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Human behavior-based optimization: a novel metaheuristic approach to solve complex optimization problems

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Abstract

Optimization techniques, specially evolutionary algorithms, have been widely used for solving various scientific and engineering optimization problems because of their flexibility and simplicity. In this paper, a novel metaheuristic optimization method, namely human behavior-based optimization (HBBO), is presented. Despite many of the optimization algorithms that use nature as the principal source of inspiration, HBBO uses the human behavior as the main source of inspiration. In this paper, first some human behaviors that are needed to understand the algorithm are discussed and after that it is shown that how it can be used for solving the practical optimization problems. HBBO is capable of solving many types of optimization problems such as high-dimensional multimodal functions, which have multiple local minima, and unimodal functions. In order to demonstrate the performance of HBBO, the proposed algorithm has been tested on a set of well-known benchmark functions and compared with other optimization algorithms. The results have been shown that this algorithm outperforms other optimization algorithms in terms of algorithm reliability, result accuracy and convergence speed.

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References

  1. Rao SS (2009) Engineering optimization: theory and practice, 4th edn. Wiley, Hoboken

    Book  Google Scholar 

  2. Holland JH (1975) Adaption in natural and artificial systems. Ann Arbor, MI

  3. Fogel DB (1995) Evolutionary computation: toward a new philosophy of machine intelligence. IEEE Press, New York

    MATH  Google Scholar 

  4. Fogel LJ, Owens AJ, Walsh MJ (1965) Artificial intelligence through a simulation of evolution. In: Proceedings of 2nd Cybern. Sci. Symp. Biophysics Cybern. Syst. Spartan Books, Washington, pp 131–155

  5. Schwefel HP (1995) Evolution and optimum seeking. Wiley, New York

    MATH  Google Scholar 

  6. Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B 26(1):29–41

    Article  Google Scholar 

  7. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Global Optim 39(3):459471

    Article  MathSciNet  MATH  Google Scholar 

  8. 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  Google Scholar 

  9. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural network, vol 4. IEEE Press, Piscataway, pp 1942–1948

  10. Kirkpatrick S, Gelatt C, Vecchi M (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  11. Erol OK, Eksin I (2006) A new optimization method: big bangbig crunch. Adv Eng Softw 37(2)

  12. Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213:267–289

    Article  MATH  Google Scholar 

  13. Kalamkarov AL, Kolpakov AG (1997) Analysis, design and optimization of composite structures, 2nd edn. Wiley, New York

    MATH  Google Scholar 

  14. Gurdal Z, Haftka RT, Hajela P (1998) Design and optimization of laminated composite materials. Wiley, New York

    Google Scholar 

  15. Jaluria Y (2007) Design and optimization of thermal systems, 2nd edn. CRC Press, Boca Raton

    Book  MATH  Google Scholar 

  16. Edgar TF, Himmelblau DM (1988) Optimization of chemical processes. McGraw-Hill, New York

    Google Scholar 

  17. Micheli GD (1994) Synthesis and optimization of digital circuits. McGraw-Hill, New York

    Google Scholar 

  18. Johnson RC (1980) Optimum design of mechanical elements. Wiley, New York

    Google Scholar 

  19. Ravindran A, Ragsdell KM, Reklaitis GV (2006) Engineering optimization: methods and applications, 2nd edn. Wiley, New York

    Book  Google Scholar 

  20. Brown JJ, Chen DZ, Greenwood GW, Hu RWTXS (1997) Scheduling for power reduction in a real-time system. In: International symposium on low power electronics and design. IEEE, Monterey, pp 84–87

  21. Surhone LM, Timpledon MT, Marseken SF (2010) Spherical coordinate system: vector fields in cylindrical and spherical coordinates, sphere, N-sphere, Cartesian coordinate system. Cylindrical Coordinate System, Betascript

    Google Scholar 

  22. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  23. Wolpert DH, Macready WG (1997) No free lunch theorems for search. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  24. He S, Wu QH, Saunders JR (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput 13(5):973–990

    Article  Google Scholar 

  25. Yao X, Liu Y, Liu G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Article  Google Scholar 

  26. Yao X, Liu Y (1997) Fast evolution strategies. In: Proceedings of evolution programming VI. Springer, Berlin, pp 151–161

  27. Clerc M, Kennedy J (2002) The particle swarmexplosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Article  Google Scholar 

  28. Montgomery DC (2012) Statistical quality control. Wiley, New York

    Google Scholar 

  29. Torn A, Zilinskas A (1989) Global optimisation. Lecture Notes in Computer Science. Springer, Berlin

  30. Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7–8):1867–1877. doi:10.1007/s00521-013-1433-8

    Article  Google Scholar 

  31. Mirjalili S, Mirjalili SM, Hatamlou A (2015) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl. doi:10.1007/s00521-015-1870-7

    Google Scholar 

  32. Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Appl 25:1077–1097. doi:10.1007/s00521-014-1597-x

    Article  Google Scholar 

  33. Bernstein D (2006) Optimization r us. IEEE Control Syst Mag 26:6–7

    Google Scholar 

  34. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

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Authors and Affiliations

  1. Electrical Engineering Department, Shahid Beheshti University, Tehran, Iran

    Seyed-Alireza Ahmadi

Authors
  1. Seyed-Alireza Ahmadi
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Correspondence to Seyed-Alireza Ahmadi.

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The author declares that he has no conflict of interest.

Appendices

Appendix 1

The software was used to generate the results of HBBO in this paper, and the implementation guides will be publicly available at http://a-ahmadi.com/hbbo.

Appendix 2

See Figs. 6, 7, 8, 9, 10, 11 and 12.

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3D view for function f 1 (sphere)

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3D view for function f 2 (Schwefel’s problem 2.22)

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3D view for function f 4 (Schwefel’s problem)

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3D view for function f 7 (quartic function with noise)

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3D view for function f 10 (Schwefel)

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3D view for function f 11 (rotated Ackley)

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Fig. 12
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3D view for function f 13 (generalized penalized)

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Ahmadi, SA. Human behavior-based optimization: a novel metaheuristic approach to solve complex optimization problems. Neural Comput & Applic 28 (Suppl 1), 233–244 (2017). https://doi.org/10.1007/s00521-016-2334-4

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  • DOI: https://doi.org/10.1007/s00521-016-2334-4

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