Towards an Improved Particle Swarm Optimization for Feature Selection: A Survey
Keywords:
Swarm Intelligence, Particle Swarm Optimization, Feature Selection, Dimensionality ReductionAbstract
Over the years, scientists have used natural discoveries such as evolution to solve real-world problems. Addressing the challenges that arise when dealing with high-dimensional data is one such problem. These challenges include difficulties in analyzing, visualizing, and modelling these high-dimensional data. As a result, the Swarm Intelligence (SI) technique was developed, which was inspired by natural swarm foraging behaviors. Particle swarm optimization (PSO) is a well-known SI algorithm for addressing a wide range of optimization problems. As a result, it has been used to solve a variety of optimization problems in fields as diverse as genomic analysis and intrusion detection systems. One of the most successful areas of PSO application is feature selection, which entails using computational techniques to select a reduced subset of features that have a sufficient relationship with their corresponding class labels. This, in turn, addresses the mentioned challenges. Nonetheless, progressive research has revealed several problems with PSO, including problems with diversity, and premature convergence among others. As a result, several improvements and extensions were made to various aspects of the algorithm since its inception to make it efficient. This paper organizes and summarizes current research on improvements to the PSO algorithm for solving the feature selection problem. Consequently, it presents current trends and directions for scholars in the field, as well as open challenges and literature gaps to investigate.
References
Abdollahi, M., Gao, X., Mei, Y., Ghosh, S., & Li, J. (2019). An Ontology-based Two-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation. 119–126.
Abdollahzadeh, B., & Gharehchopogh, F. S. (2021). A multi-objective optimization algorithm for feature selection problems. Engineering with Computers, 0123456789.
Adamu, A., Abdullahi, M., Junaidu, S. B., & Hassan, I. H. (2021). An hybrid particle swarm optimization with crow search algorithm for feature selection. Machine Learning with Applications, 6, 100108.
Agustin, R. I. (2021). Classification of immature white blood cells in acute lymphoblastic leukemia L1 using neural networks particle swarm optimization. Neural Computing and Applications, 33(17), 10869–10880.
Al-tashi, Q., Abdulkadir, S. J., Rais, H., & Mirjalili, S. (2019). Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection. IEEE Access, PP(c), 1.
Aldasht, M. (2019). Efficient Feature Selection using Particle Swarm Optimization : A hybrid filters-wrapper Approach. 2019 10th International Conference on Information and Communication Systems (ICICS), 122–127.
Almazrua, H., & Alshamlan, H. (2022). A Comprehensive Survey of Recent Hybrid Feature Selection Methods in Cancer Microarray Gene Expression Data. IEEE Access, 10, 71427–71449.
Alrefai, N., & Ibrahim, O. (2022). Optimized feature selection method using particle swarm intelligence with ensemble learning for cancer classification based on microarray datasets. Neural Computing and Applications 2022, 1–16.
Alzaqebah, M., Jawarneh, S., Mohammad, R. M. A., Alsmadi, M. K., Al-marashdeh, I., Ahmed, E. A. E., Alrefai, N., & Alghamdi, F. A. (2021). Hybrid feature selection method based on particle swarm optimization and adaptive local search method.
Ansari, G., Ahmad, T., & Najmud, M. (2019). Hybrid Filter – Wrapper Feature Selection Method for Sentiment Classification. Arabian Journal for Science and Engineering, 0123456789.
Cao, B., Fan, S., Zhao, J., Yang, P., Muhammad, K., & Tanveer, M. (2020). Quantum-enhanced multiobjective large-scale optimization via parallelism. Swarm and Evolutionary Computation, 57, 100697.
Chen, K., Xue, B., Zhang, M., & Zhou, F. (2020). An Evolutionary Multitasking-Based Feature Selection Method for High-Dimensional Classification. IEEE Transactions on Cybernetics, 1–15.
Chen, K., Zhou, F., & Yuan, X. (2019). PT US CR. Expert Systems With Applications.
Chen, T. C., Alizadeh, S. M., Albahar, M. A., Thanoon, M., Alammari, A., Guerrero, J. W. G., Nazemi, E., & Eftekhari-Zadeh, E. (2023). Introducing the Effective Features Using the Particle Swarm Optimization Algorithm to Increase Accuracy in Determining the Volume Percentages of Three-Phase Flows. Processes 2023, Vol. 11, Page 236, 11(1), 236.
Chen, Y., Liu, J., Zhu, J., & Wang, Z. (2023). An improved binary particle swarm optimization combing V-shaped and U-shaped transfer function. Evolutionary Intelligence, 1–14.
Daneshfar, F., & Kabudian, S. J. (2019). Speech emotion recognition using discriminative dimension reduction by employing a modified quantum-behaved particle swarm optimization algorithm.
