Genetic algorithm synthesis of four-bar mechanisms

Abstract

The synthesis of four-bar mechanisms is a well-understood, classical design problem. The original systematic work in this field began in the late 1800s and continues to be an active area of research. Limitations to the classical theory of four-bar synthesis potentially limit its application to certain real-world problems by virtue of the small number of precision points and unspecified order. This paper presents a numerical technique for four-bar mechanism synthesis based on genetic algorithms that removes this limitation by relaxing the accuracy of the precision points.

Evolutionary synthesis of mechanisms applied to the design of an exoskeleton for finger rehabilitation

“…Recently, an increasing number of works have used evolutionary strategies to solve mechanism synthesis problems [20][21][22][23][24]. The main advantages of these methods are their simplicity in implementing algorithms and their low computational cost in some cases.…”

“…One of the greatest challenges when these kinds of algorithms are used for synthesis of mechanisms is to find a good representation of the mechanisms. The first works using a genetic algorithm were carried out by Fang [20] and Ronston and Sturges [21]. Their algorithms used a binary representation of the mechanisms, whose processing procedures were time consuming and computationally expensive.…”

Optimum Path Synthesis of a Geared Five-Bar Mechanism

“…Several optimization algorithms, including exact gradient [9], simulated annealing [13], genetic algorithm (GA) and modified GA [7,8,10,11,19,23,25], ant-gradient [6,17,26], genetic algorithmfuzzy logic [24], differential evolution (DE) and modified DE [14-16, 18, 19, 21, 22, 27], particle swarm optimization [19], GA-DE [20,28], and hybrid optimizer [29], are used to solve the optimization problems of path synthesis. In the one-phase synthesis method, the error function in [9][10][11][14][15][16][17][18][19][20][21][22] is based on the sum of the square of Euclidean distance error (termed the square deviation in this study) between the target points and the corresponding coupler points. The error function in [23,24] is based on the orientation structural error of the fixed link.…”

Síntesis Genética de Mecanismos para Aplicaciones en Prótesis de Miembro Inferior

“…Para este fin, se han desarrollado diferentes metodologías que comprenden optimización no lineal (Levitski &Shakvazian (1960)), algoritmos genéticos (Roston &Sturges (1996);Michalewicz (1999);Cabrera et al (2002); Laribi et al (2004a); Quintero-R et al (2004)), redes neuronales (Vasiliu &Yannou (2001); Starosta (2006);Walczak (2006)), optimización Monte Carlo (Kalnas &Kota (2001)), o el método de desviación controlada (R. -Bulatovic &S. R. Djordjevic (2004)). Todos los métodos anteriores han sido utilizados para la síntesis de mecanismos de ISSN: 1697-7912.…”