scholarly journals Design of a Parallel Robotic Manipulator Using Evolutionary Computing

10.5772/50922 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 27 ◽  
Author(s):  
António M. Lopes ◽  
E.J. Solteiro Pires ◽  
Manuel R. Barbosa

In this paper the kinematic design of a 6-dof parallel robotic manipulator is analysed. Firstly, the condition number of the inverse kinematic jacobian is considered as the objective function, measuring the manipulator's dexterity and a genetic algorithm is used to solve the optimization problem. In a second approach, a neural network model of the analytical objective function is developed and subsequently used as the objective function in the genetic algorithm optimization search process. It is shown that the neuro-genetic algorithm can find close to optimal solutions for maximum dexterity, significantly reducing the computational burden. The sensitivity of the condition number in the robot's workspace is analysed and used to guide the designer in choosing the best structural configuration. Finally, a global optimization problem is also addressed.

2012 ◽  
Vol 562-564 ◽  
pp. 1955-1958
Author(s):  
Jin Bao Liu ◽  
Shou Ju Li ◽  
Wei Zhu

The inverse problem of parameter identification is deal with by minimizing an objective function that contains the difference between observed and calculated dam displacements. The optimization problem of minimizing objective function is solved with genetic algorithm. The calculated dam displacements are simulated by using finite element method according to water level change acting on dam upstream. The practical dam displacements are observed on the dam crest. The investigation shows that the forecasted dam displacements agree well with observed ones. The effectiveness of proposed inversion procedure is validated.


2012 ◽  
Vol 614-615 ◽  
pp. 1361-1366
Author(s):  
Ai Ning Su ◽  
Hui Qiong Deng ◽  
Tian Wei Xing

Reactive power optimization is an effective method for improving the electricity quality and reducing the power loss in power system, and it is a mixed nonlinear optimization problem, so the optimization process becomes very complicated. Genetic algorithm is a kind of adaptive global optimization search algorithm based on simulating biological genetic in the natural environment and evolutionary processes, can be used to solve complex optimization problems such as reactive power optimization. Genetic algorithm is used to solve reactive power optimization problem in this study, improved the basic genetic algorithm, included the select, crossover and mutation strategy, and proposed a individual fitness function with penalty factor. The proposed algorithm is applied to the IEEE9-bus system to calculate reactive power. The results show the superiority of the proposed model and algorithm.


2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Hassan Smaoui ◽  
Abdelkabir Maqsoud ◽  
Sami Kaidi

The solution of inverse problems in groundwater flow has been massively invested by several researchers around the world. This type of problem has been formulated by a constrained optimization problem and this constraint is none other than the direct problem (DP) itself. Thus, solving algorithms are developed that simultaneously solve the direct problem (Darcy’s equation) and the associated optimization problem. Several papers have been published in the literature using optimization methods based on computation of the objective function gradients. This type of method suffers from the inability to provide a global optimum. Similarly, they also have the disadvantage of not being applicable to objective functions of discontinuous derivatives. This paper is proposed to avoid these disadvantages. Indeed, for the optimization phase, we use random search-based methods that do not use derivative computations, but based on a search step followed only by evaluation of the objective function as many times as necessary to the convergence towards the global optimum. Among the different algorithms of this type of methods, we adopted the genetic algorithm (GA). On the other hand, the numerical solution of the direct problem is accomplished by the CVFEM discretization method (Control Volume Finite Element Method) which ensures the mass conservation in a natural way by its mathematical formulation. The resulting computation code HySubF-CVFEM (Hydrodynamic of Subsurface Flow by Control Volume Finite Element Method) solves the Darcy equation in a heterogeneous porous medium. Thus, this paper describes the description of the integrated optimization algorithm called HySubF-CVFEM/GA that has been successfully implemented and validated successfully compared to a schematic flow case offering analytical solutions. The results of this comparison are qualified of excellent accuracy. To identify the transmissivity field of the realistic study area, the code HySubF-CVFEM/GA was applied to the coastal “Chaouia” groundwater located in Western of Morocco. This aquifer of high heterogeneity is essential for water resources for the Casablanca region. Results analysis of this study has shown that the developed code is capable of providing high accuracy transmissivity fields, thus representing the heterogeneity observed in situ. However, in comparison with gradient method optimization the HySubF-CVFEM/GA code converges too slowly to the optimal solution (large CPU-time consuming). Despite this disadvantage, and given the high accuracy of the obtained results, the HySubF-CVFEM/GA code can be recommended to solve in an efficient and effective manner the identification parameters problems in hydrogeology.