Dhrif, H., Giraldo, L. G. S., & Wuchty, S. (2019). A Stable Combinatorial Particle Swarm Optimization for Scalable Feature Selection in Gene Expression Data. 1–13.
Donkol, A. A. E. B., Hafez, A. G., Hussein, A. I., & M. Mourad Mabrook. (2023). Optimization of Intrusion Detection Using Likely Point PSO and Enhanced LSTM-RNN Hybrid Technique in Communication Networks. IEEE Access, 11, 9469–9482.
Dulhare, U. N. (2019). optimization Prediction system for heart disease using Naive Bayes and particle swarm. January 2018, 10–14.
El-hasnony, I. M., Barakat, S. I., Elhoseny, M., & Mostafa, R. R. (2020). Improved Feature Selection Model for Big Data Analytics. 8, 66989–67004.
El-Kenawy, E.-S., & Eid, M. (2020). Hybrid gray wolf and particle swarm optimization for feature selection. 16(3), 831–844.
Engelbrecht, A. P., Grobler, J., & Langeveld, J. (2019). Engineering Applications of Artificial Intelligence Set based particle swarm optimization for the feature selection problem ✩. Engineering Applications of Artificial Intelligence, 85(June), 324–336.
Ghosh, M., Guha, R., Alam, I., Lohariwal, P., Jalan, D., & Sarkar, R. (2020). Binary genetic swarm optimization: A combination of ga and pso for feature selection. Journal of Intelligent Systems, 29(1), 1598–1610.
Hafiz, F., Swain, A., Naik, C., & Patel, N. (2019). Efficient feature selection of power quality events using two dimensional (2D) particle swarms. Applied Soft Computing Journal, 81, 105498.
Han, F., Chen, W., Ling, Q., & Han, H. (2021). Multi-objective particle swarm optimization with adaptive strategies for feature selection. Swarm and Evolutionary Computation, 62(February 2020), 100847.
Hassan, I. H., Mohammed, A., Ali, Y. S., Jeremiah, I., & Abdulraheem, S. A. (2023). Metaheuristic algorithms in text clustering. Comprehensive Metaheuristics, 131–152.
Houssein, E. H., Gad, A. G., Hussain, K., & Nagaratnam, P. (2021). Major Advances in Particle Swarm Optimization : Theory , Analysis , and Application. Swarm and Evolutionary Computation, 63(February), 100868.
Hu, Y., Zhang, Y., Gao, X., Gong, D., Song, X., Guo, Y., & Wang, J. (2023). A federated feature selection algorithm based on particle swarm optimization under privacy protection. Knowledge-Based Systems, 260, 110122.
Hu, Y., Zhang, Y., & Gong, D. (2020). Multiobjective Particle Swarm Optimization for Feature Selection With Fuzzy Cost. 1–15.
Huang, X., Chi, Y., & Zhou, Y. (2019). Feature Selection of High Dimensional Data by Adaptive Potential Particle Swarm Optimization. 1052–1059.
Huda, R. K., & Banka, H. (2020). New efficient initialization and updating mechanisms in PSO for feature selection and classification. Neural Computing and Applications, 32(8), 3283–3294.
Isuwa, J., Abdullahi, M., & Abdulrahim, A. (2022). Hybrid particle swarm optimization with sequential one point flipping algorithm for feature selection. July, 1–18.
Jeremiah, I., Abdullahi, M., Yusuf, S. A., & Idris, M. N. (2022). Integration of Specific Local Search Methods in Metaheuristic Algorithms for Optimizing the Feature Selection Process : A Survey. 4(1), 34–48.
Ji, B. A. I., Lu, X., Sun, G., Li, J., & Xiao, Y. (2020). Bio-Inspired Feature Selection : An Improved Binary Particle Swarm Optimization Approach. 8, 85989–86002.
Jun Dou, Song, Y., Wei, G., & Zhang, Y. (2022). Fuzzy information decomposition incorporated and weighted Relief-F feature selection: When imbalanced data meet incompletion. Information Sciences, 584, 417–443.
Karimi-mamaghan, M., Mohammadi, M., Meyer, P., Karimi-mamaghan, A. M., & Talbi, E. (2021). Machine Learning at the service of Meta-heuristics for solving Combinatorial Optimization Problems: A state-of-the-art. European Journal of Operational Research.
Khourdifi, Y., & Bahaj, M. (2019). Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized by Particle Swarm Optimization and Ant Colony Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized by Particle Swarm Optimization an. February.
Kılıç, F., Kaya, Y., & Yildirim, S. (2021). Knowledge-Based Systems A novel multi population based particle swarm optimization for feature selection. Knowledge-Based Systems, 219, 106894.
