Placing actuators on space structures by genetic algorithms and effectiveness indices
- 104 Downloads
Genetic algorithms are a powerful tool for the solution of combinatorial problems such as the actuator placement problem. However, they require a large number of analyses with correspondingly high computational costs. Therefore, it is useful to tune the operators and parameters of the algorithm on simple problems that are similar to more complex and computationally expensive problems. The present paper employs an easy-tocalculate measure of actuator effectiveness to evaluate several genetic algorithms. Additionally, the effects of population size and mutation rates are also investigated for a problem of placing actuators at 8 of 1507 possible locations. We find that even with the best of the algorithms and with optimum mutation rates, tens of thousands of analyses are required for obtaining near optimum locations. We propose a procedure that estimates the effectiveness of the various locations and discards ineffective ones, and find it helpful for reducing the cost of the genetic optimization.
KeywordsGenetic Algorithm Population Size Civil Engineer Computational Cost Mutation Rate
Unable to display preview. Download preview PDF.
- Bäck, T.; Schwefel, H.-P. 1993: An overview of evolutionary algorithms for parameter optimization.Evolutionary Computation 1, 1–23Google Scholar
- Chen, G.-S.; Bruno, R.J.; Salama, M. 1991: Optimal placement of active/passive members in structures using simulated annealing.AIAA J. 29, 1327–1334Google Scholar
- Holland, J.H. 1975:Adaptive in natural and artificial systems. Ann Arbor: The University of Michigan PressGoogle Scholar
- Kincaid, R.; Bloebaum, C. 1993: The damper placement problem for the CSI-phase I evolutionary model.AIAA-93-1655-CP, Proc. AIAA/ASME/ASCE/AHS/ASC 34th Structures, Structural Dynamics and Materials Conf., AIAA/ASME Adaptive Structures Forum (held in La Jolla, CA)6, 3086–3095Google Scholar
- Le Riche, R.; Haftka, R.T. 1993: Optimization of laminate stacking sequence for buckling load maximization by genetic algorithm.AIAA J. 31, 951–956Google Scholar
- Nagendra, S.; Haftka, R.T.; Gürdel, Z. 1993: Design of a blade stiffened composite panel by a genetic algorithm.AIAA-93-1584-CP, Proc. of AIAA/ASME/ASCE/AHS/ASC 34th Structures, Structural Dynamics and Materials Conf. (held in La Jolla, CA)4, 2418–2436Google Scholar
- Onoda, J.; Hanawa, Y. 1993: Actuator placement optimization by genetic optimization and improved simulated annealing.AIAA Journal 31, 1167–1169Google Scholar
- Padula, S.L.; Sandridge, C.A. 1992: Active strut placement using integer programming for the CSI evolutionary model.Proc. 4th Symp. on Multidisciplinary Analysis and Design Google Scholar
- Ponslet, E.; Haftka, R.T.; Cudney, H.H. 1993: Optimal placement of actuators and other peripherals for large space structures. In: Bendsøe, M.P.; Mota Soares, C.A. (eds.)Topology design of structures, pp. 135–144. Dordrecht: KluwerGoogle Scholar
- Radcliffe, N.J.; George, F.A.W. 1993: A study in set recombination.Proc. 5th Int. Conf. on Genetic Algorithms (held at the University of Illinois at Urbana-Champaign), pp. 23–30. San Mateo, CA: Morgan Kaufmann PublishersGoogle Scholar
- Rao, S.S.; Pan, T.S.; Venkayya, V.B. 1991: Optimal placement of actuators in actively controlled structures using genetic algorithms.AIAA J. 29, 942–943Google Scholar