Modelling and prediction of complex non-linear processes by using Pareto multi-objective genetic programming

Jamali, Ali; Khaleghi, Evan; Gholaminezhad, Iman; Nariman-Zadeh, N.

doi:10.1080/00207721.2014.945983

Cited by 15 publications

(5 citation statements)

References 14 publications

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“…33 More complete details on genetic programming and its terminology is presented in Refs. 30,33,34 In the optimization process, the inverse modeling error is evolutionarily minimized as the fitness function which is expressed as follows…”

Section: Resultsmentioning

confidence: 99%

“…In evolutionary algorithms (EAs), genetic programming is defined as an extension to genetic algorithms (GAs) in which the structures undergoing adaptation are not strings but are hierarchical computer programs of dynamically varying shape and size. Recent research works that set the stage for current GP research topics and applications is various, and includes topology optimization of structures, 32,33 optimal modeling of complex systems, 34 controller design, 35 image processing, 36 object detection, 37 data mining, 38 control of robots, 39 and so on.…”

Section: Introductionmentioning

confidence: 99%

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Optimal synthesis of crank-rocker mechanisms with optimum transmission angle for desired stroke and time-ratio using genetic programming

Ahmadi

2022

Advances in Mechanical Engineering

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Dimensional synthesis of crank-rocker mechanisms applied to provide some desired values of stroke and time ratio, is of utmost importance for designing an efficient mechanism. In the synthesis and manufacturing of crank-rocker mechanisms, the designers are further challenged by other design criteria, such as quality of motion. In this study, a novel approach based on genetic programming (GP) is proposed for dimensional synthesis of planar crank-rocker mechanisms with optimum transmission angle over the desired stroke and time-ratio. An analytical approach is elaborated which leads to an interesting relationship of length of the coupler and rocker links. It is, therefore, advised that by adopting equal lengths for coupler link and rocker link, one can guarantee the optimality of the transmission angle’s deviation ([Formula: see text]), ensuring the Grashof condition. Consequently, through an inverse modeling approach, GP method is utilized to construct some explicitly mathematical formulas to represent all the sizes and dimensions of the crank-rocker mechanism based on the any desired values of both stroke and time-ratio. In this way, an input-output data set consisting of all the dimensions and the sizes of the links as the input variables and both the stroke and time-ratio as output variables is first constructed using the pertinent mathematical equations. Indeed, such approach of inverse modeling using GP simplifies greatly the synthesis of the crack-rocker mechanisms for any desired values of both stroke and time-ratio. The proposed approach has been applied for kinematic synthesis of a crank-rocker mechanism. A comparison between the obtained results of this work and the analytic method, clearly illustrates the efficiency of the proposed approach.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal synthesis of crank-rocker mechanisms with optimum transmission angle for desired stroke and time-ratio using genetic programming

Ahmadi

2022

Advances in Mechanical Engineering

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“…In this work, previously developed multi-objective uniform-diversity genetic-programming code is used for Pareto modeling of RSOCs. Further details of this algorithm can be found in [24,25]. Experimental data of reversible operation with Ni-YSZ anode and LSM-YSZ cathode materials are gathered from the literature [26,27].…”

Section: Modeling and Optimization Methodsmentioning

confidence: 99%

Multi-objective modeling, uncertainty analysis, and optimization of reversible solid oxide cells

Salehi

Gholaminezhad

2018

Int J Energy Environ Eng

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Reversible solid oxide cells can provide efficient and cost-effective scheme for electrical-energy storage applications. However, this technology faces many challenges from material development to system-level operational parameters , which should be tackle for practical purposes. Accordingly, this study focuses on developing novel robust artificial intelligence-based blackbox models to optimize operational variables of the system. A genetic-programming algorithm is used for Pareto modeling of reversible solid oxide cells in a multi-objective fashion based on experimental input-output data. The robustness of the obtained optimal model evaluated using Monte Carlo simulations technique. An optimization study adopted to optimize the operating parameters, such as temperature and fuel composition using a differential evolution algorithm. The objective functions that have been considered for Pareto multi-objective modeling process are training error and model complexity. In addition, the discrepancy between maximum and minimum output voltage in the whole operation of the system is chosen as the optimization process objective function. The robustness of the optimal trade-off model is shown in terms of statistical indices for varied uncertainty levels from 1 to 10%. The optimized operational condition based on the suggested model reveals optimal intermediate temperature of 762 °C and fuel mixture of about 29% H 2 , 25% H 2 O, and 14% CO.

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“…As a result, many nature-based methods, including ant colony, particle swarm optimization, artificial immune system, evolutionary algorithms, and so forth, were suggested by researchers (Coello et al, 2007). Among the several direct algorithms in solving optimal problems, genetic programming (GP) has proven robust for its capabilities to symbolically present both topology and mathematical details of the solutions (Assimi et al, 2017; Bruns et al, 2019; Brameier and Banzhaf, 2001; Devarriya et al, 2020; Li et al, 2006; Gholaminezhad et al, 2014; Jamali et al, 2016; Koza, 1989, 1990, 1994a; Poli et al, 2008). In the study conducted by Maher and Mohamed (2013), the efficacy of the GP algorithm in obtaining optimal control solutions of the nonlinear problems was assessed.…”

Section: Introductionmentioning

confidence: 99%

The archived-based genetic programming for optimal design of linear/non-linear controllers

Mohammadi

Nariman-Zadeh

Jamali

2020

Transactions of the Institute of Measurement and Control

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Evaluation of control signal function is one of the critical subjects in the optimal control problems. The optimal control is usually obtained by optimizing a performance index that is a weighted combination of control effort and state trajectories in the quadratic form, typically known as quadratic performance index (QPI). For the simple case of linear time-invariant (LTI) systems, problems are commonly solved using the well-established governing Riccati equation; however, obtaining the analytical solutions for linear time-variant (LTV) and nonlinear systems has always been highly debated in the optimal control problems. In this study, a newly developed type of Genetic Programming called the archived-based genetic programming (AGP) is presented. Using this algorithm, the analytical solutions for any type of optimal control problems can be obtained faster and more efficiently than the ordinary GPs. Subsequently, due to the inefficiency of QPI in capturing the general behavior of signals, a new performance index named the absolute performance index (API) is proposed in this study. Since the developed AGP algorithm could find the analytical solutions irrespective of the conventional mathematical calculations, it can be effectively implemented to solve the introduced API measures. According to the analytical results, it is observed that in a given problem, the solutions of API are more compatible with the design goals compared with QPI. Furthermore, it is shown that some new forms of the control signals such as impulse solutions, which may not be obtained using QPI, can only be estimated using API in defining the optimal control problems.

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Modelling and prediction of complex non-linear processes by using Pareto multi-objective genetic programming

Cited by 15 publications

References 14 publications

Optimal synthesis of crank-rocker mechanisms with optimum transmission angle for desired stroke and time-ratio using genetic programming

Optimal synthesis of crank-rocker mechanisms with optimum transmission angle for desired stroke and time-ratio using genetic programming

Multi-objective modeling, uncertainty analysis, and optimization of reversible solid oxide cells

The archived-based genetic programming for optimal design of linear/non-linear controllers

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