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Progress in Artificial Intelligence
Published in: Progress in Artificial Intelligence 4/2022

03-09-2022 | Regular Paper

A novel chaotic flower pollination algorithm for modelling an optimized low-complexity neural network-based NAV predictor model

Authors: Smita Mohanty, Rajashree Dash

Published in: Progress in Artificial Intelligence | Issue 4/2022

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Abstract

Investment instruments for structured investments include mutual funds, and the net asset value (NAV) is used to calculate their value. Due to uncertainty and influences from economic and political factors, it is challenging to predict such complex financial series. The study developed a model to predict NAV by using a low-complexity neural network, the Legendre polynomial neural network (LPNN). Moreover, a new chaotic flower pollination algorithm (NCHFPA) was developed to adjust the unknown parameters of the network through the learning process. NCHFPA is a fusion of chaos-based meta-heuristics with differentiated evolution (DE) algorithm in the local pollination phase of flower pollination algorithm (FPA). In order to determine the best variant of NCHFPA, five different chaotic functions have been investigated in three control parameters. The model was enhanced by integrating the natural evolution features from DE and the pollination process from FPA along with chaos theory. Three real-time mutual fund data sets of reputed Indian financial firms Aditya Birla (AB), SBI and ICICI were used to test this proposed LPNN-NCHFPA model. In order to verify and validate the predictor model further, a comparative analysis is performed with other optimization algorithms such as FPA, Chaotic FPA, particle swarm optimization (PSO) and DE. The proposed framework exhibits an improved performance of 36.65%, 28.22%, 20.10% and 17.18% in RMSE over LPNN-PSO, LPNN-DE, LPNN-FPA and LPNN-CHFPA, respectively, for AB mutual fund. For SBI, an improvement of 46.88%, 32.31%, 18.77% and 6.05% in RMSE and for ICICI, an improvement of 28.87%, 24.58%, 15.63% and 10.05% in RMSE are reported over LPNN-PSO, LPNN-DE, LPNN-FPA and LPNN-CHFPA, respectively, which clearly reveal the competency of the proposed framework over other experimented models.
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Literature
1.
Guo, X., Li, J.: A novel twitter sentiment analysis model with baseline correlation for financial market prediction with improved efficiency. In: 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 472–477. IEEE (2019).
2.
Mohanty, S., Dash, R.: Application of computational intelligence techniques in the domain of net asset value prediction: a survey. In: Intelligent and Cloud Computing, pp. 573–580 (2021)
3.
Precup, R.E., Preitl, S., Petriu, E., Bojan-Dragos, C.A., Szedlak-Stinean, A.I., Roman, R.C., Hedrea, E.L.: Model-based fuzzy control results for networked control systems. Rep. Mech. Eng. 1(1), 10–25 (2020)CrossRef
4.
Stojčić, M., Stjepanović, A., Stjepanović, Đ: ANFIS model for the prediction of generated electricity of photovoltaic modules. Decis. Mak. Appl. Manag. Eng. 2(1), 35–48 (2019)CrossRef
5.
Chiang, W.C., Urban, T.L., Baldridge, G.: A neural network fund net asset approach to mutual value forecasting. Omega 24(2), 205–215 (1996)CrossRef
6.
Lin, H.S., Chen, M.L., Tong, C.C., Dai, J.W.: Using grey and RBFNN to predict the net asset value of single nation equity funds-a case study of Taiwan, US, and Japan. In: 2007 IEEE international conference on grey systems and intelligent services. IEEE, pp 892–897 (2007)
7.
Yan, H., Liu, W., Liu, X., Kong, H., Lv, C.: Predicting net asset value of investment fund based on BP neural network. In: 2010 international conference on computer application and system modelling (ICCASM 2010), vol. 10. IEEE, pp V10–635 (2010)
8.
Priyadarshini, E., Babu, A.C.: A comparative analysis for forecasting the NAV’s of indian mutual fund using multiple regression analysis and artificial neural networks. Int. J. Trade Econ. Finance 3(5), 347–350 (2012)CrossRef
9.
Priyadarshini, E.: A comparative analysis of prediction using Artificial Neural network and auto regressive integrated moving average. ARPN J. Eng. Appl. Sci. 10(7), 3078–3081 (2015)
10.
Narula, A., Jha, C.B., Panda, G.: Development and performance evaluation of three novel prediction models for mutual fund NAV prediction. Ann. Res. J Symbiosis Centre Manag. Stud. 3, 227–238 (2015)
11.
Anish, C.M., Majhi, B.: Net asset value prediction using FLANN model. Int. J. Sci. Res. 4(2), 2222–2227 (2015)
12.
