Advertisement

Synergistic fibroblast optimization: a novel nature-inspired computing algorithm

  • T T Dhivyaprabha
  • P Subashini
  • M Krishnaveni
Article
First Online:
  • 37 Downloads

Abstract

The evolutionary algorithm, a subset of computational intelligence techniques, is a generic population-based stochastic optimization algorithm which uses a mechanism motivated by biological concepts. Bio-inspired computing can implement successful optimization methods and adaptation approaches, which are inspired by the natural evolution and collective behavior observed in species, respectively. Although all the meta-heuristic algorithms have different inspirational sources, their objective is to find the optimum (minimum or maximum), which is problem-specific. We propose and evaluate a novel synergistic fibroblast optimization (SFO) algorithm, which exhibits the behavior of a fibroblast cellular organism in the dermal wound-healing process. Various characteristics of benchmark suites are applied to validate the robustness, reliability, generalization, and comprehensibility of SFO in diverse and complex situations. The encouraging results suggest that the collaborative and self-adaptive behaviors of fibroblasts have intellectually found the optimum solution with several different features that can improve the effectiveness of optimization strategies for solving non-linear complicated problems.

Key words

Synergistic fibroblast optimization (SFO) Fitness analysis Convergence Benchmark suite Monk’s dataset 

CLC number

TP18 R329.2 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Balouek–Thomert D, Bhattacharya AK, Caron E, et al., 2016. Parallel differential evolution approach for cloud workflow placements under simultaneous optimization of multiple objectives. Proc IEEE Congress on Evolutionary Computation, p.822–829.  https://doi.org/10.1109/CEC.2016.7743876 Google Scholar
  2. Banerjee S, Bharadwaj A, Gupta D, et al., 2012. Remote sensing image classification using artificial bee colony algorithm. Int J Comput Sci Inform, 2(3):2231–5292Google Scholar
  3. Chen GHG, Rockafellar RT, 1997. Convergence rates in forward–backward splitting. SIAM J Optim, 7(2):421–444.  https://doi.org/10.1137/S1052623495290179 MathSciNetCrossRefzbMATHGoogle Scholar
  4. Chuang LY, Chang HW, Tu CJ, et al., 2008. Improved binary PSO for feature selection using gene expression data. Comput Biol Chem, 32(1):29–38.  https://doi.org/10.1016/j.compbiolchem.2007.09.005 CrossRefzbMATHGoogle Scholar
  5. Colaço MJ, Dulikravich GS, 2009. A survey of basic deterministic, heuristic and hybrid methods for single objective optimization and response surface generation. In: Orlande HRB, Fudym O, Maillet D, et al. (Eds.), Thermal Measurements and Inverse Techniques. CRC Press, Boca Raton, p.355–406.Google Scholar
  6. Cruz C, González JR, Pelta DA, 2011. Optimization in dynamic environments: a survey on problems, methods and measures. Soft Comput, 15(7):1427–1448.  https://doi.org/10.1007/s00500-010-0681-0 CrossRefGoogle Scholar
  7. Dallon J, Sherratt J, Maini P, et al., 2000. Biological implications of a discrete mathematical model for collagen deposition and alignment in dermal wound repair. Math Med Biol, 17(4):379–393.  https://doi.org/10.1093/imammb/17.4.379 CrossRefzbMATHGoogle Scholar
