Comparison of Two Metaheuristic Algorithms on Sizing and Topology Optimization of Trusses and Mathematical Functions

Year 2018, Volume: 31 Issue: 2, 416 - 435, 01.06.2018

Ali Mortazavi , Vedat Toğan , Ayhan Nuhoğlu

https://izlik.org/JA85YH27FN

Abstract

Optimum
solution of an anticipated problem is generally reached through minimizing or
maximizing a governing real function while sometimes should satisfy various predefined
limitations. Selecting an algorithm as a main optimizer plays a key role on the
solution process. In this respect, current study intends to compare the
performances of two different common metaheuristic optimization algorithms as
integrated particle swarm optimizer (iPSO) and teaching and learning based optimizer
(TLBO). The TLBO is two-phase algorithm while the iPSO is a single-phase
algorithm. Their capabilities are compared over some benchmark cases including
mathematical functions and structural optimization problems. To increase the
complexity of the test problems both size and topology specifications of the
structural systems are simultaneously taken as the decision variables. Achieved
results demonstrate the superiority of the iPSO in comparison with TLBO in both
search capability and convergence rate.

Keywords

size and topology optimization , particle swarm optimization , teaching learning based optimization

References

• [1] Mortazavi, A. and Toğan, V., "Simultaneous size, shape, and topology optimization of truss structures using integrated particle swarm optimizer", Structural and Multidisciplinary Optimization, 54: 715-736, (2016).
• [2] Dede, T. and Togan, V., "A teaching learning based optimization for truss structures with frequency constraints", Structural Engineering and Mechanics, 53: 833-845, (2015).
• [3] Mortazavi, A. and Togan, V., "Particle Swarm Optimizer integrated with fly-back mechanism and weighted particle for optimization of the truss structures", 11st International congress on advances in civil engineering, Istanbul, Turkey, (2014).
• [4] Miguel, L.F.F., Lopez, R.H. and Miguel, L.F.F., "Multimodal size, shape, and topology optimisation of truss structures using the Firefly algorithm", Advances in Engineering Software, 56: 23-37, (2013).
• [5] Sonmez, M., "Discrete optimum design of truss structures using artificial bee colony algorithm", Structural and Multidisciplinary Optimization, 43: 85-97, (2011).
• [6] Aydoğdu, İ., Akın, A. and Saka, M.P., "Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution", Advances in Engineering Software, 92: 1-14, (2016).
• [7] Lamberti, L., "An efficient simulated annealing algorithm for design optimization of truss structures", Computers & Structures, 86: 1936-1953, (2008).
• [8] Hasançebi, O., "Adaptive evolution strategies in structural optimization: Enhancing their computational performance with applications to large-scale structures", Computers & Structures, 86: 119-132, (2008).
• [9] Camp, C., "Design of Space Trusses Using Big Bang–Big Crunch Optimization", Journal of Structural Engineering, 133: 999-1008, (2007).
• [10] Achtziger, W. and Stolpe, M., "Global optimization of truss topology with discrete bar areas—Part II: Implementation and numerical results", Computational Optimization and Applications, 44: 315-341, (2007).
• [11] Camp, C. and Bichon, B., "Design of Space Trusses Using Ant Colony Optimization", Journal of Structural Engineering, 130: 741-751, (2004).
• [12] Noilublao, N. and Bureerat, S., "Simultaneous Topology, Shape, and Sizing Optimisation of Plane Trusses with Adaptive Ground Finite Elements Using MOEAs", Mathematical Problems in Engineering, 2013: 9, (2013).
• [13] Kennedy, J. and Eberhart, R., "Particle swarm optimization", Neural Networks, 1995. Proceedings., IEEE International Conference on, 4: 1942-1948 vol.1944, (1995).
• [14] Mortazavi, A. and Toğan, V., "Simultaneous size, shape, and topology optimization of truss structures using integrated particle swarm optimizer", Structural and Multidisciplinary Optimization, 1-22, (2016).
• [15] Rao, R.V., Savsani, V.J. and Vakharia, D.P., "Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems", Computer-Aided Design, 43: 303-315, (2011).
• [16] Camp, C.V. and Farshchin, M., "Design of space trusses using modified teaching–learning based optimization", Engineering Structures, 62–63: 87-97, (2014).
• [17] Lim, W.H. and Mat Isa, N.A., "An adaptive two-layer particle swarm optimization with elitist learning strategy", Information Sciences, 273: 49-72, (2014).
• [18] Li, N.-J., Wang, W.-J., James Hsu, C.-C., Chang, W., Chou, H.-G. and Chang, J.-W., "Enhanced particle swarm optimizer incorporating a weighted particle", Neurocomputing, 124: 218-227, (2014).
• [19] Fan, Q. and Yan, X., "Self-adaptive particle swarm optimization with multiple velocity strategies and its application for p-Xylene oxidation reaction process optimization", Chemometrics and Intelligent Laboratory Systems, 139: 15-25, (2014).
• [20] Elsayed, S.M., Sarker, R.A. and Mezura-Montes, E., "Self-adaptive mix of particle swarm methodologies for constrained optimization", Information Sciences, 277: 216-233, (2014).
• [21] He, S., Prempain, E. and Wu, Q.H., "An improved particle swarm optimizer for mechanical design optimization problems", Engineering Optimization, 36: 585-605, (2004).
• [22] Degertekin, S.O. and Hayalioglu, M.S., "Sizing truss structures using teaching-learning-based optimization", Computers & Structures, 119: 177-188, (2013).
• [23] Toğan, V., "Design of planar steel frames using Teaching–Learning Based Optimization", Engineering Structures, 34: 225-232, (2012).
• [24] Deb, K. and Gulati, S., "Design of truss-structures for minimum weight using genetic algorithms", Finite Elements in Analysis and Design, 37: 447-465, (2001).
• [25] Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y., Auger, A. and Tiwari, S., "Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization", Technical Report, Nanyang Technological University, Singapore, May 2005 AND KanGAL Report 2005005, IIT Kanpur, India, (2005).
• [26] Tang, K., Li, Z., Luo, L. and Liu, B., "Multi-strategy adaptive particle swarm optimization for numerical optimization", Engineering Applications of Artificial Intelligence, 37: 9-19, (2015).
• [27] Hajela, P. and Lee, E., "Genetic algorithms in truss topological optimization", International Journal of Solids and Structures, 32: 3341-3357, (1995).
• [28] Ringertz, U.T., "On topology optimization of trusses", Engineering Optimization, 9: 209-218, (1985).