, H. L. (2021). Towards augmented kernel extreme learning models for bankruptcy prediction: Algorithmic behavior and comprehensive analysis. Neurocomputing, 430, 185–212.

    Google Scholar 

  37. Liu, Y., Heidari, A. A., Cai, Z. N., Liang, G. X., Chen, H. L., Pan, Z. F., Alsufyani, A., & Bourouis, S. (2022). Simulated annealing-based dynamic step shuffled frog leaping algorithm: Optimal performance design and feature selection. Neurocomputing, 503, 325–362.

    Google Scholar 

  38. Xue, Y., Xue, B., & Zhang, M. J. (2019). Self-adaptive particle swarm optimization for large–scale feature selection in classification. ACM Transactions on Knowledge Discovery from Data (TKDD), 13, 1–27.

    Google Scholar 

  39. Xue, Y., Cai, X., & Neri, F. (2022). A multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for largE–scale feature selection in classification. Applied Soft Computing, 127, 109420.

    Google Scholar 

  40. Dong, R. Y., Chen, H. L., Heidari, A. A., Turabieh, H., Mafarja, M., & Wang, S. S. (2021). Boosted kernel search: Framework, analysis and case studies on the economic emission dispatch problem. Knowledge–Based Systems, 233, 107529.

    Google Scholar 

  41. Yu, K. J., Zhang, D. Z., Liang, J., Chen, K., Yue, C. T., Qiao, K. J., & Wang, L. (2022). A correlation-guided layered prediction approach for evolutionary dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/TEVC.2022.3193287

    Article  Google Scholar 

  42. Liang, J., Qiao, K. J., Yu, K. J., Qu, B. Y., Yue, C. T., Guo, W. F., & Wang, L. (2022). Utilizing the relationship between unconstrained and constrained pareto fronts for constrained multiobjective optimization. IEEE Transactions on Cybernetics. https://doi.org/10.1109/TCYB.2022.3163759

    Article  Google Scholar 

  43. Deng, W., Xu, J. J., Gao, X. Z., & Zhao, H. M. (2022). An enhanced msiqde algorithm with novel multiple strategies for global optimization problems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52, 1578–1587.

    Google Scholar 

  44. Huang, C., Zhou, X. B., Ran, X. J., Liu, Y., Deng, W. Q., & Deng, W. (2023). Co-evolutionary competitive swarm optimizer with three–phase for large–scale complex optimization problem. Information Sciences, 619, 2–18.

    Google Scholar 

  45. Xue, Y., Tong, Y. L., & Neri, F. (2022). An ensemble of differential evolution and adam for training feed-forward neural networks. Information Sciences, 608, 453–471.

    Google Scholar 

  46. Wen, X. Y., Wang, K. H., Li, H., Sun, H. Q., Wang, H. Q., & Jin, L. J. (2021). A two-stage solution method based on nsga-ii for green multi-objective integrated process planning and scheduling in a battery packaging machinery workshop. Swarm and Evolutionary Computation, 61, 100820.

    Google Scholar 

  47. Wang, G. Q., Fan, E., Zheng, G. H., Li, K. X., & Huang, H. G. (2022). Research on vessel speed heading and collision detection method based on ais data. Mobile Information Systems, 10, 1–10.

    Google Scholar 

  48. Zhao, C. L., Zhou, Y. R., & Lai, X. S. (2022). An integrated framework with evolutionary algorithm for multi-scenario multi-objective optimization problems. Information Sciences, 600, 342–361.

    Google Scholar 

  49. Hussien, A. G., Heidari, A. A., Ye, X. J., Liang, G. X., Chen, H. L., & Pan, Z. F. (2022). Boosting whale optimization with evolution strategy and Gaussian random walks: An image segmentation method. Engineering with Computers. https://doi.org/10.1007/s00366-021-01542-0

    Article  Google Scholar 

  50. Abd El Aziz, M., Ewees, A. A., & Hassanien, A. E. (2017). Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Systems with Applications, 83, 242–256.

    Google Scholar 

  51. Cura, T. (2009). Particle swarm optimization approach to portfolio optimization. Nonlinear Analysis: Real World Applications, 10, 2396–2406.