  8. Das S, Suganthan PN, 2011. Differential evolution: a survey of the state–of–the–art. IEEE Trans Evol Comput, 15(1): 4–31.  https://doi.org/10.1109/TEVC.2010.2059031 CrossRefGoogle Scholar
  9. Das S, Abraham A, Konar A, 2008. Automatic clustering using an improved differential evolution algorithm. IEEE Trans Syst Man Cybern A, 38(1):218–237.  https://doi.org/10.1109/TSMCA.2007.909595 CrossRefGoogle Scholar
  10. Derrac J, García S, Hui S, et al., 2013. Statistical analysis of convergence performance throughout the evolutionary search: a case study with SaDE–MMTS and Sa–EPSDEMMTS. Proc IEEE Symp on Differential Evolution, p.151–156.  https://doi.org/10.1109/SDE.2013.6601455 Google Scholar
  11. Dhivyaprabha TT, Subashini P, Krishnaveni M, 2016. Computational intelligence based machine learning methods for rule–based reasoning in computer vision applications. Proc IEEE Symp Series on Computational Intelligence, p.1–8.  https://doi.org/10.1109/SSCI.2016.7850050 Google Scholar
  12. DiMilla PA, Barbee K, Lauffenburger DA, 1991. Mathematical model for the effects of adhesion and mechanics on cell migration speed. Biophys J, 60(1):15–37.  https://doi.org/10.1016/S0006-3495(91)82027-6 CrossRefGoogle Scholar
  13. Eberhart R, Kenndy J, 1995. A new optimizer using particle swarm theory. IEEE 6th Int Symp on Micro Machine and Human Science, p.39–43.  https://doi.org/10.1109/MHS.1995.494215 Google Scholar
  14. Eberhart R, Shi YH, 2001. Particle swarm optimization: developments, applications and resources. Proc Congress on Evolutionary Computation, p.81–86.  https://doi.org/10.1109/CEC.2001.934374 Google Scholar
  15. Goldbarg EFG, de Souza GR, Goldbarg MC, 2006. Particle swarm for the traveling salesman problem. Proc 6th Evolutionary Computation in Combinatorial Optimization, p.99–110.  https://doi.org/10.1007/11730095_9 Google Scholar
  16. Gupta S, Bhardwaj S, 2013. Rule discovery for binary classification problem using ACO based antminer. Int J Comput Appl, 74(7):19–23.  https://doi.org/10.5120/12898-9806 Google Scholar
  17. He J, Lin GM, 2016. Average convergence rate of evolutionary algorithms. IEEE Trans Evol Comput, 20(2):316–321.  https://doi.org/10.1109/TEVC.2015.2444793 CrossRefGoogle Scholar
  18. Herrera F, Lozano M, Molina D, 2009. Test Suite for the Special Issue of Soft Computing on Scalability of Evolutionary Algorithms and Other Metaheuristics for Large Scale Continuous Optimization Problems. Technical Report, University of Granada, Spain.Google Scholar
  19. Izakian H, Ladani BT, Abraham A, et al., 2010. A discrete particle swarm approach for grid job scheduling. Int J Innov Comput Inform Contr, 6(9):1–15.Google Scholar
  20. Jamil M, Yang XS, 2013. A literature survey of benchmark functions for global optimisation problems. Int J Math Model Numer Optim, 4(2):150–194.  https://doi.org/10.1504/IJMMNO.2013.055204 zbMATHGoogle Scholar
  21. Jana ND, Hati AN, Darbar R, et al., 2013. Real parameter optimization using Levy distributed differential evolution. Proc 5th Int Conf on Pattern Recognition and Machine Intelligence, p.605–613.  https://doi.org/10.1007/978-3-642-45062-4_85 CrossRefGoogle Scholar
  22. Khaparde AR, Raghuwanshi MM, Malik LG, 2016. Empirical analysis of differential evolution algorithm with rotational mutation operator. Int J Latest Trends Eng Technol, 6(3):170–176.Google Scholar
  23. Khoshnevisan B, Rafiee S, Omid M, et al., 2015. Developing a fuzzy clustering model for better energy use in farm management systems. Renew Sustain Energy Rev, 48(3): 27–34.  https://doi.org/10.1016/j.rser.2015.03.029 CrossRefGoogle Scholar
