Modified genetic algorithm strategy for structural identification

Perry, M. J.; Koh, C. G.; Choo, Yoo Sang

doi:10.1016/j.compstruc.2005.11.008

Cited by 92 publications

(60 citation statements)

References 13 publications

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“…However, for the model parameters there are no rules or constraints that we could use to reduce the search space as in the way we used the insect walking rules for the main gait learning task. Because of this, the search space reduction method proposed by Perry et al (2006) was employed. This strategy reduces the search space for parameters which converge quickly.…”

Section: Evolutionary Identification Of the Simulation Modelmentioning

confidence: 99%

A biologically inspired approach to feasible gait learning for a hexapod robot

Belter¹,

Skrzypczyński²

2010

International Journal of Applied Mathematics and Computer Science

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The objective of this paper is to develop feasible gait patterns that could be used to control a real hexapod walking robot. These gaits should enable the fastest movement that is possible with the given robot's mechanics and drives on a flat terrain. Biological inspirations are commonly used in the design of walking robots and their control algorithms. However, legged robots differ significantly from their biological counterparts. Hence we believe that gait patterns should be learned using the robot or its simulation model rather than copied from insect behaviour. However, as we have found tahula rasa learning ineffective in this case due to the large and complicated search space, we adopt a different strategy: in a series of simulations we show how a progressive reduction of the permissible search space for the leg movements leads to the evolution of effective gait patterns. This strategy enables the evolutionary algorithm to discover proper leg co-ordination rules for a hexapod robot, using only simple dependencies between the states of the legs and a simple fitness function. The dependencies used are inspired by typical insect behaviour, although we show that all the introduced rules emerge also naturally in the evolved gait patterns. Finally, the gaits evolved in simulations are shown to be effective in experiments on a real walking robot.

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Section: Evolutionary Identification Of the Simulation Modelmentioning

confidence: 99%

A biologically inspired approach to feasible gait learning for a hexapod robot

Belter¹,

Skrzypczyński²

2010

International Journal of Applied Mathematics and Computer Science

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“…GAs have been used to identify the damage severity of truss structures (Chou and Ghaboussi (2001)) and parameters of shear-type building structures (Koh et al (2000); (2003)). Perry et al (2006) presented a modified GA to identify structural systems. DE has been successfully applied in induction motor identification problems (Ursem and Vadstrup (2003)) and structural system identification ).…”

Section: Introductionmentioning

confidence: 99%

Identification of Structural Systems Using Particle Swarm Optimization

Xue

Tang

Zhou

2009

Journal of Asian Architecture and Building Engineering

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Particle swarm optimization (PSO) is a new heuristic method that has yielded promising results for solving complex optimization problems. Its advantages are a simple structure, ease of use, quality of solution, and robustness. This paper utilizes the PSO algorithm for parameter estimation of structural systems, which could be formulated as a multi-modal numerical optimization problem with high dimension. Simulation results for identifying the parameters of multiple degree-of-freedom (DOF) linear and nonlinear structural systems are presented to demonstrate the effectiveness of the proposed method.

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“…GA applications in system identification such the identification of elastic properties of composite plates from dynamic test data is presented in (Jesiel et al 1999;Chakraborthy and Mukhopadhyaya 2002). Recently efforts have been made to alter the architecture of GA and to incorporate local search algorithms to further improve its performance, see for example see Perry et al 2004).…”

Section: Evolutionary Algorithms and Behaviourally Inspired Algorithmsmentioning

confidence: 99%

Structural System Identification in the Time Domain using Evolutionary and Behaviorally Inspired Algorithms and their Hybrids

2009

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In this study, parametric identification of structural properties such as stiffness and damping is carried out using acceleration responses in the time domain. The process consists of minimizing the difference between the experimentally measured and theoretically predicted acceleration responses. The unknown parameters of certain numerical models, viz., a ten degree of freedom lumped mass system, a nine member truss and a non-uniform simply supported beam are thus identified. Evolutionary and behaviorally inspired optimization algorithms are used for minimization operations. The performance of their hybrid combinations is also investigated. Genetic Algorithm (GA) is a well known evolutionary algorithm used in system identification. Recently Particle Swarm Optimization (PSO), a behaviorally inspired algorithm, has emerged as a strong contender to GA in speed and accuracy. The discrete Ant Colony Optimization (ACO) method is yet another behaviorally inspired method studied here. The performance (speed and accuracy) of each algorithm alone and in their hybrid combinations such as GA with PSO, ACO with PSO and ACO with GA are extensively investigated using the numerical examples with effects of noise added for realism. The GA+PSO hybrid algorithm was found to give the best performance in speed and accuracy compared to all others. The next best in performance was pure PSO followed by pure GA. ACO performed poorly in all the cases.

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Modified genetic algorithm strategy for structural identification

Cited by 92 publications

References 13 publications

A biologically inspired approach to feasible gait learning for a hexapod robot

A biologically inspired approach to feasible gait learning for a hexapod robot

Identification of Structural Systems Using Particle Swarm Optimization

Structural System Identification in the Time Domain using Evolutionary and Behaviorally Inspired Algorithms and their Hybrids

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