Structural Engineering and Mechanics

  • 제52권2호
  • /
  • Pages.405-426
  • /
  • 2014
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

A new hybrid meta-heuristic for structural design: ranked particles optimization

  • Kaveh, A. (Centre of Excellence for Fundamental Studies in Structural Engineering, School of Civil Engineering, Iran University of Science and Technology) ;
  • Nasrollahi, A. (Centre of Excellence for Fundamental Studies in Structural Engineering, School of Civil Engineering, Iran University of Science and Technology)
  • 투고 : 2013.03.25
  • 심사 : 2014.07.20
  • 발행 : 2014.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, a new meta-heuristic algorithm named Ranked Particles Optimization (RPO), is presented. This algorithm is not inspired from natural or physical phenomena. However, it is based on numerous researches in the field of meta-heuristic optimization algorithms. In this algorithm, like other meta-heuristic algorithms, optimization process starts with by producing a population of random solutions, Particles, located in the feasible search space. In the next step, cost functions corresponding to all random particles are evaluated and some of those having minimum cost functions are stored. These particles are ranked and their weighted average is calculated and named Ranked Center. New solutions are produced by moving each particle along its previous motion, the ranked center, and the best particle found thus far. The robustness of this algorithm is verified by solving some mathematical and structural optimization problems. Simplicity of implementation and reaching to desired solution are two main characteristics of this algorithm.

키워드

참고문헌

  1. American Institute of Steel Construction (AISC) (1989), Manual of steel construction-allowable stress design, 9th Edition, American Institute of Steel Construction, Chicago
  2. De Jong, K. (1975), "Analysis of the behavior of a class of genetic adaptive systems", Ph.D. Thesis, University of Michigan, Ann Arbor, MI.
  3. Dorigo, M., Maniezzo, V. and Colorni, A. (1996), "The ant system: optimization by a colony of cooperating agents", IEEE Tran. Syst. Man Cyber. 26(1), 29-41. https://doi.org/10.1109/3477.484436
  4. Eberhart, R.C. and Kennedy, J. (1995), "A new optimizer using particle swarm theory", Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan.
  5. Erol, O.K. and Eksin, I. (2006), "New optimization method: big bang-big crunch" Adv. Eng. Softw., 37, 106-111. https://doi.org/10.1016/j.advengsoft.2005.04.005
  6. Fogel, L.J., Owens, A.J. and Walsh, M.J. (1966), Artificial Intelligence Through Simulated Evolution, Wiley, Chichester.
  7. Geem, Z.W., Kim, J.H. and Loganathan, G.V. (2001), "A new heuristic optimization algorithm: harmony search" Simulation 76(2), 60-68. https://doi.org/10.1177/003754970107600201
  8. Goldberg, D.E. (1989), Genetic Algorithms in Search Optimization and Machine Learning, Addison-Wesley, Boston.
  9. Gholizadeh, S. and Barati, H. (2014) "Topology optimization of nonlinear single layer domes by a new metaheuristic", Steel Compos. Struct, 16(6), 681-701. https://doi.org/10.12989/scs.2014.16.6.681
  10. Hasancebi, O., Carbas, S., Dogan, E., Erdal, F. and Saka, M.P. (2009), "Performance evaluation of metaheuristic search techniques in the optimum design of real size pin jointed structures", Comput. Struct., 87, 284-302. https://doi.org/10.1016/j.compstruc.2009.01.002
  11. Holland, J.H. (1975), Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor.
  12. Kaveh, A. and Khayatazad, M. (2012), "A new meta-heuristic method: Ray Optimization", Comput. Struct., 112-113, 283-294. https://doi.org/10.1016/j.compstruc.2012.09.003
  13. Kaveh, A. and Mahdavai, VR. (2014), "Colliding bodies optimization: a novel meta-heuristic method", Comput. Struct., 139, 18-27. https://doi.org/10.1016/j.compstruc.2014.04.005
  14. Kaveh, A. and Nasrollahi, A. (2013), "Engineering design optimization using a hybrid PSO and HS algorithm", Asian J. Civil Eng., 14(2), 201-223.
  15. Kaveh, A. and Talatahari, S. (2009a), "Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures", Comput. Struct., 87(5-6), 267-283. https://doi.org/10.1016/j.compstruc.2009.01.003
  16. Kaveh, A. and Talatahari, S. (2009b), "Size optimization of space trusses using big bang-big crunch algorithm", Comput. Struct., 87(17-18), 1129-1140. https://doi.org/10.1016/j.compstruc.2009.04.011
  17. Kaveh, A. and Talatahari, S. (2010a), "A novel heuristic optimization method: charged system search", Acta Mech., 213(3-4), 267-289. https://doi.org/10.1007/s00707-009-0270-4
  18. Kaveh, A. and Talatahari, S. (2010b), "Optimal design of skeletal structures via the charged system search algorithm", Struct. Multidiscip. Optim., 41(6), 893-911. https://doi.org/10.1007/s00158-009-0462-5
  19. Kaveh, A., Motie Share, M.A. and Moslehi, M. (2013), "A new meta-heuristic algorithm for optimization: magnetic charged system search", Acta Mech., 224(1), 85-107 https://doi.org/10.1007/s00707-012-0745-6
  20. Kelesoglu, O. and Ulker, M. (2005), "Fuzzy optimization geometrical nonlinear space truss design", Turk. J. Eng. Environ. Sci., 29, 321-329.
  21. Kirkpatrick, S., Gelatt, C. and Vecchi, M. (1983), "Optimization by simulated annealing", Sci., 220(4598), 671-680. https://doi.org/10.1126/science.220.4598.671
  22. Koza, J.R. (1990), "Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems", Report No. STAN-CS-90-1314, Stanford University, Stanford, CA.
  23. Lee, K.S. and Geem, Z.W. (2004), "A new structural optimization method based on the harmony search algorithm", Comput. Struct., 82, 781-798. https://doi.org/10.1016/j.compstruc.2004.01.002
  24. Rashedi, E., Nezamabadi-pour, H. and Saryazdi, S. (2009), "GSA: a gravitational search algorithm", Inf. Sci., 179, 2232-2248. https://doi.org/10.1016/j.ins.2009.03.004
  25. Schutte, J.J. and Groenwold, A.A. (2003), "Sizing design of truss structures using particle swarms", Struct. Multidiscip. Optim., 25, 261-269. https://doi.org/10.1007/s00158-003-0316-5
  26. Tsoulos, I.G. (2008), "Modifications of real code genetic algorithm for global optimization", Appl. Math. Comput., 203, 598-607. https://doi.org/10.1016/j.amc.2008.05.005

