Abstract
Stopping rule for multi-start local search is investigated for application to structural optimization problems with moderately large number of local optima. The size of attractor of an unknown local optimal solution is estimated using the information of already obtained solutions. A stopping rule is defined using the likelihood of obtaining the specific set of local optimal solutions. This way, characteristics of the specific optimization problem is successfully incorporated. The proposed rule is first verified using the mathematical problems in comparison with the existing rule utilizing the estimated ratio of the total size of attractors to the size of feasible domain. The rule is next applied to an optimization problem of a plane frame under constraints on inter-story drift angle and stress against static loads. Characteristics of the distribution of attractors of optimal solutions are also investigated.
Similar content being viewed by others
References
Boender CGE, Rinnooy Kan AHG (1987) Bayesian stopping rules for multistart global optimization methods. Math Program 37:59–80
Dorea CCY (1983) Expected number of steps of a random optimization method. J Optim Theory Appl 39 (2):165–171
Dorea CCY, Goncalves CR (1993) Alternative sampling strategy for a random optimization algorithm. J Optim Theory Appl 78(2):401–407
Hart WE (1998) Sequential stopping rules for random optimization methods with applications to multistart local search. SIAM J Optim 9(1):270–290
Lagaris IE, Tsoulos IG (2008) Stopping rules for box-constrained stochastic global optimization. Appl Math Comput 197:622–632
Le Riche R, Haftka RT (2012) On global optimization articles in SMO. Struct Multidisc Optim 46:627–629
Muselli M (1997) A theoretical approach to restart in global optimization. J Glob Optim 10:1–16
Ohsaki M (2001) Random search method based on exact reanalysis for topology optimization of trusses with discrete cross-sectional areas. Comput Struct 79(6):673–679
Ohsaki M (2008) Local search for multiobjective optimization of steel frames. In: Proceedings 5th China-Japan-Korea joint symposium on optimization of structural and mechanical systems (CJK-OSM5), Jeju Korea
Ohsaki M, Katsura M (2012) A random sampling approach to worst-case design of structures. Struct Multidisc Optim 46:27–39
Pant M, Thangaraj R, Abraham A, Deep K (2009) Particle swarm optimization using sobol mutation. Int J Simul Syst Sci Technol 10(3):89–98
Törn A, Z̆ilinskas (1989) Global Optimization, Lecture notes in comput. Sci., no. 350. Springer
Voglis C, Lagaris IE (2009) Towards ldeal Multistart: A stochastic approach for locating the minima of a continuous function inside a bounded domain. Appl Math Comput 213:216–219
Zabinsky ZB (2003) Stochastic adaptive search for global optimization. Kluwer Academic Publishers
Zabinsky ZB, Bulger D, Khompatraporn C (2010) Stopping and restarting strategy for stochastic sequential search in global optimization. J Glob Optim 46:273–286
Zieliński R (1981) A statistical estimate of the structure of multi-extremal problems. Math Program 21:348–356
Acknowledgments
This work is partially supported by JSPS KAKENHI No. 25420576.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ohsaki, M., Yamakawa, M. Stopping rule of multi-start local search for structural optimization. Struct Multidisc Optim 57, 595–603 (2018). https://doi.org/10.1007/s00158-017-1779-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00158-017-1779-0