Kumar, L., & Kumari, K. (2019). A novel hybrid BPSO – SCA approach for feature selection. Natural Computing, 0123456789.
Kunhare, N., Tiwari, R., & Dhar, J. (2020). Particle swarm optimization and feature selection for intrusion detection system. Sādhanā, 0123456789.
Lalwani, S., Sharma, H., Satapathy, S. C., Deep, K., & Bansal, J. C. (2019). A Survey on Parallel Particle Swarm Optimization Algorithms. Arabian Journal for Science and Engineering, 44(4), 2899–2923.
Lee, J., Park, J., Kim, H., & Kim, D. (2019). Competitive Particle Swarm Optimization for Multi-Category Text Feature Selection.
Li, A., Xue, B., & Zhang, M. (2021). Improved binary particle swarm optimization for feature selection with new initialization and search space reduction strategies. 106.
Li, X., Zhang, J., & Safara, F. (2021). Improving the Accuracy of Diabetes Diagnosis Applications through a Hybrid Feature Selection Algorithm. Neural Processing Letters, 0123456789.
Mahsa, M., Tab, F. A., & Khabat, S. (2019). Evolutionary Feature Selection Based on Semi-Local Search. 228–233.
Manohar, K., & Logashanmugam, E. (2022). Hybrid deep learning with optimal feature selection for speech emotion recognition using improved meta-heuristic algorithm. Knowledge-Based Systems, 246.
Mirsadeghi, E., & Khodayifar, S. (2020). Hybridizing particle swarm optimization with simulated annealing and differential evolution. Cluster Computing, 0123456789.
Mohd Ali, N., Besar, R., & Nor, N. A. (2022). Hybrid Feature Selection of Breast Cancer Gene Expression Microarray Data Based on Metaheuristic Methods: A Comprehensive Review. Symmetry, 14(10).
Moslehi, F., & Haeri, A. (2019a). A novel hybrid wrapper – filter approach based on genetic algorithm , particle swarm optimization for feature subset selection. Journal of Ambient Intelligence and Humanized Computing, 0123456789.
Moslehi, F., & Haeri, A. (2019b). An evolutionary computation ‑ based approach for feature selection. Journal of Ambient Intelligence and Humanized Computing, 0123456789.
Nabi, D. S. A., & Ramya Laxmi, K. (2021). Prediction Accuracy Model Aiming to Improve Prediction Accuracy in Congenital Heart Anomaly Detection using Hybrid Feature Selection with Modified Particle Swarm Optimization Approach. Journal of Physics: Conference Series, 1998(1).
Nagra, A. A., Han, F., & Ling, Q. H. (2019). An improved hybrid self-inertia weight adaptive particle swarm optimization algorithm with local search. Engineering Optimization, 51(7), 1115–1132.
Nagra, A. A., Han, F., Ling, Q. H., Abubaker, M., Mehta, S., & Apasiba, A. T. (2019). Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4 . 5 decision tree classifier for feature selection problems. Connection Science, 0(0), 1–21.
Narasimhan, B., & Malathi, A. (2019). Altered particle swarm optimization based attribute selection strategy with improved fuzzy Artificial Neural Net ...
Nayar, N., Ahuja, S., & Jain, S. (2019). Swarm Intelligence for Feature Selection : A Review of Literature and Reflection.
Nemet, S., Ostojić, G., Kukolj, D., Stankovski, S., & Jovanovic, D. (2019). Feature Selection Using Combined Particle Swarm Optimization and Artificial Neural Network Approach. Journal of Mechatronics, Automation and Identification Technology, 4(1), 7–11.
Nguyen, B. H., Xue, B., & Andreae, P. (2018). A Particle Swarm Optimization based Feature Selection Approach to Transfer Learning in Classification. 37–44.
Nguyen, B. H., Xue, B., & Andreae, P. (2019). A New Binary Particle Swarm Optimization Approach : Momentum and Dynamic Balance Between Exploration and Exploitation. IEEE Transactions on Cybernetics, PP, 1–15.
Pan, J. (2020). Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection. 1–16.
Qi, Y., Ding, F., Xu, F., & Yang, J. (2020). Channel and Feature Selection for a Motor Imagery-Based BCI System Using Multilevel Particle Swarm Optimization. 2020.
Qiu, C. (2019). A novel multi ‑ swarm particle swarm optimization for feature selection. Genetic Programming and Evolvable Machines, 66.
Qiu, C. (2020). A Multi-swarm Particle Swarm Optimization with an Adaptive Regrouping Strategy for Feature Selection. 130–136.
