Abstract
Time evolutionary optimization (TEO) is a novel population-based meta-heuristic optimization algorithm, inspired by natural selection and evolution of creatures over time. Time and the environment are two main factors of evolution at TEO. In this paper, enhanced time evolutionary optimization (ETEO) is presented. ETEO is the new version of TEO which modifies time evolutionary factor and applied population clustering. Population clustering amplified environmental factor to increase the efficiency of ETEO. For this purpose, a memory is used to save some best designs and ETEO can escape from local optimal points. The algorithm was validated by solving several constraint benchmarks and engineering design problems. The comparison results between the proposed algorithm and other metaheuristic methods contain TEO, indicate the ETEO is competitive with them, and in some cases superior to, other available heuristic methods in terms of the efficiency, faster convergence rate, robustness of finding final solution and requires a smaller number of function evaluations for solving constrained engineering problems.
Similar content being viewed by others
References
Ghoddosian A, Sheikhi M (2013) Metaheuristic optimization algorithm in engineering. Semnan University, Semnan
Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley, Boston
Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern 26(1):29–41
Sheikhi M, Ghoddosian A (2013) A hybrid imperialist competitive ant colony algorithm for optimum geometry design of frame structures. Struct Eng Mech 46(3):403–416
Mirzaei Z, Akbarpour A, Khatibinia M, Khashei Siuki A (2015) Optimal design of homogeneous earth dams by particle swarm optimization incorporating support vector machine approach. Int J Geomech Eng 9(6):709–727
Kaveh A, Zakian P (2018) Improved GWO algorithm for optimal of truss structures. Eng Comput 34(4):685–707
Kaveh A, Farhoudi N (2013) A new optimization method: Dolphin echolocation. Adv Eng Softw 59:53–70
Arjmand M, Sheikhi Azqandi M, Delavar M (2018) Hybrid improved dolphin echolocation and ant colony optimization for optimal discrete sizing of truss structures. J Rehabil Civ Eng 6(1):74–89
Riyahi Vezvari M, Ghoddosian A, Nikoobin A (2018) Numbers cup optimization: a new method for optimization problems. Struct Eng Mech 66(4):465–476
Senel FA, Gokce F, Yukcel AS, Yigit T (2018) A novel hybrid PSO-GWO algorithm for optimization problems. Eng Comput. https://doi.org/10.1007/s00366-018-0668-5
Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112:283–294. https://doi.org/10.1016/j.compstruc.2012.09.003
Kaveh A, Mahdavi VR (2014) Colliding bodies optimization method for optimum design of truss structures with continuous variables. Adv Eng Softw 70:1–12
Kaveh A, Ilchi Ghazaan M (2014) Enhanced colliding bodies optimization for design problems with continuous and discrete variables. Adv Eng Softw 77:66–75
Kaveh A, Ilchi Ghazaan M (2017) A new meta-heuristic algorithm: vibrating particles system. Sci Iran 24(2):551–566. https://doi.org/10.24200/sci.2017.2417
Sheikhi M, Delavar M, Arjmand M (2016) Time evolutionary optimization: a new meta-heuristic optimization algorithm. In: Proceedings of the 4th international congress on civil engineering, architecture and urban development, Shahid Beheshti University, Tehran, Iran
Kaveh A (2014) Ray optimization algorithm. Advances in metaheuristic algorithms for optimal design of structures, book chapter. Springer, Cham, pp 233–276
Tsoulos IG (2008) Modifications of real code genetic algorithm for global optimization. Appl Math Comput 203:598–607
Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10:629–640
Becerra R, Coello CAC (2006) Cultured differential evolution for constrained optimization. Comput Methods Appl Mech Eng 195:4303–4322
Homaifar A, Qi CX, Lai SH (1994) Constrained optimization via genetic algorithms. Simulation 62:242–253
Wang L, Li LP (2010) An effective differential evolution with level comparison for constrained engineering design. Struct Multidiscip Optim 41:947–963
Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inform Sci 178:3043–3074
Zavala AEM, Aguirre AH (2005) Diharce, constrained optimization via evolutionary swarm optimization algorithm (PESO). Proc Conf Genet Evol Comput 2005:209–216
Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110:151–166
Rao RV, Patel V (2012) An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. Int J Ind Eng Comput 3:535–560
Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13:2592–2612
Lampinen J (2002) A constraint handling approach for the differential evolution algorithm. IEEE Trans Evol Comput 2002:1468–1473
Zahara EY, Kao T (2009) Hybrid Nelder–Mead simplex search and particle swarm optimization for constrained engineering design problems. Expert Syst Appl 36:3880–3886
Mezura-Montes E, Coello CAC (2005) A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Trans Evol Comput 9:1–17
Wang Y, Cai Z, Zhou Y, Fan Z (2009) Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint handling technique. Struct Multidiscip Optim 37:395–413
Runarsson TP, Xin Y (2005) Search biases in constrained evolutionary optimization. IEEE Trans Syst Man Cybern Part C Appl Rev 35:233–243
Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems, LNAI, vol 4529. Springer, Berlin, pp 789–798
Coello CAC, Becerra RL (2004) Efficient evolutionary optimization through the use of a cultural algorithm. Eng Optim 36:219–236
Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Method Appl Mech Eng 194:3902–3933
Amirjanov A (2006) The development of a changing range genetic algorithm. Comput Methods Appl Mech Eng 195:2495–2508
Michalewicz Z (1995) Genetic algorithms, numerical optimization, and constraints. In: Esheman L (ed) Proceedings of the sixth international conference on genetic algorithms. San Mateo: Morgan Kauffman, pp 151–8
Tessema B, Yen GG (2006) A self adaptive penalty function based algorithm for constrained optimization. IEEE Trans Evol Comput 2006:246–253
Runarsson TP, Xin Y (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Trans Evol Comput 4:284–294
Renato AK, Dos Santos LC (2006) Coevolutionary particle swarm optimization using gaussian distribution for solving constrained optimization problems. IEEE Trans Syst Man Cybern Part B Cybern 36:1407–1416
Huang FZ, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186(1):340–356
Ray T, Liew KM (2003) Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans Evol Comput 7:386–396
Parsopoulos K, Vrahatis M (2005) Unified particle swarm optimization for solving constrained engineering optimization problems, vol 3612. Springer, Berlin, pp 582–591
Mezura-Montes E, Velazquez-Reyes J, Coello CAC (2006) Modified differential evolution for constrained optimization. Evol Comput CEC 2006, IEEE Congress, pp 25–32
Kaveh A, Zolghadr A (2016) A novel meta-heuristic algorithm: tug of war optimization. Int J Optim Civ Eng 6(4):469–492
Acknowledgements
The authors would like to acknowledge the financial support of Bozorgmehr University of Qaenat for this research under contract number 39144.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix 1
1.1 Constrained problem 1
Subject to:
1.2 Constrained problem 2
Subject to:
1.3 Constrained problem 3
Subject to:
1.4 Constrained problem 4
Subject to:
Appendix 2
2.1 Three-bar truss design problem
Subject to
2.2 Pressure vessel design problem
Subject to:
2.3 Speed reducer design problem
Subject to:
where
2.4 Tension/compression spring design problem
Subject to:
2.5 Welded beam design problem
Subject to:
where
Rights and permissions
About this article
Cite this article
Sheikhi Azqandi, M., Delavar, M. & Arjmand, M. An enhanced time evolutionary optimization for solving engineering design problems. Engineering with Computers 36, 763–781 (2020). https://doi.org/10.1007/s00366-019-00729-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00366-019-00729-w