Abstract
The firefly algorithm is nature-inspired, and it belongs to swarm intelligence category optimization. Firefly algorithm is the latest algorithm developed under the nature-inspired algorithm (NIA), which is fruitful for business and engineering optimization. So, for business optimization, healthcare industries are affected through big data where big data affects in different ways such as patients understanding and care, improved personalized care, analysis trends, predict health outcomes, etc. Big data analysis is required to optimize a broad set of data generated by the different mediums in the healthcare and engineering sectors. A firefly algorithm can be used to optimize data analysis and results. Therefore, the firefly algorithm becomes essential to optimize the healthcare sector application process and provides optimized solutions. This paper discussed firefly algorithm implementations, optimization solutions for an engineering problem, fireflies' algorithm (FA) performance through MATLAB 2019b, formulation, optimization problems in healthcare, firefly algorithms in other applications, and conclusions. This paper focuses on how the firefly algorithm (FA) can be used, modified, and merged with other optimization algorithms to solve engineering problems.
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References
Aiane, D., El-Amraoui, A., & Mesghouni, K. (2016). A new optimization approach for a home health care problem. In 2015 international conference on industrial engineering and systems management (IESM), pp. 285–290.
Alomoush, W., Omar, K., Alrosan, A., Alomari, Y. M., Albashish, D., & Almomani, A. (2018). Firefly photinus search algorithm. The Journal of King Saud University Computer and Information Sciences.
Alreahan, H. O., Al-Ramadhani, S. T., & Kahya, M. A. (2019). Applying firefly algorithm to identify thinking types influencing achievement in mathematics. Journal of Interdisciplinary Mathematics, 22(8), 1583–1587.
Apostolopoulos, T., & Vlachos, A. (2011). Application of the firefly algorithm for solving the economic emissions load dispatch problem. International Journal of Combinatorics, 2011, 1–23.
Ardam, S., & Soleimanian Gharehchopogh, F. (2019). Diagnosing liver disease using firefly algorithm based on adaboost. Journal of Health Administration., 22(1), 61–77.
Arora, S., & Singh, S. (2017). Performance research on firefly optimization algorithm with mutation. International Conference on Computer and Communication Systems.
Aydilek, İB. (2018). A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Applied Soft Computing, 66, 232–249.
Bhawan, A., Delhi, N., Gwalior, M., Road, M. L., & Pradesh, M. (2013). Artificial bee colony algorithm : A survey Jagdish Chand Bansal Harish Sharma * and Shimpi Singh Jadon. International Journal of Advanced Intelligence Paradigms, 5, 123–159.
Bollmann, A., Roig, M., Castells, F., Laguna, P., & Leif, S. (2007). Principal component analysis in ECG signal processing. EURASIP Journal on Advances in Signal Processing, 2007, 074580.
Carbas, S. (2016). Design optimization of steel frames using an enhanced firefly algorithm. Engineering Optimization, 48(12), 2007–2025.
Chepa, N., Hashim, N. L., Yusof, Y., & Hussain, A. (2016). The application of Firefly algorithm in an Adaptive Emergency Evacuation Centre Management (AEECM) for dynamic relocation of flood victims. AIP Conference Proceedings, 1761.
Cheung, N. J., Ding, X. M., & Bin Shen, H. (2014). Adaptive firefly algorithm: Parameter analysis and its application. PLoS ONE, 9(11), e112634.
Chowdhury, S., Mayilvahanan, P., & Govindaraj, R. (2020). Optimal feature extraction and classification-oriented medical insurance prediction model: machine learning integrated with the internet of things. International Journal of Computing Applications, 1–13.
Christensen, J., & Bastien, C. (2016). Introduction to general optimization principles and methods.
Costa, L., & P. Oliveira, P. (2011). An introduction to optimization. Optimization Polymer Processing, 11–28
Danesh, M., & Shirgahi, H. (2017). A novel hybrid knowledge of firefly and pso swarm intelligence algorithms for efficient data clustering. Journal of Intelligent & Fuzzy Systems, 33(6), 3529–3538.
Deepak, G., Teja, V., & Santhanavijayan, A. (2020). A novel firefly driven scheme for resume parsing and matching based on entity linking paradigm. Journal of Discrete Mathematical Sciences and Cryptography, 23(1), 157–165.