  24. Krishnaveni M, Subashini P, Dhivyaprabha TT, 2016. A new optimization approach—SFO for denoising digital images. Proc Int Conf on Computation System and Information Technology for sustainable Solutions, p.34–39.  https://doi.org/10.1109/CSITSS.2016.7779436 CrossRefGoogle Scholar
  25. Levey AS, Eckardt KU, Tsukamoto Y, et al., 2005. Definition and classification of chronic kidney disease: a position statement from kidney disease: improving global outcomes (KDIGO). Kidn Int, 67(6):2089–2100.  https://doi.org/10.1111/j.1523-1755.2005.00365.x CrossRefGoogle Scholar
  26. Liang JJ, Qu BY, Suganthan PN, 2013. Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real–Parameter Numerical Optimization. Technical Report 201 311, Zhengzhou University, Zhengzhou, and Nanyang Technological University, Singapore.Google Scholar
  27. Marrow P, 2000. Nature–inspired computing technology and applications. BT Technol J, 18(4):13–23.  https://doi.org/10.1023/A:1026746406754 CrossRefGoogle Scholar
  28. McCaffrey JD, 2012. Simulated protozoa optimization. Proc 13th Int Conf on Information Reuse & Integration, p.179–184.  https://doi.org/10.1109/IRI.2012.6303008 Google Scholar
  29. McDougall S, Dallon J, Sherratt J, et al., 2006. Fibroblast migration and collagen deposition during dermal wound healing: mathematical modelling and clinical implications. Phil Trans Royal Soc A, 364(1843):1385–1405.  https://doi.org/10.1098/rsta.2006.1773 MathSciNetCrossRefGoogle Scholar
  30. Mo HW, 2012. Ubiquity Symposium: evolutionary computation and the processes of life: evolutionary computation as a direction in nature–inspired computing. Ubiquity, 2012:1–9.  https://doi.org/10.1145/2390009.2390011 CrossRefGoogle Scholar
  31. Niu B, Zhu YL, He XX, et al., 2007. MCPSO: a multi–swarm cooperative particle swarm optimizer. Appl Math Comput, 185(2):1050–1062.  https://doi.org/10.1016/j.amc.2006.07.026 zbMATHGoogle Scholar
  32. Poli R, Kennedy J, Blackwell T, 2007. Particle swarm optimization: an overview. Swarm Intell, 1(1):33–57.  https://doi.org/10.1007/s11721-007-0002-0 CrossRefGoogle Scholar
  33. Pooranian Z, Shojafar M, Abawajy JH, et al., 2015. An efficient meta–heuristic algorithm for grid computing. J Comb Optim, 30(3):413–434.  https://doi.org/10.1007/s10878-013-9644-6 MathSciNetCrossRefzbMATHGoogle Scholar
  34. Qin AK, Huang VL, Suganthan PN, 2009. Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput, 13(2): 398–417.  https://doi.org/10.1109/TEVC.2008.927706 CrossRefGoogle Scholar
  35. Rajam SP, Balakrishnan G, 2012. Recognition of Tamil sign language alphabet using image processing to aid deafdumb people. Proc Eng, 30:861–868.  https://doi.org/10.1016/j.proeng.2012.01.938 CrossRefGoogle Scholar
  36. Rodemann HP, Rennekampff HO, 2011. Functional diversity of fibroblasts. In: Mueller MM, Fusenig NE (Eds.), Tumor–Associated Fibroblasts and Their Matrix. Springer, Dordrecht, p.23–36.  https://doi.org/10.1007/978-94-007-0659-0_2
  37. Sajjadi S, Shamshirband S, Alizamir M, et al., 2016. Extreme learning machine for prediction of heat load in district heating systems. J Energy Build, 122:222–227.  https://doi.org/10.1016/j.enbuild.2016.04.021 CrossRefGoogle Scholar
