Developed comparative analysis of metaheuristic optimization algorithms for optimal active control of structures

Katebi, Javad; Shoaei-parchin, Mona; Shariati, Mahdi; Nguyen-Thoi, T.; Khorami, Majid

doi:10.1007/s00366-019-00780-7

Cited by 112 publications

(35 citation statements)

References 46 publications

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“…GP is a heuristic search technique for evolving programs that can be regarded as a generalisation of genetic algorithms (see Katebi et al (2020) for comparative analysis of metaheuristic optimization algorithms). This optimization approach is based on the representation of programs in tree structures.…”

Section: Methodsmentioning

confidence: 99%

A Genetic Programming Approach for Estimating Economic Sentiment in the Baltic Countries and the European Union

Clavería

Monte

Porras

2021

Technological and Economic Development of Economy

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In this study, we introduce a sentiment construction method based on the evolution of survey-based indicators. We make use of genetic algorithms to evolve qualitative expectations in order to generate country-specific empirical economic sentiment indicators in the three Baltic republics and the European Union. First, for each country we search for the non-linear combination of firms’ and households’ expectations that minimises a fitness function. Second, we compute the frequency with which each survey expectation appears in the evolved indicators and examine the lag structure per variable selected by the algorithm. The industry survey indicator with the highest predictive performance are production expectations, while in the case of the consumer survey the distribution between variables is multi-modal. Third, we evaluate the out-of-sample predictive performance of the generated indicators, obtaining more accurate estimates of year-on-year GDP growth rates than with the scaled industrial and consumer confidence indicators. Finally, we use non-linear constrained optimisation to combine the evolved expectations of firms and consumers and generate aggregate expectations of of year-on-year GDP growth. We find that, in most cases, aggregate expectations outperform recursive autoregressive predictions of economic growth.

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Section: Methodsmentioning

confidence: 99%

A Genetic Programming Approach for Estimating Economic Sentiment in the Baltic Countries and the European Union

Clavería

Monte

Porras

2021

Technological and Economic Development of Economy

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“…Therefore, out of the total 24,000 pieces of data accessible here, there is a need to determine a portion applicable to training processes and another to testing processes. As widely suggested in the literature by various researchers, [33,[55][56][57][58], 80% of data was allocated to training and the remaining part was allocated to testing. A variety of algorithms have been introduced in the literature for the purpose of training ANN, among which a key one is the Levenberg-Marquardt (LM) method [100][101][102][103][104].…”

Section: Initial Modelmentioning

confidence: 99%

Seepage Analysis in Short Embankments Using Developing a Metaheuristic Method Based on Governing Equations

et al. 2020

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Seepage is one of the most challenging issues in some procedures such as design, construction, and operation of embankment or earth fill dams. The purpose of this research is to develop a new solution based on governing equations to solve the seepage problem in an effective way. Therefore, by implementing the equations in the programming environment, more than 24,000 models were designed to be applicable to different conditions. Input data included different parameters such as slopes in upstream and downstream, embankment width, soil permeability coefficient, height, and freeboard. With the use of this big data, a new process was developed to provide simple mathematical models for the seepage rate analysis. The study first used intelligent models to simulate the seepage behavior. Finally, the accuracy of the models was optimized using a new metaheuristic algorithm. This led to the ultimate flexibility of the final model presented as a new solution capable of evaluating different conditions. Finally, using the best model, new mathematical relationships were developed based on this methodology. This new solution can be used as a proper alternative to the governing equations of seepage rate estimation. Another advantage of the proposed model is its high flexibility that can be well applied to engineering design in this field, which was not possible using the initial equations.

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“…Sliding Mode Control (SMC) is a powerful tool for solving the robust stability and tracking problem of nonlinear dynamical systems operating under various kinds of uncertainties and disturbances [1]- [7]. The key advantages of SMC are robustness to parametric uncertainties, low sensitivity to external disturbances, order reduction, fast convergence, and ease of implementation [8]- [11]. Due to these advantages, SMC has been extensively used in applications including robotics, chaotic systems, wind…”

Section: Introduction a Background And Motivationmentioning

confidence: 99%

Design of a Non-Singular Adaptive Integral-Type Finite Time Tracking Control for Nonlinear Systems With External Disturbances

et al. 2021

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This paper proposes an adaptive non-singular fast terminal sliding mode control (FTSMC) with integral surface for the finite time tracking control of nonlinear systems with external disturbances. An appropriate parameter-tuning adaptation law is derived to tackle the disturbances. A new fast terminal sliding scheme with self-tuning algorithm is proposed to synthesize the adaptive non-singular fast integral terminal sliding approach. The proposed approach has the following features: 1) It does not require the derivative of the fractional power terms with respect to time, thereby eschewing the singularity problem typically associated with TSMC; 2) It guarantees the existence of the switching phase under exogenous disturbances with unknown bounds; 3) Because of the integral terms in the sliding surface, the power functions are hidden behind the integrator; 4) It ensures chattering-free dynamics. The effectiveness of the proposed approach is assessed using both a simulation and an experimental study. The obtained results showed that the FTSM control technique guarantees that when the switching surface is reached, tracking errors converge to zero at a fast convergence rate. Additionally, the integral term offers one extra degree-of-freedom and since the time-derivative of fractional power terms is not needed in the controller, the proposed switching surface provides a comprehensive framework for singularity avoidance.

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Developed comparative analysis of metaheuristic optimization algorithms for optimal active control of structures

Cited by 112 publications

References 46 publications

A Genetic Programming Approach for Estimating Economic Sentiment in the Baltic Countries and the European Union

A Genetic Programming Approach for Estimating Economic Sentiment in the Baltic Countries and the European Union

Seepage Analysis in Short Embankments Using Developing a Metaheuristic Method Based on Governing Equations

Design of a Non-Singular Adaptive Integral-Type Finite Time Tracking Control for Nonlinear Systems With External Disturbances

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