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Two Layer Hybrid Scheme of IMO and PSO for Optimization of Local Aligner: COVID-19 as a Case Study

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Artificial Intelligence for COVID-19

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 358))

Abstract

Nowadays, meta-heuristic algorithm (MA) succeeded in optimizing many engineering problems. Ions motion optimization (IMO) algorithm is a MA that inspired its search strategy from ions attraction based on force law. IMO has good exploration capability but poor exploitation of the search space. The performance of IMO was tested for implementing fragmented local aligner technique (FLAT) which is a local aligner method for finding the longest common consecutive subsequence (LCCS) between pair of biological sequences. Due to the huge length of sequences FLAT based on IMO produce poor results due to the poor exploitation which need to be enhanced by adding particle swarm optimization (PSO) algorithm which has efficient exploitation capability. The enhanced version of IMO (IMO-PSO)was merged as two layer (bottom layer for exploration using IMO and the upper layer exploit the best solution founded from the bottom layer). This hybrid scheme increase the diversity of solutions which increase the quality of solutions. FLAT based on IMO-PSO was tested on real biological sequences gathered from NCBI versus IMO and the standard local alignment algorithm. Besides, COVID-19 was analyzed against other viruses to detect the LCCS between it. FLAT based on IMO-PSO produced an enhancement of the performance of IMO for finding LCCS between biological sequences.

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References

  1. Talbi, E.-G.: Metaheuristics: from Design to Implementation, vol. 74. John Wiley (2009)

    Google Scholar 

  2. Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  3. Holland, J.H.: genetic algorithms. Sci. Am. 267(1), 66–73 (1992)

    Article  Google Scholar 

  4. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)

    Article  Google Scholar 

  5. Javidy, B., Hatamlou, A., Mirjalili, S.: Ions motion algorithm for solving optimization problems. Appl. Soft Comput. 32, 72–79 (2015)

    Article  Google Scholar 

  6. Kaveh, A., Dadras, A.: A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv. Eng. Softw. 110, 69–84 (2017)

    Article  Google Scholar 

  7. Abedinpourshotorban, H., et al.: Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol. Comput. 26, 8–22 (2016)

    Article  Google Scholar 

  8. Nematollahi, A.F., Rahiminejad, A., Vahidi, B.: A novel physical based meta-heuristic optimization method known as lightning attachment procedure optimization. Appl. Soft Comput. 59, 596–621 (2017)

    Article  Google Scholar 

  9. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)

    Article  Google Scholar 

  10. Rahmanzadeh, S., Pishvaee, M.S.: Electron radar search algorithm: a novel developed meta-heuristic algorithm. Soft Comput., 1–23 (2019)

    Google Scholar 

  11. Zou, Y.: The whirlpool algorithm based on physical phenomenon for solving optimization problems. Eng. Comput. (2019)

    Google Scholar 

  12. Jain, M., Singh, V., Rani, A.: A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm Evol. Comput. 44, 148–175 (2019)

    Article  Google Scholar 

  13. Mirjalili, S.: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89, 228–249 (2015)

    Article  Google Scholar 

  14. Karaboga, D., Basturk, B.: Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. Found. Fuzzy Logic Soft Comput., 789–798 (2007)

    Google Scholar 

  15. Kennedy: Particle swarm optimization. Neural Netw. (1995)

    Google Scholar 

  16. Yazdani, M., Jolai, F.: Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J. Comput. Des. Eng. 3(1), 24–36 (2016)

    Google Scholar 

  17. Lamy, J.-B.: Artificial Feeding Birds (AFB): a new metaheuristic inspired by the behavior of pigeons. Advances in Nature-Inspired Computing and Applications, pp. 43–60. Springer, New York (2019)

    Chapter  Google Scholar 

  18. Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)

    Article  Google Scholar 

  19. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

  20. Wang, M.-J., et al.: A load economic dispatch based on ion motion optimization algorithm. Advances in Intelligent Information Hiding and Multimedia Signal Processing, pp. 115–125. Springer, New York (2020)

    Google Scholar 

  21. Das, S., Bhattacharya, A., Chakraborty, A.K.: Quasi-reflected ions motion optimization algorithm for short-term hydrothermal scheduling. Neural Comput. Appl. 29(6), 123–149 (2018)

    Article  Google Scholar 

  22. Yang, C.-H., Wu, K.-C., Chuang, L.-Y.: Breast cancer risk prediction using ions motion optimization algorithm. J. Life Sci. Technol. 4(2), 49–55 (2016)

    Google Scholar 

  23. Mohapatra, G., Debnath, M.K., Mohapatra, K.K.: IMO based novel adaptive dual-mode controller design for AGC investigation in different types of systems. Cogent Eng. (just-accepted), 1711675 (2020)

    Google Scholar 

  24. Yang, C.-H., et al.: Protein folding prediction in the HP model using ions motion optimization with a greedy algorithm. BioData mining 11(1), 17 (2018)