Eren, Y., Küçükdemiral, İ. B., & Üstoğlu, İ. (2017). Introduction to optimization. In Optimization in renewable energy systems (pp. 27–74). Butterworth-Heinemann.
Fister, I., Yang, X. S., & Brest, J. (2013). A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation, 13, 34–46.
Francisco, R. B., Costa, M. F. P., & Rocha, A. M. A. C. (2014) Experiments with firefly algorithm. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), (Vol. 8580 LNCS, no. PART 2, pp. 227–236).
Gandomi, A. H., Yang, X. S., Talatahari, S., & Alavi, A. H. (2013). Firefly algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation, 18(1), 89–98.
Glybovets, M. M., & Gulayeva, N. M. (2017). Evolutionary multimodal optimization. In Optimization Methods and Applications (pp. 137–181). Cham : Springer.
Goel, R., & Maini, R. (2018). A hybrid of ant colony and firefly algorithms (HAFA) for solving vehicle routing problems. Journal of Computational Science, 25, 28–37.
Guo, Q., Wu, W., Massart, D. L., Boucon, C., & De Jong, S. (2002). Feature selection in principal component analysis of analytical data. Chemometrics and Intelligent Laboratory Systems, 61, 123–132.
Harrag, A. (2015). Nature-inspired feature subset selection application to arabic speaker recognition system. The International Journal of Speech Technology., 18(2), 245–255.
He, L., & Huang, S. (2017). Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing, 240, 152–174.
Ikeguchi, T., Hasegawa, M., Kimura, T., Matsuura, T., & Aihara, K. (2011). Theory and applications of chaotic optimization methods. Studies in Computing Intelligence, 357(1), 131–161.
Jitca, D., Teodorescu, H. N., Apopei, V., & Grigoras, F. (2002). Improved speech synthesis using fuzzy methods. International Journal of Speech Technology, 5(3), 227–235.
Johari, N. F., Zain, A. M., Mustaffa, N. H., & Udin, A. (2013). Firefly algorithm for optimization problem. Applied Mechanics and Materials, 421, 512–517.
Joseph Manoj, R., Anto Praveena, M. D., & Vijayakumar, K. (2019). An ACO–ANN based feature selection algorithm for big data. Cluster Computing, 22, 3953–3960.
Kadam, V. J., Jadhav, S. M., & Vijayakumar, K. (2019). Breast cancer diagnosis using feature ensemble learning based on stacked sparse autoencoders and softmax regression. Journal of Medical Systems, 43(8), 263.
Kahya, M. A., Altamir, S. A., & Algamal, Z. Y. (2019). Improving firefly algorithm-based logistic regression for feature selection. The Journal of Interdisciplinary Mathematics, 22(8), 1577–1581.
Kanungo, A., Mittal, M., & Dewan, L. (2020). Wavelet based PID controller using GA optimization and scheduling for feedback systems. The Journal of Interdisciplinary Mathematics, 23(1), 145–152.
Kumar, S., Sharma, B., Sharma, V. K., & Poonia, R. C. (2018). Automated soil prediction using bag-of-features and chaotic spider monkey optimization algorithm. Evolutionary Intelligence.
Langari, R. K., Sardar, S., Amin Mousavi, S. A., & Radfar, R. (2020). Combined fuzzy clustering and firefly algorithm for privacy preserving in social networks. Expert Systems with Applications, 141, 112968.
Liu, C., Tian, Y., Zhang, Q., Yuan, J., & Xue, B. (2013). Adaptive firefly optimization algorithm based on stochastic inertia weight. Proccedings of the Sixth International Conference on Computational Intelligence, 1(1), 334–337.
Long, N. C., & Meesad, P. (2014). An optimal design for type-2 fuzzy logic system using hybrid of chaos firefly algorithm and genetic algorithm and its application to sea level prediction. Journal of Intelligence Fuzzy Systems, 27(3), 1335–1346.
Long, N. C., Meesad, P., & Unger, H. (2015). A highly accurate firefly based algorithm for heart disease prediction. Expert Systems with Applications, 42(21), 8221–8231.
