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
Talbi, E.-G.: Metaheuristics: from Design to Implementation, vol. 74. John Wiley (2009)
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)
Holland, J.H.: genetic algorithms. Sci. Am. 267(1), 66–73 (1992)
Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)
Javidy, B., Hatamlou, A., Mirjalili, S.: Ions motion algorithm for solving optimization problems. Appl. Soft Comput. 32, 72–79 (2015)
Kaveh, A., Dadras, A.: A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv. Eng. Softw. 110, 69–84 (2017)
Abedinpourshotorban, H., et al.: Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol. Comput. 26, 8–22 (2016)
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)
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)
Rahmanzadeh, S., Pishvaee, M.S.: Electron radar search algorithm: a novel developed meta-heuristic algorithm. Soft Comput., 1–23 (2019)
Zou, Y.: The whirlpool algorithm based on physical phenomenon for solving optimization problems. Eng. Comput. (2019)
Jain, M., Singh, V., Rani, A.: A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm Evol. Comput. 44, 148–175 (2019)
Mirjalili, S.: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89, 228–249 (2015)
Karaboga, D., Basturk, B.: Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. Found. Fuzzy Logic Soft Comput., 789–798 (2007)
Kennedy: Particle swarm optimization. Neural Netw. (1995)
Yazdani, M., Jolai, F.: Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J. Comput. Des. Eng. 3(1), 24–36 (2016)
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)
Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)
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)
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)
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)
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)
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)
Fong, S., Deb, S., Chaudhary, A.: A review of metaheuristics in robotics. Comput. Electr. Eng. 43, 278–291 (2015)
Hassan, M., Yousif, A.: Cloud job scheduling with ions motion optimization algorithm. Eng. Technol. Appl. Sci. Res. 10(2), 5459–5465 (2020)
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)
Kamboj, V.K.: A novel hybrid PSO–GWO approach for unit commitment problem. Neural Comput. Appl. 27(6), 1643–1655 (2016)
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
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)
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)
Kaveh, A., Bakhshpoori, T., Afshari, E.: An efficient hybrid particle swarm and swallow swarm optimization algorithm. Comput. Struct. 143, 40–59 (2014)
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)
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
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)
Garg, H.: A hybrid PSO-GA algorithm for constrained optimization problems. Appl. Math. Comput. 274, 292–305 (2016)
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147(1), 195–197 (1981)
Xiong, J.: Essential Bioinformatics. Cambridge University Press (2006)
Di Francesco, V., Garnier, J., Munson, P.: Improving protein secondary structure prediction with aligned homologous sequences. Protein Sci. 5(1), 106–113 (1996)
Feng, D.-F., Doolittle, R.F.: Progressive alignment and phylogenetic tree construction of protein sequences. Methods Enzymol. 183, 375–387 (1990)
Li, L., Khuri, S.: A Comparison of DNA Fragment Assembly Algorithms. in METMBS (2004)
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)
Gotoh, O.: An improved algorithm for matching biological sequences. J. Mol. Biol. 162(3), 705–708 (1982)
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)
Harrag, A., Daili, Y.: Three-diodes PV model parameters extraction using PSO algorithm. Revue des Energies Renouvelables 22(1), 85–91 (2019)
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)
Hannan, M., et al.: Optimization techniques to enhance the performance of induction motor drives: a review. Renew. Sustain. Energy Rev. (2017)
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)
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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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