    MathSciNet  MATH  Google Scholar 

  52. Perold, A. F. (1984). Large–scale portfolio optimization. Management Science, 30, 1143–1160.

    MathSciNet  MATH  Google Scholar 

  53. Thakkar, A., & Chaudhari, K. (2021). A comprehensive survey on portfolio optimization, stock price and trend prediction using particle swarm optimization. Archives of Computational Methods in Engineering, 28, 2133–2164.

    MathSciNet  Google Scholar 

  54. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.

    Google Scholar 

  55. Hüsken, M., Jin, Y. C., & Sendhoff, B. (2005). Structure optimization of neural networks for evolutionary design optimization. Soft Computing, 9, 21–28.

    Google Scholar 

  56. Loghmanian, S. M. R., Jamaluddin, H., Ahmad, R., Yusof, R., & Khalid, M. (2012). Structure optimization of neural network for dynamic system modeling using multi-objective genetic algorithm. Neural Computing and Applications, 21, 1281–1295.

    Google Scholar 

  57. Shan, W. F., Hu, H. Y., Cai, Z. N., Chen, H. L., Liu, H. J., Wang, M. F., & Teng, Y. T. (2022). Multi-strategies boosted mutative crow search algorithm for global tasks: Cases of continuous and discrete optimization. Journal of Bionic Engineering, 19, 1860–1849.

    Google Scholar 

  58. Han, X., Han, Y. Y., Chen, Q. D., Li, J. Q., Sang, H. Y., Liu, Y. P., Pan, Q. K., & Nojima, Y. (2021). Distributed flow shop scheduling with sequencE–dependent setup times using an improved iterated greedy algorithm. Complex System Modeling and Simulation, 1, 198–217.

    Google Scholar 

  59. Wang, G. G., Gao, D., & Pedrycz, W. (2022). Solving multi-objective fuzzy job-shop scheduling problem by a hybrid adaptive differential evolution algorithm. IEEE Transactions on Industrial Informatics, 18, 8519–8528.

    Google Scholar 

  60. Xia, J. F., Yang, D. Q., Zhou, H., Chen, Y. Y., Zhang, H. L., Liu, T., Heidari, A. A., Chen, H. L., & Pan, Z. F. (2022). Evolving kernel extreme learning machine for medical diagnosis via a disperse foraging sine cosine algorithm. Computers in Biology and Medicine, 141, 105137.

    Google Scholar 

  61. Shan, W. F., Qiao, Z. L., Heidari, A. A., Chen, H. L., Turabieh, H., & Teng, Y. T. (2021). Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis. Knowledge–Based Systems, 214, 106728.

    Google Scholar 

  62. Abbassi, R., Abbassi, A., Heidari, A. A., & Mirjalili, S. (2019). An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Conversion and Management, 179, 362–372.

    Google Scholar 

  63. Huang, C. L., & Wang, C. J. (2006). A GA-based feature selection and parameters optimization for support vector machines. Expert Systems with Applications, 31, 231–240.

    Google Scholar 

  64. Wei, J. X., Zhang, R. S., Yu, Z. X., Hu, R. J., Tang, J. X., Gui, C., & Yuan, Y. N. (2017). A bpso-svm algorithm based on memory renewal and enhanced mutation mechanisms for feature selection. Applied Soft Computing, 58, 176–192.

    Google Scholar 

  65. Zhao, S. W., Wang, P. J., Heidari, A. A., Zhao, X. H., Ma, C., & Chen, H. L. (2021). An enhanced Cauchy mutation grasshopper optimization with trigonometric substitution: Engineering design and feature selection. Engineering with Computers, 38, 4583–4616.

    Google Scholar 

  66. Agrawal, P., Ganesh, T., & Mohamed, A. W. (2021). A novel binary gaining–sharing knowledge–based optimization algorithm for feature selection. Neural Computing and Applications, 33, 5989–6008.

    Google Scholar 

  67. Agrawal, P., Abutarboush, H. F., Ganesh, T., & Mohamed, A. W. (2021). Metaheuristic algorithms on feature selection: A survey of one decade of research (2009–2019). IEEE Access, 9, 26766–26791.