Lu, S., & Wang, X. (2016). Modeling the fuzzy cold storage problem and its solution by a discrete firefly algorithm. Journal of Intelligent & Fuzzy Systems, 31(4), 2431–2440.
Memari, A., Ahmad, R., Jokar, M. R. A., & Rahim, A. R. A. (2018). A new modified firefly algorithm for optimizing a supply chain network problem. Applied Sciences, 9(1), 1–12.
Moazenzadeh, R., Mohammadi, B., Shamshirband, S., & Chau, K. W. (2018). Coupling a firefly algorithm with support vector regression to predict evaporation in northern iran. Engineering Applications of Computational Fluid and Mechanisms, 12(1), 584–597.
Mohanty, D. K. (2016). Application of firefly algorithm for design optimization of a shell and tube heat exchanger from economic point of view. International Journal of Thermal Sciences, 102, 228–238.
Nalluri, M. S. R., Kannan, K., Manisha, M., & Roy, D. S. (2017). Hybrid disease diagnosis using multi-objective optimization with evolutionary parameter optimization. Journal of Healthcare Engineering.
Nguyen, T. T., Quynh, N. V., & Van Dai, L. (2018). Improved firefly algorithm: A novel method for optimal operation of thermal generating units. Complexity, 2018.
Opf, T. Chapter-4 application of firefly algorithm 4.1. 34–99.
Osuna-Enciso, V., Cuevas, E., & Sossa, H. (2013). A comparison of nature inspired algorithms for multi-threshold image segmentation. Expert Systems with Applications, 40(4), 1213–1219.
Pant, M. (2018). A brief overview of fire fly algorithm. In soft computing: Theories and applications (pp. 727–738). Singapore: Springer.
Pap, I. A., Oniga, S., Orha, I., & Alexan, A. (2018). IoT-based eHealth data acquisition system. In 2018 IEEE international conference on automation, quality and testing, robotics (AQTR) (pp. 1–5). IEEE.
Ritthipakdee, A., Thammano, A., Premasathian, N., & Jitkongchuen, D. (2017) Firefly mating algorithm for continuous optimization problems. Computational Intelligence and Neuroscience, 2017.
Saemi, B., Asghar, A., & Hosseinabadi, R. (2016). Nature inspired partitioning clustering algorithms : A review and analysis. In International workshop soft computing applications (pp. 96–116). Cham: Springer.
Senthilnath, J., Omkar, S. N., & Mani, V. (2011). Clustering using firefly algorithm: Performance study. Swarm and Evolutionary Computation, 1(3), 164–171.
Sharma, H., Bansal, J. C., & Arya, K. V. (2012). Fitness based Differential Evolution. Memetic Computing, 4(4), 303–316.
Sharma, R., & Saha, A. (2020). Identification of critical test paths using firefly algorithm for object oriented software. The Journal of Interdisciplinary Mathematics, 23(1), 191–203.
Shung, K. P. (2018). Accuracy, precision, recall or F1?|by Koo Ping Shung. Towards Data Science.
Shunmugapriya, P., & Kanmani, S. (2017). A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid). Swarm and Evolutionary Computation., 36, 27–36.
Singh, N. K., & Mahajan, V. (2020). Detection of cyber cascade failure in smart grid substation using advance grey wolf optimization. Journal of Interdisciplinary Mathematics, 23(1), 69–79.
Sinha, R. K., & Sahu, S. S. (2019). Adaptive firefly algorithm based optimized key generation for image security. Journal of Intelligent & Fuzzy Systems, 36(5), 4437–4447.
Sivaranjani, P., & Senthil Kumar, A. (2017). Hybrid Particle Swarm Optimization-Firefly algorithm (HPSOFF) for combinatorial optimization of non-slicing VLSI floorplanning. Journal of Intelligent & Fuzzy Systems, 32(1), 661–669.
Slavakis, K., Giannakis, G. B., & Mateos, G. (2014). Modeling and optimization for big data analytics: (Statistical) learning tools for our era of data deluge. IEEE Signal Processing Magazine., 31(5), 18–31.