Author(s):  
S. M. Megahed ◽  
K. T. Hamza

Abstract In this paper, a model for simulation of planar flexible link manipulators is presented. Identification of the model parameters is done based on experimental results obtained from separate experiments performed on every link in the manipulator. A simple experimental procedure is used to determine the first three natural frequencies and damping ratios corresponding to them. The experimental data provides guidance in selecting an appropriate number of elements per link for modeling, mass and stiffness parameters and an initial value of damping parameters. Further enhancement of the model is performed through Genetic Algorithm optimization of the damping parameters with the objective function to be minimized defined as the sum of squares of errors between the experimental response and the simulated one. After optimization, the simulated response and the experimental one are closely matching.


2021 ◽  
Vol 4 (4) ◽  
pp. 303-308
Author(s):  
Basayya K. Belleri ◽  
Shravankumar B. Kerur

In the present work, the optimal balancing of the planar six-bar mechanism is investigated to minimize the fluctuations of shaking force and shaking moment. An optimization problem is formulated for balancing the planar six-bar mechanism by developing an objective function. The genetic algorithm and MINITAB software were used to solve the optimization problem. The selection of weighting factors has a crucial role to obtain the optimum values of design parameters. Two sets of weighting factors were considered as per the contribution of X and Y components of the shaking force and shaking moments. Shaking force and shaking moments were minimized drastically and were compared with the original values.


2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Libo Zhang ◽  
Jingjie He ◽  
Su Wang

Inverse kinematic solutions for a dual redundant camera robot in position are examined in order to alleviate operation difficulty and reduce time. The inverse kinematic algorithm is based on a basic genetic algorithm, and the genetic algorithm which is used to solve the problem of a redundant robot is mainly optimized in the joint space. On this basis, the genetic algorithm improvement strategies are studied. In this paper, a genetic algorithm with constrained 2 redundant degrees of freedom (DOF) is proposed through setting 2 parameter variables, with more flexible structure of optimization objective function and more efficient algorithm than basic genetic algorithm. Finally, the result of inverse kinematic algorithm is achieved in terms of the physical prototype.


2005 ◽  
Vol 16 (02) ◽  
pp. 241-260 ◽  
Author(s):  
L. VERMEULEN-JOURDAN ◽  
C. DHAENENS ◽  
E-G. TALBI

In this article, we model a linkage disequilibrium study (genomic study) as an optimization problem where a given objective function has to be optimized. The objective of the study is to discover haplotypes (associations of genetic markers) candidate to explain multi-factorial diseases such as diabetes or obesity. To determine what kind of algorithm will be able to solve this problem, we first study the specificities and the structure of the problem. Results of this study show that exact algorithms are not adapted to this specific problem and lead us to the development of a parallel dedicated adaptive multipopulation genetic algorithm that is able to find several haplotypes of different sizes. After describing the genomic problem, we present the dedicated genetic algorithm, its specificities, such as the use of several populations and its advanced mechanisms such as the adaptive choice of operators, random immigrants, and its parallel implementation. Results on a real dataset are given.


Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1580
Author(s):  
Hou ◽  
Wen

There are many uncertain factors that contribute to process faults and this make it is hard to locate the assignable causes when a process fault occurs. The fuzzy relational equation (FRE) is effective to represent the uncertain relationship between the causes and effects, but the solving difficulties greatly limit its practical utilization. In this paper, the relation between the occurrence degree of abnormal patterns and assignable causes was modeled by FRE. Considering an objective function of least distance between the occurrence degree of abnormal patterns and its assignable cause’s contribution degree determined by FRE, the FRE solution can be obtained by solving an optimization problem with a genetic algorithm (GA). Taking the previous optimization solution as the initial solution of the following run, the GA was run repeatedly. As a result, an optimal interval FRE solution was achieved. Finally, the proposed approach was validated by an application case and some simulation cases. The results show that the model and its solving method are both feasible and effective.


1995 ◽  
Vol 2 (6) ◽  
pp. 445-450 ◽  
Author(s):  
J.-L. Marcelin ◽  
S. Shakhesi ◽  
F. Pourroy

This article deals with the optimal damping of beams constrained by viscoelastic layers when only one or several portions of the beam are covered. The design variables are the dimensions and locations of the viscoelastic layers and the objective function is the maximum damping factor. The discrete design variable optimization problem is solved using a genetic algorithm. Numerical results for minimum and maximum damping are compared to experimental results. This is done for a various number of materials and beams.


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