    Google Scholar 

  68. Kuyu, Y. Ç., & Vatansever, F. (2022). Modified forensic-based investigation algorithm for global optimization. Engineering with Computers, 38, 3197–3218.

    Google Scholar 

  69. Kaveh, A., Hamedani, K. B., & Kamalinejad, M. (2021). An enhanced forensic-based investigation algorithm and its application to optimal design of frequency-constrained dome structures. Computers & Structures, 256, 106643.

    Google Scholar 

  70. Hoang, N. D., Huynh, T. C., & Tran, V. D. (2021). Computer vision-based patched and unpatched pothole classification using machine learning approach optimized by forensic-based investigation metaheuristic. Complexity, 2021, 3511375.

    Google Scholar 

  71. Fathy, A., Rezk, H., & Alanazi, T. M. (2021). Recent approach of forensic-based investigation algorithm for optimizing fractional order PID-based MPPT with proton exchange membrane fuel cell. IEEE Access, 9, 18974–18992.

    Google Scholar 

  72. Chou, J. S., & Truong, D. N. (2022). Multi-objective forensic-based investigation algorithm for solving structural design problems. Automation in Construction, 134, 104084.

    Google Scholar 

  73. Chou, J. S., & Nguyen, N. M. (2020). FBI inspired meta-optimization. Applied Soft Computing, 93, 106339.

    Google Scholar 

  74. Meng, A. B., Chen, Y. C., Yin, H., & Chen, S. Z. (2014). Crisscross optimization algorithm and its application. Knowledge–Based Systems, 67, 218–229.

    Google Scholar 

  75. Meng, A., Ge, J., Yin, H., & Chen, S. (2016). Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm. Energy Conversion and Management, 114, 75–88.

    Google Scholar 

  76. Zhao, D., Liu, L., Yu, F. H., Heidari, A. A., Wang, M. J., Oliva, D., Muhammad, K., & Chen, H. L. (2021). Ant colony optimization with horizontal and vertical crossover search: Fundamental visions for multi-threshold image segmentation. Expert Systems with Applications, 167, 114122.

    Google Scholar 

  77. Liu, H. R., Liu, M. Z., Li, D. F., Zheng, W. F., Yin, L. R., & Wang, R. L. (2022). Recent advances in pulse–coupled neural networks with applications in image processing. Electronics, 11, 3264.

    Google Scholar 

  78. Xu, J. W., Pan, S. C., Sun, P. Z. H., Park, S. H., & Guo, K. (2022). Human-factors-in-driving-loop: Driver identification and verification via a deep learning approach using psychological behavioral data. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2022.3225782

    Article  Google Scholar 

  79. Zhang, X., Wen, S. J., Yan, L., Feng, J. F., & Xia, Y. (2022). A hybrid-convolution spatial–temporal recurrent network for traffic flow prediction. The Computer Journal. https://doi.org/10.1093/comjnl/bxac171

    Article  Google Scholar 

  80. Cheng, L., Yin, F., Theodoridis, S., Chatzis, S., & Chang, T. H. (2022). Re-thinking Bayesian learning for data analysis: The art of prior and inference in sparsity-aware modeling. IEEE Signal Processing Magazine, 39, 18–52.

    Google Scholar 

  81. Wu, G., Mallipeddi, R., & Suganthan, P.N. (2017). Problem definitions and evaluation criteria for the cec 2017 competition on constrained real-parameter optimization. In: National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore, Technical Report.

  82. Nenavath, H., & Jatoth, R. K. (2018). Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking. Applied Soft Computing, 62, 1019–1043.

    Google Scholar 

  83. Liu, G. M., Jia, W. Y., Wang, M. J., Heidari, A. A., Chen, H. L., Luo, Y. G., & Li, C. Y. (2020). Predicting cervical hyperextension injury: A covariance guided sine cosine support vector machine. IEEE Access, 8, 46895–46908.

    Google Scholar 

  84. Jia, D. L., Zheng, G. X., Qu, B. Y., & Khan, M. K. (2011). A hybrid particle swarm optimization algorithm for high-dimensional problems. Computers & Industrial Engineering, 61, 1117–1122.