Song, F., Guo, Z., & Mei, D. (2010). Feature selection using principal component analysis. In 2010 international conference on system science, engineering design and manufacturing informatization (Vol. 1, pp. 27–30).
Song, Z., Niu, D., Qiu, J., Xiao, X., & Ma, T. (2016). Improved short-term load forecasting based on EEMD, Guassian disturbance firefly algorithm and support vector machine. Journal of Intelligent & Fuzzy Systems, 31(3), 1709–1719.
Sorna Keerthi, R., & Meena Alias Jeyanthi, K. (2018). Effortless trellis coded firefly optimized LMMSE based channel estimation for LTE-Advanced downlink. Journal of Intelligent & Fuzzy Systems, 34(6), 4331–4344.
Strumberger, I., Bacanin, N., & Tuba, M. (2017). Enhanced firefly algorithm for constrained numerical optimization. In 2017 IEEE congress on evolutionary computation (CEC) (pp. 2120–2127). IEEE.
Tai, A. M. Y., et al. (2019). Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry. Artificial Intelligence in Medicine., 99, 101704.
Tawhid, M. A., & Ali, A. F. (2018). An effective hybrid firefly algorithm with the cuckoo search for engineering optimization problems. Foundations of Computational Mathematics, 1(4), 349–368.
Technology, I., Tun, U., Onn, H., Tun, U., & Onn, H. (2018). corrected Un Author Proof roof Author Pcted Un.
Tilahun, S. L., & Ngnotchouye, J. M. T. (2017). Firefly algorithm for discrete optimization problems: A survey. KSCE Journal of Civil Engineeering, 21(2), 535–545.
Vijayakumar, K., & Arun, C. (2017). Analysis and selection of risk assessment frameworks for cloud based enterprise applications., Biomedicine Research, vol. 2017, no. Special Issue ArtificialIntelligentTechniquesforBioMedicalSignalProcessingEdition-I, pp. S129–S136.
Vijayakumar, K., & Arun, C. (2019). Continuous security assessment of cloud based applications using distributed hashing algorithm in SDLC. Cluster Computing, 22, 10789–10800.
Wahid, F., & Ghazali, R. (2019). Hybrid of firefly algorithm and pattern search for solving optimization problems. Evolutionary Intelligence, 12(1).
Wahid, F., Ghazali, R., & Shah, H. (2018). An improved hybrid firefly algorithm for solving optimization problems. Advances in Intelligent Systems and Computing, 700, 14–23.
Wang, G. G., Deb, S., & Coelho, L. D. S. (2016). Elephant herding optimization. In 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI) (pp. 1–5). IEEE..
Wang, H., et al. (2017a). Firefly algorithm with adaptive control parameters. Soft Computing, 21(17), 5091–5102.
Wang, H., et al. (2017b). Firefly algorithm with neighborhood attraction. Information Science, 382–383, 374–387.
Wang, Z., Huang, L. & He, C. X. (2019). A multi-objective and multi-period optimization model for urban healthcare waste ’ s reverse logistics network design. Journal of Combinatorial Optimization.
X. Yang. (2017). Studies in computational intelligence 744 nature-inspired algorithms and applied optimization.
Yang, X. S. (2014). Preface. Studies in Computational Intelligence, 585, v–vi.
Yang, X. S., & He, X. (2013). Firefly algorithm: Recent advances and applications. International Journal of Swarm Intelligence, 1(1), 36.
Yang, X. S., & Karamanoglu, M. (2013). Swarm intelligence and bio-inspired computation: An overview. Swarm Intelligence Bio-Inspired Computing, 3–23.
Yerigeri, V. V., & Ragha, L. K. (2019). Meta-heuristic approach in neural network for stress detection in Marathi speech. International Journal of Speech Technology, 22(4), 937–957.
Zhang, L., Liu, L., Yang, X. S., & Dai, Y. (2016). A novel hybrid firefly algorithm for global optimization. PLoS ONE, 11(9), 1–17.
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Rahul, K., Banyal, R.K. Firefly algorithm: an optimization solution in big data processing for the healthcare and engineering sector. Int J Speech Technol 24, 581–592 (2021). https://doi.org/10.1007/s10772-020-09783-y
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DOI: https://doi.org/10.1007/s10772-020-09783-y