Hota, S., Pati, S.P., Satapathy, P.: Forecasting of net asset value of Indian mutual funds using firefly algorithm-based neural network model. Lecture Notes NetwSyst 151, 217–224 (2021)
13.
Mohanty, S., Dash, R.: A flower pollination algorithm based Chebyshev polynomial neural network for net asset value prediction. Evol. Intell. 2021, 1–17 (2021)
14.
Anish, C.M., Majhi, B., Majhi, R.: Development and evaluation of novel forecasting adaptive ensemble model. J. Finance Data Sci. 2(3), 188–201 (2016)CrossRef
15.
Rout, M., Koudjonou, K.M., Satapathy, S.C.: Analysis of net asset value prediction using low complexity neural network with various expansion techniques. Evol. Intell. 14, 643–655 (2021)CrossRef
16.
Patra, J.C., Chin, W.C., Meher, P.K., Chakraborty, G.: Legendre-FLANN-based nonlinear channel equalization in wireless communication system. In: 2008 IEEE International Conference on Systems, Man and Cybernetics, pp. 1826–1831 (2008).
17.
George, N.V., Panda, G.: A reduced complexity adaptive Legendre neural network for nonlinear active noise control. In: 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 560–563 (2012)
18.
Rout, A.K., Dash, P.K., Dash, R., Bisoi, R.: Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach. J. King Saud Univ. Comput. Inf. Sci. 29(4), 536–552 (2017)
19.
Dash, R., Dash, P.K.: Prediction of financial time series data using hybrid evolutionary Legendre neural network: evolutionary LENN. Int. J. Appl. Evol. Comput. 7(1), 16–32 (2016)CrossRef
20.
Mall, S., Chakraverty, S.: Application of Legendre neural network for solving ordinary differential equations. Appl. Soft Comput. 43, 347–356 (2016)CrossRef
21.
Dash, R., Dash, P.K.: MDHS–LPNN: a hybrid FOREX predictor model using a Legendre polynomial neural network with a modified differential harmony search technique. In: Handbook of Neural Computation, 1st edn, Chapter 25, 459–486 (2017)
22.
Dash, R.: Performance analysis of an evolutionary recurrent Legendre Polynomial Neural Network in application to FOREX prediction. J. King Saud Univ. Comput. Inf. Sci. 32(9), 1000–1011 (2020)
23.
Nanda, S.J., Panda, G.: A survey on nature inspired metaheuristic algorithms for partitional clustering, Swarm. Evol. Comput. 16, 1–18 (2014)CrossRef
24.
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, vol. 1, pp. 39–43 (1995)
25.
Karaboga, D., Basturk, B.: Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: International Fuzzy Systems Association World Congress. Springer, Berlin, Heidelberg, pp. 789–798 (2007)
26.
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRef
27.
Yang, X.S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010)CrossRef
28.
Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Springer, Berlin. Heidelberg, pp. 65–74 (2010)CrossRef
29.
Rechenberg, I.: Evolutions strategien. In: Simulations Methoden in der Medizin und Biologie. Springer, Berlin Heidelberg, pp. 83–114 (1978)
30.
31.
Storn, R., Price, K.: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces, vol. 3. Berkeley: ICSI (1995)
32.
Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15, 4–31 (2011)CrossRef
33.
Sa, A.A.R., Andrade, A.O., Soares, A.B., Nasuto, S.J.: Exploration vs. exploitation in differential evolution, 1st–4th April, 2008, University of Aberdeen, Scotland. In: Proceedings of the AISB 2008 Symposium on Swarm Intelligence Algorithms and Application, vol. 11, pp. 57–63 (2008)
34.
Crepinsek, M., Liu, S.-H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. 45, 1–33 (2013)MATHCrossRef
35.
Yang, X.S.: Flower pollination algorithm for global optimization. In: International Conference on Unconventional Computing and Natural Computation. Springer, Berlin, Heidelberg, pp. 240–249 (2012)CrossRef
36.
Singh, U., Salgotra, R.: Synthesis of linear antenna array using flower pollination algorithm. Neural Comput. Appl. 2016, 1–11 (2016)
37.
Prathiba, R., Moses, M.B., Sakthivel, S.: Flower pollination algorithm applied for different economic load dispatch problems. Int. J. Eng. Technol. 6(2), 1009–1016 (2014)
38.
Emary, E., Zawbaa, H.M., Hassanien, A.E., Parv, B.: Multiobjective retinal vessel localization using flower pollination search algorithm with pattern search. Adv. Data Anal. Classif. 11(3), 611–627 (2017)MathSciNetMATHCrossRef
39.
Draa, A.: On the performances of the flower pollination algorithm–Qualitative and quantitative analyses. Appl. Soft Comput. 34, 349–371 (2015)CrossRef
40.
Hoang, N.D., Bui, D.T., Liao, K.W.: Groutability estimation of grouting processes with cement grouts using differential flower pollination optimized support vector machine. Appl. Soft Comput. 45, 173–186 (2016)CrossRef
41.