    Article  Google Scholar 

  25. Fong, S., Deb, S., Chaudhary, A.: A review of metaheuristics in robotics. Comput. Electr. Eng. 43, 278–291 (2015)

    Article  Google Scholar 

  26. Hassan, M., Yousif, A.: Cloud job‎ scheduling with‎ ions motion optimization algorithm. Eng. Technol. Appl. Sci. Res. 10(2), 5459–5465 (2020)

    Article  Google Scholar 

  27. Issa, M., et al.: ASCA-PSO: adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment. Expert Syst. Appl. 99, 56–70 (2018)

    Article  Google Scholar 

  28. Kamboj, V.K.: A novel hybrid PSO–GWO approach for unit commitment problem. Neural Comput. Appl. 27(6), 1643–1655 (2016)

    Article  Google Scholar 

  29. 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), 2003. IEEE

    Google Scholar 

  30. Shen, Q., Shi, W.-M., Kong, W.: Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data. Comput. Biol. Chem. 32(1), 53–60 (2008)

    Article  Google Scholar 

  31. Jiang, S., Ji, Z., Shen, Y.: A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints. Int. J. Electr. Power Energy Syst. 55, 628–644 (2014)

    Article  Google Scholar 

  32. Kaveh, A., Bakhshpoori, T., Afshari, E.: An efficient hybrid particle swarm and swallow swarm optimization algorithm. Comput. Struct. 143, 40–59 (2014)

    Article  Google Scholar 

  33. Abd-Elazim, S., Ali, E.: A hybrid particle swarm optimization and bacterial foraging for optimal power system stabilizers design. Int. J. Electr. Power Energy Syst. 46, 334–341 (2013)

    Article  Google Scholar 

  34. Holden, N., Freitas, A.A.: A hybrid particle swarm/ant colony algorithm for the classification of hierarchical biological data. In: Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005, 2005. IEEE

    Google Scholar 

  35. Pan, T.-S., Dao, T.-K., Chu, S.-C.: Hybrid particle swarm optimization with bat algorithm. Genetic and Evolutionary Computing, pp. 37–47. Springer, New York (2015)

    Chapter  Google Scholar 

  36. Garg, H.: A hybrid PSO-GA algorithm for constrained optimization problems. Appl. Math. Comput. 274, 292–305 (2016)

    MathSciNet  MATH  Google Scholar 

  37. Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147(1), 195–197 (1981)

    Article  Google Scholar 

  38. Xiong, J.: Essential Bioinformatics. Cambridge University Press (2006)

    Google Scholar 

  39. Di Francesco, V., Garnier, J., Munson, P.: Improving protein secondary structure prediction with aligned homologous sequences. Protein Sci. 5(1), 106–113 (1996)

    Article  Google Scholar 

  40. Feng, D.-F., Doolittle, R.F.: Progressive alignment and phylogenetic tree construction of protein sequences. Methods Enzymol. 183, 375–387 (1990)

    Article  Google Scholar 

  41. Li, L., Khuri, S.: A Comparison of DNA Fragment Assembly Algorithms. in METMBS (2004)

    Google Scholar 

  42. Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)

    Article  Google Scholar 

  43. Gotoh, O.: An improved algorithm for matching biological sequences. J. Mol. Biol. 162(3), 705–708 (1982)

    Article  Google Scholar 

  44. Khanna, V., et al.: Estimation of photovoltaic cells model parameters using particle swarm optimization. Physics of Semiconductor Devices, pp. 391–394. Springer, New York (2014)

    Chapter  Google Scholar 

  45. Harrag, A., Daili, Y.: Three-diodes PV model parameters extraction using PSO algorithm. Revue des Energies Renouvelables 22(1), 85–91 (2019)

    Google Scholar 

  46. Ishaque, K., et al.: An improved particle swarm optimization (PSO)–based MPPT for PV with reduced steady-state oscillation. IEEE Trans. Power Electron. 27(8), 3627–3638 (2012)

    Article  Google Scholar 

  47. Hannan, M., et al.: Optimization techniques to enhance the performance of induction motor drives: a review. Renew. Sustain. Energy Rev. (2017)

    Google Scholar 

  48. Wang, W., et al.: A universal index and an improved PSO algorithm for optimal pose selection in kinematic calibration of a novel surgical robot. Robot. Comput.-Integr. Manuf. 50, 90–101 (2018)

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer and Systems Engineering Department, Faculty of Engineering, Zagazig University, Zazig, Egypt

    Mohamed Issa & Ahmed Helmi

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  1. Mohamed Issa
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  2. Ahmed Helmi
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Correspondence to Mohamed Issa .

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Editors and Affiliations

  1. Department of Computer Sciences, University of Guadalajara, Guadalajara, Jalisco, Mexico

    Diego Oliva

  2. Operations Research and Decision Support Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Said Ali Hassan

  3. Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt

    Ali Mohamed

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Issa, M., Helmi, A. (2021). Two Layer Hybrid Scheme of IMO and PSO for Optimization of Local Aligner: COVID-19 as a Case Study. In: Oliva, D., Hassan, S.A., Mohamed, A. (eds) Artificial Intelligence for COVID-19. Studies in Systems, Decision and Control, vol 358. Springer, Cham. https://doi.org/10.1007/978-3-030-69744-0_21

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