Qiu, C., & Liu, N. (2021). cte d Au tho r P roo f Un co rre Au tho r P f Un co rre cte d.
Rafiei, A., Moradi, P., & Ghaderzade, A. (2023). Multi-Label Feature Selection Using a Hybrid Approach Based on the Particle Swarm Optimization Algorithm. Rimag.
Rahmi, N. S., Neural, C., Classification, I., Hasan, H., Shafri, H. Z. M., & Habshi, M. (2020). Analyzing cerebral infarction using support vector machine with artificial bee colony and particle swarm optimization feature selection Analyzing cerebral infarction using support vector machine with artificial bee colony and particle swarm optimization f.
Rodrigues, A. L., Santana, M. A. De, Azevedo, W. W., Bezerra, R. S., Barbosa, V. A. F., Lima, R. C. F. De, & Santos, W. P. (2019). Identification of mammary lesions in thermographic images : feature selection study using genetic algorithms and particle swarm optimization.
Rustam, Z., & Kintandani, P. (2019). Application of Support Vector Regression in Indonesian Stock Price Prediction with Feature Selection Using Particle. 2019.
Sahu, B. (2019). Multi-Tier Hybrid Feature Selection by Combining Filter and Wrapper for Subset Feature Selection in Cancer Classification. 12(January), 1–11.
Sarkar, S., Ghosh, M., & Chatterjee, A. (2019). An Advanced Particle Swarm Optimization Based Feature Selection Method for Tri-script Handwritten Digit Recognition (Vol. 1). Springer Singapore.
Sarumi, O. A., & Leung, C. K. (2022). Adaptive Machine Learning Algorithm and Analytics of Big Genomic Data for Gene Prediction. Intelligent Systems Reference Library, 206, 103–123.
Sharma, D., Willy, C., & Bischoff, J. (2021). Optimal subset selection for causal inference using machine learning ensembles and particle swarm optimization. Complex & Intelligent Systems, 7(1), 41–59.
Song, X. fang, Zhang, Y., Gong, D. wei, & Sun, X. yan. (2021). Feature selection using bare-bones particle swarm optimization with mutual information. Pattern Recognition, 112, 107804.
Song, X., Zhang, Y., Gong, D., & Gao, X. (2021). A Fast Hybrid Feature Selection Based on Correlation-Guided Clustering and Particle Swarm Optimization for High-Dimensional Data. 1–14.
Song, X., Zhang, Y., Gong, D., Liu, H., & Zhang, W. (2022). Surrogate Sample-Assisted Particle Swarm Optimization for Feature Selection on High-Dimensional Data. IEEE Transactions on Evolutionary Computation.
Song, X., Zhang, Y., Guo, Y., Sun, X., & Wang, Y. (2020). Variable-size Cooperative Coevolutionary Particle Swarm Optimization for Feature Selection on High-dimensional Data. 14(8).
Sun, L., Yang, Y., & Ning, T. (2023). A novel feature selection using Markov blanket representative set and Particle Swarm Optimization algorithm. Computational and Applied Mathematics 2023 42:2, 42(2), 1–32.
Sundaramurthy, S., & Jayavel, P. (2020). A hybrid Grey Wolf Optimization and Particle Swarm Optimization with C4.5 approach for prediction of Rheumatoid Arthritis. Applied Soft Computing Journal, 94, 106500.
Tadist, K., Mrabti, F., Nikolov, N. S., Zahi, A., & Najah, S. (2021). SDPSO: Spark Distributed PSO-based approach for feature selection and cancer disease prognosis. Journal of Big Data, 8(1).
Tawhid, M. A., & Dsouza, K. B. (2019). Solving feature selection problem by hybrid binary genetic enhanced particle swarm optimization algorithm. 15, 207–219.
Too, J., Abdullah, A. R., & Saad, N. M. (2019a). A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy.
Too, J., Abdullah, A. R., & Saad, N. M. (2019b). Hybrid Binary Particle Swarm Optimization Differential Evolution-Based Feature Selection for EMG Signals Classification.
Too, J., Abdullah, A. R., Saad, N. M., & Tee, W. (2019). EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization.
Wang, C., & Song, W. (2019). A modified particle swarm optimization algorithm based on velocity updating mechanism. Ain Shams Engineering Journal, xxxx.
Wang, Xue, Y., & Jia, W. (2020). A New Population Initialization of Particle Swarm Optimization Method Based on PCA for Feature Selection. Journal on Big Data, 3(1), 1–9.
Wei, B., Zhang, W., Xia, X., Zhang, Y., Yu, F., & Zhu, Z. (2019). Efficient Feature Selection Algorithm Based on Particle Swarm Optimization with Learning Memory. IEEE Access, 7, 166066–166078.
