Parametric reconfiguration improvement in non-iterative concurrent mechatronic design using an evolutionary-based approach

Portilla-Flores, Edgar Alfredo; Mezura-Montes, Efrén; Álvarez-Gallegos, Jaime; Coello, Carlos A. Coello; Cruz-Villar, Carlos A.; Villarreal-Cervantes, Miguel Gabriel

doi:10.1016/j.engappai.2011.02.019

Cited by 25 publications

(16 citation statements)

References 17 publications

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“…Indeed, the existence of interactions between the different sub-systems and the multi-field nature of the devices strongly influences the system performances. The optimization of the whole system with regard to several objectives has to be achieved by including all these interactions [9]. Mechanism and Machine Theory 61 (2013) [68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83] Li et al [1] developed a concurrent framework for a mechatronic design, called Design For Control (DFC).…”

Section: Introductionmentioning

confidence: 99%

Application of multi-objective genetic algorithms to the mechatronic design of a four bar system with continuous and discrete variables

EL-Kribi

Houidi

Affi

et al. 2013

Mechanism and Machine Theory

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Section: Introductionmentioning

confidence: 99%

Application of multi-objective genetic algorithms to the mechatronic design of a four bar system with continuous and discrete variables

EL-Kribi

Houidi

Affi

et al. 2013

Mechanism and Machine Theory

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“…In the last decades, the use of heuristic techniques have been used in engineering problems [16], [17]- [19]. This is due to the increment of the technological advances and because problems are non-convex, discontinuous and/or present discrete variables that make it difficult (or imposible) to solve them by traditional optimization techniques such as nonlinear programming techniques.…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

“…In this work, the differential evolution (DE) algorithm [20] with a constraint-handling mechanism [17] is used to solve the dynamic optimization problem. The constraint-handling differential evolution (CHDE) algorithm is shown in Fig.…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

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Differential Evolution for the Control Gain's Optimal Tuning of a Four-bar Mechanism

Calva-Yáñez¹,

Niño-Suárez²,

Villarreal-Cervantes³

et al. 2013

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Abstract-In this paper the variation of the velocity error of a four-bar mechanism with spring and damping forces is reduced by solving a dynamic optimization problem using a differential evolution algorithm with a constraint handling mechanism. The optimal design of the velocity control for the mechanism is formulated as a dynamic optimization problem. Moreover, in order to compare the results of the differential evolution algorithm, a simulation experiment of the proposed control strategy was carried out. The simulation results and discussion are presented in order to evaluate the performance of both approaches in the control of the mechanism.

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“…In [13], an approach based on a differential evolution algorithm to promote parametric reconfiguration characteristics on a continuously variable transmission C.V.T. and on a parallel robot optimal design is presented.…”

Section: Introductionmentioning

confidence: 99%

Influence of the Binomial Crossover in the DE Variants Based on the Robot Design with Optimum Mechanical Energy

et al. 2015

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Abstract-Differential evolution (DE) is a powerful algorithm to find an optimal solution in real world problems. Nevertheless, the binomial crossover parameter is an important issue for the success of the algorithm. The proper selection of the binomial crossover parameter depends on the problem at hand. In this work, the effect of the binomial crossover in the DE/Rand/1/bin, DE/Best/1/Bin and DE/Current to rand/1/Bin is empirically studied and analyzed in the optimum design of the kinematic and the dynamic parameters of links for a parallel robot. The optimum design minimizes mechanical energy and consequently reduces the energy provided by the actuator. Based on the experimental results, the range of crossover parameter values that properly explores the search space is obtained. The importance of finding a proper crossover parameter is highlighted. In addition, the optimal design shows a decrease in the parallel robot mechanical energy compared with non-optimal design.Index Terms-Differential evolution, binomial crossover, optimum design, mechatronic design.

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Parametric reconfiguration improvement in non-iterative concurrent mechatronic design using an evolutionary-based approach

Cited by 25 publications

References 17 publications

Application of multi-objective genetic algorithms to the mechatronic design of a four bar system with continuous and discrete variables

Application of multi-objective genetic algorithms to the mechatronic design of a four bar system with continuous and discrete variables

Differential Evolution for the Control Gain's Optimal Tuning of a Four-bar Mechanism

Influence of the Binomial Crossover in the DE Variants Based on the Robot Design with Optimum Mechanical Energy

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