    Google Scholar 

  85. Singh, R. P., Mukherjee, V., & Ghoshal, S. P. (2016). Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem. Applied Soft Computing, 40, 161–177.

    Google Scholar 

  86. Heidari, A. A., Abbaspour, R. A., & Chen, H. L. (2019). Efficient boosted grey wolf optimizers for global search and kernel extreme learning machine training. Applied Soft Computing, 81, 105521.

    Google Scholar 

  87. Reddy, K. S., Panwar, L., Panigrahi, B., & Kumar, R. (2019). Binary whale optimization algorithm: A new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets. Engineering Optimization, 51, 369–389.

    MathSciNet  MATH  Google Scholar 

  88. Mafarja, M., Aljarah, I., Heidari, A. A., Faris, H., Fournier-Viger, P., Li, X., & Mirjalili, S. (2018). Binary dragonfly optimization for feature selection using timE–varying transfer functions. KnowledgE–Based Systems, 161, 185–204.

    Google Scholar 

  89. Pietruszkiewicz, W. (2004). Application of discrete predicting structures in an early warning expert system for financial distress (Doctoral dissertation, Ph. d. thesis, Faculty of Computer Science and Information Technology, Szczecin University of Technology).

  90. Emary, E., Zawbaa, H. M., & Hassanien, A. E. (2016). Binary grey wolf optimization approaches for feature selection. Neurocomputing, 172, 371–381.

    Google Scholar 

  91. Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2010). Bgsa: Binary gravitational search algorithm. Natural Computing, 9, 727–745.

    MathSciNet  MATH  Google Scholar 

  92. Dara, S., & Banka, H. (2014). A binary pso feature selection algorithm for gene expression data. 2014 International conference on advances in communication and computing technologies (ICACACT 2014) (pp. 1–6). Mumbai, India: IEEE.

    Google Scholar 

  93. Mirjalili, S., Mirjalili, S. M., & Yang, X.-S. (2014). Binary bat algorithm. Neural Computing and Applications, 25, 663–681.

    Google Scholar 

  94. Faris, H., Mafarja, M. M., Heidari, A. A., Aljarah, I., Alam, A.-Z., Mirjalili, S., & Fujita, H. (2018). An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowledge-Based Systems, 154, 43–67.

    Google Scholar 

  95. Zhao, C. C., Wang, H. S., Chen, H. L., Shi, W. W., & Feng, Y. J. (2022). Jamsnet: A remote pulse extraction network based on joint attention and multi-scale fusion. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/TCSVT.2022.3227348

    Article  Google Scholar 

  96. Chen, Y., Gan, H. M., Chen, H. L., Zeng, Y. G., Xu, L., Heidari, A. A., Zhu, X. D., & Liu, Y. N. (2023). Accurate iris segmentation and recognition using an end-to-end unified framework based on madnet and dsanet. Neurocomputing, 517, 264–278.

    Google Scholar 

  97. Li, Y., Zhang, Y., Cui, W. G., Lei, B. Y., Kuang, X. H., & Zhang, T. (2022). Dual encoder-based dynamic-channel graph convolutional network with edge enhancement for retinal vessel segmentation. IEEE Transactions on Medical Imaging, 41, 1975–1989.

    Google Scholar 

  98. Wang, S. S., Wang, B. L., Zhang, Z., Heidari, A. A., & Chen, H. L. (2023). Class-aware sample reweighting optimal transport for multi-source domain adaptation. Neurocomputing, 523, 213–223.

    Google Scholar 

  99. Yan, B., Li, Y., Li, L., Yang, X. C., Li, T. Q., Yang, G., & Jiang, M. F. (2022). Quantifying the impact of pyramid squeeze attention mechanism and filtering approaches on Alzheimer’s disease classification. Computers in Biology and Medicine, 148, 105944.

    Google Scholar 

  100. Lv, J., Li, G., Tong, X. R., Chen, W. B., Huang, J. H., Wang, C. Y., & Yang, G. (2021). Transfer learning enhanced generative adversarial networks for multi-channel MRI reconstruction. Computers in Biology and Medicine, 134, 104504.