Liu, H., Abraham, A., Clerc, M.: Chaotic dynamic characteristics in swarm intelligence. Appl. Soft Comput. 7(3), 1019–1026 (2007)CrossRef
42.
Zhang, W.J., Xie, X.F.: DEPSO: hybrid particle swarm with differential evolution operator. In: SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme-System Security and Assurance (Cat. No. 03CH37483), vol. 4. IEEE, pp. 3816–3821 (2003, October)
43.
Musigawan, P., Chiewchanwattana, S., Sunat, K.: Improved differential evolution via cuckoo search operator. In: International Conference on Neural Information Processing. Springer, Berlin, Heidelberg, pp. 465–472 (2012, November).
44.
Abdullah, A., Deris, S., Mohamad, M.S., Hashim, S.Z. M.: A new hybrid firefly algorithm for complex and nonlinear problem. In: Distributed Computing and Artificial Intelligence. Springer, Berlin, Heidelberg, pp. 673–680 (2012)
45.
Chakraborty, D., Saha, S., Dutta, O.: DE-FPA: a hybrid differential evolution-flower pollination algorithm for function minimization. In: 2014 International Conference on High Performance Computing and Applications (ICHPCA). IEEE, pp. 1–6 (2014, December)
46.
Abdel-Raouf, O., Abdel-Baset, M., El-henawy, I.: An improved flower pollination algorithm with chaos. Int. J. Educ. Manag. Eng. 2, 1–8 (2014)
47.
Metwalli, M., Abdel-Baset, M., Hezam, I.: A modified flower pollination algorithm for fractional programming problems. Int. J. Intell. Syst. Appl. Eng. 3(3), 116–123 (2015)CrossRef
48.
Arora, S., Anand, P.: Chaos-enhanced flower pollination algorithms for global optimization. J. Intell. Fuzzy Syst. 33(6), 3853–3869 (2017)CrossRef
49.
Pauline, O., Meng, O.K., Kiong, S.C.: An improved flower pollination algorithm with chaos theory for function optimization. In: AIP Conference Proceedings, vol. 1870, No. 1. AIP Publishing LLC, p. 050012 (2017, August)
50.
Kaur, A., Pal, S.K., Singh, A.P.: New chaotic flower pollination algorithm for unconstrained non-linear optimization functions. Int. J. Syst. Assur. Eng. Manag. 9(4), 853–865 (2018)CrossRef
51.
Meng, O.K., Pauline, O., Kiong, S.C., Wahab, H.A., Jafferi, N.: Application of modified flower pollination algorithm on mechanical engineering design problem. In: IOP Conference Series: Materials Science and Engineering, vol. 165, No. 1. IOP Publishing, p. 012032 (2017).
52.
Yousri, D., AbdelAty, A.M., Said, L.A., Elwakil, A.S., Maundy, B., Radwan, A.G.: Chaotic flower pollination and grey wolf algorithms for parameter extraction of bio-impedance models. Appl. Soft Comput. 75, 750–774 (2019)CrossRef
53.
Kaur, A., Pal, S.K., Singh, A.P.: Hybridization of chaos and flower pollination algorithm over K-means for data clustering. Appl. Soft Comput. 97, 105523 (2020)CrossRef
54.
Yousri, D., Babu, T.S., Allam, D., Ramachandaramurthy, V.K., Etiba, M.B.: A novel chaotic flower pollination algorithm for global maximum power point tracking for photovoltaic system under partial shading conditions. IEEE Access 7, 121432–121445 (2019)CrossRef
55.
Yousri, D., Allam, D., Babu, T.S., AbdelAty, A.M., Radwan, A.G., Ramachandaramurthy, V.K., Eteiba, M.B.: Fractional chaos maps with flower pollination algorithm for chaotic systems’ parameters identification. Neural Comput. Appl. 32(20), 16291–16327 (2020)CrossRef
56.
Ozsoydan, F.B., Baykasoglu, A.: Chaos and intensification enhanced flower pollination algorithm to solve mechanical design and unconstrained function optimization problems. Expert Syst. Appl. 184, 115496 (2021)CrossRef
57.
Zhou, Y., Zhang, S., Luo, Q., Wen, C.: Using flower pollination algorithm and atomic potential function for shape matching. Neural Comput. Appl. 29(6), 21–40 (2018)CrossRef
58.
Yang, X.S., Karamanoglu, M., He, X.: Multi-objective flower algorithm for optimization. Procedia Comput. Sci. 18, 861–868 (2013)CrossRef
59.
Abdel-Raouf, O., Abdel-Baset, M., El-henawy, I.: A new hybrid flower pollination algorithm for solving constrained global optimization problems. Adv. Eng. Technol. Appl. 3(2), 1–9 (2014)
60.
Gämperle, R., Müller, S.D., Koumoutsakos, P.: A parameter study for differential evolution. In: Proceedings of the WSEAS International Conferences on Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, Interlaken, Switzerland, February 11–15, 2002 (2002)
Metadata
Title
A novel chaotic flower pollination algorithm for modelling an optimized low-complexity neural network-based NAV predictor model
Authors
Smita Mohanty
Rajashree Dash
Publication date
03-09-2022
Publisher
Springer Berlin Heidelberg
Published in
Progress in Artificial Intelligence / Issue 4/2022
Print ISSN: 2192-6352
Electronic ISSN: 2192-6360
DOI
https://doi.org/10.1007/s13748-022-00289-z

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