Variations of Ant Colony Optimization for the Solution of the Structural Damage Identification Problem

Braun, Carlos Eduardo; Chiwiacowsky, Leonardo Dagnino; Gmez, Arthur T.

doi:10.1016/j.procs.2015.05.218

Cited by 30 publications

(16 citation statements)

References 18 publications

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“…PSO was selected due to its almost similar characteristics with GSA and its application in the similar type of sputtering process [23][24][25], while GA was selected due to its adaptation in other type of sputtering processes [26][27][28]. As for AIS and ACO, these algorithms have been adapted in various engineering parameter optimization problems [29][30][31][32]. All of the algorithms were executed 30 times, with their average fitness values and processing times recorded as shown in Table 6.…”

Section: Resultsmentioning

confidence: 99%

Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm

et al. 2016

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This research is focusing on the radio frequency (RF) magnetron sputtering process, a physical vapor deposition technique which is widely used in thin film production. This process requires the optimized combination of deposition parameters in order to obtain the desirable thin film. The conventional method in the optimization of the deposition parameters had been reported to be costly and time consuming due to its trial and error nature. Thus, gravitational search algorithm (GSA) technique had been proposed to solve this nano-process parameters optimization problem. In this research, the optimized parameter combination was expected to produce the desirable electrical and optical properties of the thin film. The performance of GSA in this research was compared with that of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Immune System (AIS) and Ant Colony Optimization (ACO). Based on the overall results, the GSA optimized parameter combination had generated the best electrical and an acceptable optical properties of thin film compared to the others. This computational experiment is expected to overcome the problem of having to conduct repetitive laboratory experiments in obtaining the most optimized parameter combination. Based on this initial experiment, the adaptation of GSA into this problem could offer a more efficient and productive way of depositing quality thin film in the fabrication process.

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Section: Resultsmentioning

confidence: 99%

Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm

et al. 2016

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“…The pattern search method of Hooke-Jeeves (HJ) [13] is a well known algorithm consisting of the repeated application of exploratory moves about a base point which, if successful, is followed by pattern moves. HJ have been used in some hybrid algorithms solving the damage identification problem, such as AS+HJ [14], qG-HJ [7], and MPCA-HJ [6,8].…”

Section: Hooke-jeeves Pattern Search Methodsmentioning

confidence: 99%

Multi-Particle Collision Algorithm with Hooke Jeeves applied to the damage identification in a Kabe Problem

Torres¹,

Velho²,

Chiwiacowsky³

2018

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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A new variant of the hybrid metaheutic MPCAHJ (Multi-Particle Collision Algorithm with Hooke-Jeeves method) is presented. Multi-Particle Collision Algorithm is a metaheuristic algorithm that performs a search on the search space. With the addition of the Rotation-Based Learning mechanism to the exploration search, a maior area of the search space has chance to be visited. The Hooke-Jeeves direct search method exploites the best solution found, allowing to achieve better solutions. The performance of all implementation are evaluated over twenty-two well known benchmark functions.

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“…Fixing the values for discrete design variables and then updating the optimal values only for continuous design variables by performing SQP on the approximation model The end of Step Two Figure 3 Flowchart for the coordinate search technique in Step Two (3) Hooke-Jeeves direct search technique (Kolda et al 2003, Garcia et al 2006, Brauna et al 2015, Liu 2016), a more robust approach than the first two algorithms, can more efficiently and effectively exploit the design space of discrete variables for the optimal solutions. The flowchart for the Hooke-Jeeves direct search technique is depicted in Figure 4.…”

Section: Startmentioning

confidence: 99%

Efficient hybrid algorithms to solve mixed discrete-continuous optimization problems

Liu

Zhang

et al. 2018

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Abstract:Purpose -In real world cases, it is common to encounter mixed discrete-continuous problems where some or all of the variables may take only discrete values. To solve these non-linear optimization problems, it is very time-consuming in use of finite element methods. The purpose of this paper is to study the efficiency of the proposed hybrid algorithms for the mixed discrete-continuous optimization, and compares it with the performance of Genetic Algorithms (GA).Design/methodology/approach -In this paper, the enhanced multipoint approximation method (MAM) is utilized to reduce the original nonlinear optimization problem to a sequence of approximations. Then, the Sequential Quadratic Programming (SQP) technique is applied to find the continuous solution. Following that, the implementation of discrete capability into the MAM is developed to solve the mixed discrete-continuous optimization problems. Findings-The efficiency and rate of convergence of the developed hybrid algorithms outperforming GA are examined by six detailed case studies in the ten-bar planar truss problem and the superiority of the Hooke-Jeeves assisted MAM algorithm over the other two hybrid algorithms and GAs is concluded.Originality/value -The authors propose three efficient hybrid algorithms: the rounding-off, the coordinate search, and the Hooke-Jeeves search assisted MAMs, to solve nonlinear mixed discrete-continuous optimization problems. Implementations include the development of new procedures for sampling discrete points, the modification of the trust region adaptation strategy, and strategies for solving mix optimization problems. To improve the efficiency and effectiveness of metamodel construction, regressors φ defined in this paper can have the form in common with the empirical formulation of the problems in many engineering subjects.

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Variations of Ant Colony Optimization for the Solution of the Structural Damage Identification Problem

Cited by 30 publications

References 18 publications

Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm

Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm

Multi-Particle Collision Algorithm with Hooke Jeeves applied to the damage identification in a Kabe Problem

Efficient hybrid algorithms to solve mixed discrete-continuous optimization problems

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