    Google Scholar 

  101. Sun, X. H., Cao, X. Y., Zeng, B., Zhai, Q. Z., & Guan, X. H. (2022). Multi-stage dynamic planning of integrated hydrogen-electrical microgrids under multiscale uncertainties. IEEE Transactions on Smart Grid. https://doi.org/10.1109/TSG.2022.3232545

    Article  Google Scholar 

  102. Cao, X. Y., Cao, T. X., Xu, Z. B., Zeng, B., Gao, F., & Guan, X. H. (2022). Resilience constrained scheduling of mobile emergency resources in electricity-hydrogen distribution network. IEEE Transactions on Sustainable Energy. https://doi.org/10.1109/TSTE.2022.3217514

    Article  Google Scholar 

  103. Ban, Y., Wang, Y., Liu, S., Yang, B., Liu, M. Z., Yin, L. R., & Zheng, W. F. (2022). 2d/3d multimode medical image alignment based on spatial histograms. Applied Sciences, 12, 8261.

    Google Scholar 

  104. Wu, Z. D., Xuan, S. L., Xie, J., Lin, C. Z., & Lu, C. L. (2022). How to ensure the confidentiality of electronic medical records on the cloud: A technical perspective. Computers in Biology and Medicine, 147, 105726.

    Google Scholar 

  105. Wu, Z. D., Li, G. L., Shen, S. G., Lian, X., Chen, E. H., & Xu, G. D. (2021). Constructing dummy query sequences to protect location privacy and query privacy in location-based services. World Wide Web, 24, 25–49.

    Google Scholar 

  106. Wu, Z. D., Shen, S. G., Lian, X. Z., Su, X. N., & Chen, E. H. (2020). A dummy-based user privacy protection approach for text information retrieval. Knowledge–Based Systems, 195, 105679.

    Google Scholar 

  107. Wu, Z. D., Shen, S. G., Li, H. X., Zhou, H. P., & Lu, C. L. (2021). A basic framework for privacy protection in personalized information retrieval: An effective framework for user privacy protection. Journal of Organizational and End User Computing (JOEUC), 33, 1–26.

    Google Scholar 

  108. Wu, Z. D., Shen, S. G., Zhou, H. P., Li, H. X., Lu, C. L., & Zou, D. D. (2021). An effective approach for the protection of user commodity viewing privacy in E–commerce website. Knowledge–Based Systems, 220, 106952.

    Google Scholar 

  109. Wu, Z. D., Xie, J., Shen, S. G., Lin, C. Z., Xu, G. D., & Chen, E. H. (2023). A confusion method for the protection of user topic privacy in Chinese keyword based book retrieval. ACM Transactions on Asian and Low-Resource Language Information Processing. https://doi.org/10.1145/3571731

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Special Fund of Fundamental Scientific Research Business Expense for Higher School of Central Government (ZY20180119), the Natural Science Foundation of Zhejiang Province (LZ22F020005), the Natural Science Foundation of Hebei Province (D2022512001) and National Natural Science Foundation of China (42164002, 62076185). We acknowledge the comments of the reviewers.

Author information

Authors and Affiliations

  1. School of Emergency Management, Institute of Disaster Prevention, Sanhe, 065201, China

    Hanyu Hu, Weifeng Shan, Lili Xing & Xinxin He

  2. Earthquake Administration of Anhui Province, Hefei, 230031, China

    Jun Chen

  3. Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China

    Ali Asghar Heidari & Huiling Chen

  4. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Maofa Wang

Authors
  1. Hanyu Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weifeng Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lili Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ali Asghar Heidari
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Huiling Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xinxin He
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Maofa Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Weifeng Shan or Huiling Chen.

Ethics declarations

Conflict of Interest

The authors declare that there is no conflict of interests regarding the publication of article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, H., Shan, W., Chen, J. et al. Dynamic Individual Selection and Crossover Boosted Forensic-based Investigation Algorithm for Global Optimization and Feature Selection. J Bionic Eng 20, 2416–2442 (2023). https://doi.org/10.1007/s42235-023-00367-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42235-023-00367-5

Keywords

Search

Navigation