  38. Sedighizadeh D, Masehian E, 2009. Particle swarm optimization methods, taxonomy and applications. Int J Comput Theor Eng, 1(5):486–502.  https://doi.org/10.7763/IJCTE.2009.V1.80 CrossRefGoogle Scholar
  39. Shamekhi A, 2013. An improved differential evolution optimization algorithm. Int J Res Rev Appl Sci, 15(2): 132–145.MathSciNetGoogle Scholar
  40. Snáel V, Krömer P, Abraham A, 2013. Particle swarm optimization with protozoic behaviour. Proc IEEE Int Conf on Systems, Man, and Cybernetics, p.2026–2030.  https://doi.org/10.1109/SMC.2013.347 Google Scholar
  41. Stebbings H, 2001. Cell Motility. Encyclopedia of Life Sciences. Encyclopedia of Life Sciences. Nature Publishing Group, London.CrossRefGoogle Scholar
  42. Storn R, 2008. Differential evolution research—trends and open questions. In: Chakraborty UK (Ed.), Advances in Differential Evolution. Article 143. Springer, Berlin.  https://doi.org/10.1007/978-3-540-68830-3_1 CrossRefGoogle Scholar
  43. Storn R, Price K, 1997. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim, 11(4):341–359.  https://doi.org/10.1023/A:1008202821328. MathSciNetCrossRefzbMATHGoogle Scholar
  44. Subashini P, Dhivyaprabha TT, Krishnaveni M, 2017. Synergistic fibroblast optimization. Proc Artificial Intelligence and Evolutionary Computations in Engineering Systems, p.285–294.  https://doi.org/10.1007/978-981-10-3174-8_25 CrossRefGoogle Scholar
  45. Tanweer MR, Suresh S, Sundararajan N, 2015. Self regulating particle swarm optimization algorithm. Inform Sci, 294: 182–202.  https://doi.org/10.1016/j.ins.2014.09.053 MathSciNetCrossRefzbMATHGoogle Scholar
  46. Tanweer MR, Al–Dujaili A, Suresh S, 2016. Empirical assessment of human learning principles inspired PSO algorithms on continuous black–box optimization testbed. Proc 6th Int Conf on Swarm, Evolutionary, and Memetic Computing, p.17–28.  https://doi.org/10.1007/978-3-319-48959-9_2 CrossRefGoogle Scholar
  47. van den Bergh F, Engelbrecht AP, 2006. A study of particle swarm optimization particle trajectories. J Inform Sci, 176(8):937–971.  https://doi.org/10.1016/j.ins.2005.02.003 MathSciNetCrossRefzbMATHGoogle Scholar
  48. Wan X, Wang WQ, Liu JM, et al., 2014. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol, 14(1):1–13.  https://doi.org/10.1186/1471-2288-14-135 CrossRefGoogle Scholar
  49. Xu L, Bai JN, Li LM, 2015. Brain CT image classification based on improving harmony search algorithm optimize LSSVM. Metal Min Ind, 9:781–787Google Scholar
  50. Zhang K, Zhu WY, Liu J, et al., 2015. Discrete particle swarm optimization algorithm for solving graph coloring problem. Proc 10th Int Conf Bio–inspired Computing—Theories and Applications, p.643–652.  https://doi.org/10.1007/978-3-662-49014-3_57 CrossRefGoogle Scholar
  51. Zhao HB, Feng LN, 2014. An improved adaptive dynamic particle swarm optimization algorithm. J Netw, 9(2): 488–494.MathSciNetGoogle Scholar
  52. Zhao SK, 2009. Performance Analysis and Enhancements of Adaptive Algorithms and Their Applications. PhD Thesis, Nanyang Technological University, Singapore.Google Scholar
  53. Zou DX, Gao LQ, Li S, et al., 2011. Solving 0–1 knapsack problem by a novel global harmony search algorithm. Appl Soft Comput, 11(2):1556–1564.  https://doi.org/10.1016/j.asoc.2010.07.019 CrossRefGoogle Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceAvinashilingam Institute for Home Science and Higher Education for WomenCoimbatoreIndia

Personalised recommendations

Cite article

Buy options