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Applications and analysis of bio-inspired eagle strategy for engineering optimization

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Abstract

All swarm-intelligence-based optimization algorithms use some stochastic components to increase the diversity of solutions during the search process. Such randomization is often represented in terms of random walks. However, it is not yet clear why some randomization techniques (and thus why some algorithms) may perform better than others for a given set of problems. In this work, we analyze these randomization methods in the context of nature-inspired algorithms. We also use eagle strategy to provide basic observations and relate step sizes and search efficiency using Markov theory. Then, we apply our analysis and observations to solve four design benchmarks, including the designs of a pressure vessel, a speed reducer, a PID controller, and a heat exchanger. Our results demonstrate that eagle strategy with Lévy flights can perform extremely well in reducing the overall computational efforts.

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References

  1. Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptural comparison. ACM Comput Surv 35:268–308

    Article  Google Scholar 

  2. Cagnina LC, Esquivel SC, Coello Coello CA (2008) Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica 32:319–326

    MATH  Google Scholar 

  3. Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2):311–338

    Article  MATH  Google Scholar 

  4. Ferrante N (2012) Diversity management in memetic algorithms. In: Handbook of memetic algorithms, studies in computational intelligence, vol 379. Springer, Berlin, pp 153–165

  5. Fishman GS (1995) Monte Carlo: concepts, algorithms and applications. Springer, New York

    Google Scholar 

  6. Fister I, Fister I Jr, Yang XS, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput (in press). doi:10.1016/j.swevo.2013.06.001

  7. Fister I, Yang XS, Brest J, Fister I Jr (2013) Modified firefly algorithm using quaternion representation. Expert Syst Appl 40(16):7220–7230

    Article  Google Scholar 

  8. Gamerman D (1997) Markov Chain Monte Carlo. Chapman & Hall/CRC

  9. Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845

    Article  MATH  MathSciNet  Google Scholar 

  10. Gandomi AH, Yun GJ, Yang XS, Talatahari S (2013) Chaos-enhanced accelerated particle swarm optimization. Commun Nonlinear Sci Numer Simul 18(2):327–340

    Article  MATH  MathSciNet  Google Scholar 

  11. Gandomi AH, Yang XS, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255

    Article  Google Scholar 

  12. Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

    Article  MathSciNet  Google Scholar 

  13. Geyer CJ (1992) Practical Markov Chain Monte Carlo. Stat Sci 7:473–511

    Article  Google Scholar 

  14. Ghate A, Smith R (2008) Adaptive search with stochastic acceptance probabilities for global optimization. Oper Res Lett 36:285–290

    Article  MATH  MathSciNet  Google Scholar 

  15. Gilks WR, Richardson S, Spiegelhalter DJ (1996) Markov Chain Monte Carlo in practice. Chapman & Hall/CRC

  16. Gutowski M (2001) Lévy flights as an underlying mechanism for global optimization algorithms. ArXiv Math Phys e-Prints

  17. Jaberipour M, Khorram E (2010) Two improved harmony search algorithms for solving engineering optimization problems. Commun Nonlinear Sci Numer Simulat 15(11):3316–3331

    Article  Google Scholar 

  18. Kirkpatrick S, Gellat CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:670–680

    Article  Google Scholar 

  19. Mantegna RN (1994) Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Phys Rev E 49:4677–4683

    Article  Google Scholar 

  20. Matlab® (2012) Control System Toolbox, R2012a, version 7.14

  21. Nolan JP (2009) Stable distributions: models for heavy-tailed data. American University

  22. Pavlyukevich I (2007) Lévy flights, non-local search and simulated annealing. J Comput Phys 226:1830–1844

    Article  MATH  MathSciNet  Google Scholar 

  23. Ramos-Fernandez G, Mateos JL, Miramontes O, Cocho G, Larralde H, Ayala-Orozco B (2004) Lévy walk patterns in the foraging movements of spider monkeys (Ateles geoffroyi). Behav Ecol Sociobiol 55:223–230

