Optimum synthesis of path generating four-bar mechanisms using differential evolution and a modified error function

Matekar, Sanjay B.; Gogate, Gunesh R.

doi:10.1016/j.mechmachtheory.2012.01.017

Cited by 66 publications

(28 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Parameter adaptation of F 2 : The mutation factor F 2 in the exhaustive local exploitation mechanism is self generated according to [28]. This factor uses a Cauchy distribution with a location parameter µ F 2 , specifying the location of the peak of the distribution and the scale parameter of 0.1 which specifies the half-width at half-maximum as it is observed in (16).…”

Section: Exhaustive Local Exploitation Mechanism With Adaptive Scale mentioning

confidence: 99%

“…Hence, the manipulator design have been stated as an optimization problem where optimization techniques such as, heuristic algorithms [15], [16], [17], [18], [19], [20] and gradient based algorithms [21], have been used. Nevertheless, if the optimization problem is nonlinear or discontinuous one, gradient based algorithms are not suitable to solve the problem because they converge to local minima near the initial condition (sensitive to initial condition) [22], [23], then the design solution will perform poorly.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimum Design of Parallelogram Five-bar Manipulator for Dexterous Workspace by using ELEMAEF in Differential Evolution

Villarreal-Cervantes¹,

Cruz-Mucio²,

Portilla-Flores³

2014

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

Abstract:The kinematic design of mechanism is an important stage in the design methodology. A dexterous workspace for a manipulator is an outstanding characteristic that must be considered in it. Hence, a mono-objective constraint optimization problem (MOCOP) for the kinematic design of a manipulator with three revolute joints (3R robot), that fulfils a defined dexterous workspace, is formulated. The MOCOP is solved by proposing a mechanism in the differential evolution (DE) algorithm called exhaustive local exploitation mechanism with adaptive scale factor (ELEMAEF). This mechanism exhaustively exploits a local region in the search space with the information of the base and the difference vectors of good trial vector, in an attempt to generate better individuals in the same direction. In addition, the ELEMAEF guides the evolution of the population toward a better zone without sacrificing the search capabilities of the DE algorithm. A comparison of the DE algorithm with and without the ELEMAEF for this particular design problem is presented. The use of the ELEMAEF gives a superior performance in the DE algorithm.

show abstract

Section: Exhaustive Local Exploitation Mechanism With Adaptive Scale mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimum Design of Parallelogram Five-bar Manipulator for Dexterous Workspace by using ELEMAEF in Differential Evolution

Villarreal-Cervantes¹,

Cruz-Mucio²,

Portilla-Flores³

2014

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…However, the GA-DE algorithm sometimes suffers the premature convergence especially for a difficult problem. In the GA-DE algorithm, a large population size and more excellent individuals as the disturbed vectors can be utilized to alleviate the premature convergence problem, together with more repeated runs [27,28] to find satisfactory results.…”

Section: Schemesmentioning

confidence: 99%

“…References [25,26] use other error functions as the objective function. Recently, Matekar and Gogate [27] proposed a modified distance error function for path synthesis problems with prescribed timing and obtained the lower transverse errors at the cost of the higher longitudinal errors because they thought the former is an indication of closeness between the generated and prescribed paths and the latter is an indication of the error in the timing. Sedano et al [29] proposed a new error estimator defined by means of the precision points.…”

Section: Introductionmentioning

confidence: 99%

Optimum Path Synthesis of a Geared Five-Bar Mechanism

Lin

2013

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Most studies on path synthesis problems are to trace simple or smooth trajectories. In this work, an optimum synthesis for several special trajectories generated by a geared five-bar mechanism is studied using the one-phase synthesis method. The synthesis problem for the special trajectories, which is originally studied using the two-phase synthesis method discussed in the literature, is a real challenge due to very few dimensionally proportioned mechanisms that can generate the special trajectories. The challenging special trajectories with up to 41 discrete points include a self-overlapping curve, nonsmooth curves with straight segments and vertices, and sophisticated shapes. The error function of the square deviation of positions is used as the objective function and the GA-DE evolutionary algorithm is used to solve the optimization problems. Findings show that the proposed method can obtain approximately matched trajectories at the cost of a tremendous number of evaluations of the objective function. Therefore, the challenging problems may serve as the benchmark problems to test the effectiveness and efficiency of synthesis methods and/or optimization algorithms. All the synthesized solutions have been validated using the animation of the SolidWorks assembly so that the obtained mechanisms are sound and usable.

show abstract

“…The effectiveness of the method depends essentially on the number of design parameters, therefore mechanism synthesis addresses also the techniques minimizing the number of design parameters (Buśkiewicz, 2014;Buśkiewicz, 2010;McGarva and Mullineux, 1994;Ullah and Kota, 1997;Wen-Yi, 2010). Other techniques focus on developing algorithms minimizing an objective function and constructing new error functions (Penunuri et al, 2011;Sanchez Marin and Gonzalez, 2004;Matekar and Gogate, 2012;Bulatovic and Dordevic, 2009;Chao and Jorge, 2008).…”

Section: Introductionmentioning

confidence: 99%