피인용 문헌

  1. Application of Probabilistic Particle Swarm in Optimal Design of Large-Span Prestressed Concrete Slabs vol.40, pp.1, 2016, https://doi.org/10.1007/s40996-016-0005-4
  2. Grasshopper Optimisation Algorithm: Theory and application vol.105, 2017, https://doi.org/10.1016/j.advengsoft.2017.01.004
  3. Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning vol.2017, 2017, https://doi.org/10.1155/2017/9897078
  4. A Sparse Signal Reconstruction Method Based on Improved Double Chains Quantum Genetic Algorithm vol.9, pp.9, 2017, https://doi.org/10.3390/sym9090178
  5. Optimum shape of large-span trusses according to AISC-LRFD using Ranked Particles Optimization vol.134, 2017, https://doi.org/10.1016/j.jcsr.2017.03.021
  6. An automated approach for optimal design of prestressed concrete slabs using PSOHS vol.21, pp.3, 2017, https://doi.org/10.1007/s12205-016-1126-9
  7. Prediction of the Corrosion Current Density in Reinforced Concrete Using a Self-Organizing Feature Map vol.7, pp.10, 2017, https://doi.org/10.3390/coatings7100160
  8. Hybrid Metaheuristic-Neural Assessment of the Adhesion in Existing Cement Composites vol.7, pp.4, 2017, https://doi.org/10.3390/coatings7040049
  9. A New Radar Signal Recognition Method Based on Optimal Classification Atom and IDCQGA vol.10, pp.11, 2018, https://doi.org/10.3390/sym10110659
  10. Structural failure classification for reinforced concrete buildings using trained neural network based multi-objective genetic algorithm vol.63, pp.4, 2014, https://doi.org/10.12989/sem.2017.63.4.429
  11. A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space vol.4, pp.2, 2014, https://doi.org/10.33889/ijmems.2019.4.2-039