    Article  Google Scholar 

  24. Reynolds AM, Frye MA (2007) Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search. PLoS One 2:e354

    Article  Google Scholar 

  25. Reynolds AM, Rhodes CJ (2009) The Lévy flight paradigm: random search patterns and mechanisms. Ecology 90:877–887

    Article  Google Scholar 

  26. Ting TO, Lee TS (2012) Drilling optimization via particle swarm optimization. Int J Swarm Intell Res 1(2):42–53

    Google Scholar 

  27. Ting TO, Rao MVC, Loo CK (2006) A novel approach for unit commitment problem via an effective hybrid particle swarm optimization. IEEE Trans Power Syst 21(1):1–8

    Article  Google Scholar 

  28. Viswanathan GM, Buldyrev SV, Havlin S, da Luz MGE, Raposo EP, Stanley HE (1996) Lévy flight search patterns of wandering albatrosses. Nature 381:413–415

    Article  Google Scholar 

  29. Xue DY, Chen YQ, Atherton DP (2007) Linear feedback control. SIAM Publications, Philadelphia

    Book  MATH  Google Scholar 

  30. Yang XS (2008) Introduction to computational mathematics. World Scientific Publishing

  31. Yang XS (2008) Introduction to mathematical optimization: from linear programming to metaheuristics. Cambridge International Science Publishing, Cambridge

    Google Scholar 

  32. Yang XS (2011) Bat algorithm for multi-objective optimisation. Int J Bio-Inspired Comput 3(5):267–274

    Google Scholar 

  33. Yang XS (2011) Review of meta-heuristics and generalised evolutionary walk algorithm. Int J Bio-Inspired Comput 3(2):77–84

    Article  Google Scholar 

  34. Yang XS (2013) Multiobjective firefly algorithm for continuous optimization. Eng Comput 29(2):175–184

    Article  Google Scholar 

  35. Yang XS, Deb S (2011) Eagle strategy using Lévy walk and firefly algorithms for stochastic optimization. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, pp 101–111

  36. Yang XS, Deb S (2012) Two-stage eagle strategy with differential evolution. Int J Bio-Inspired Comput 4(1):1–5

    Article  MathSciNet  Google Scholar 

  37. Yang XS, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624

    Article  MathSciNet  Google Scholar 

  38. Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483

    Article  Google Scholar 

  39. Yang XS, Deb S, Fong S (2011) Accelerated particle swarm optimization and support vector machine for business optimization and applications. In: Networked digital technologies 2011, communications in computer and information science, 136. pp 53–66

  40. Yang XS, Ting TO, Karamanoglu M (2013) Random walks, Lévy flights, Markov chains and metaheuristic optimization. In: Future information communication technology and applications, vol. 235. Lecture notes in electrical engineering, pp 1055–1064

  41. Yang XS, Karamangolu M, He XS (2013) Multi-objective flower algorithm for optimization. Procedia Comput Sci 18:861–868

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. School of Science and Technology, Middlesex University, The Burroughs, London, NW4 4BT, UK

    Xin-She Yang & Mehmet Karamanoglu

  2. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu Province, People’s Republic of China

    T. O. Ting

  3. College of Automation, Harbin Engineering University, Harbin, People’s Republic of China

    Yu-Xin Zhao

Authors
  1. Xin-She Yang
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  2. Mehmet Karamanoglu
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  3. T. O. Ting
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  4. Yu-Xin Zhao
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Correspondence to Xin-She Yang.

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Yang, XS., Karamanoglu, M., Ting, T.O. et al. Applications and analysis of bio-inspired eagle strategy for engineering optimization. Neural Comput & Applic 25, 411–420 (2014). https://doi.org/10.1007/s00521-013-1508-6

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  • DOI: https://doi.org/10.1007/s00521-013-1508-6

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