Multi-criteria optimization of the four-finger gripper mechanism

2020 ◽  
Vol 8 (4) ◽  
pp. 276-286
Author(s):  
Vu Duc Quyen ◽  
Andrey Ronzhin
Keyword(s):  
Multicriteria Optimization ◽  
Kinematic Model ◽  
Objective Functions ◽  
Nsga Ii ◽  
Suction Nozzle

Three posterior algorithms NSGA-II, MOGWO and MOPSO to solve the problem of multicriteria optimization of the robotic gripper design are considered. The description of the kinematic model of the developed prototype of the four-fingered gripper for picking tomatoes, its limitations and objective functions used in the optimization of the design are given. The main advantage of the developed prototype is the use of one actuator for the control of the fingers and the suction nozzle. The results of optimization of the kinematic model and the dimensions of the elements of robotic gripper using the considered posterior algorithms are presented.

Multi-Objective Trajectory Optimization of Free-Floating Space Manipulator Using NSGA-II

2015 ◽  
Vol 713-715 ◽  
pp. 800-804 ◽  
Author(s):  
Gang Chen ◽  
Cong Wei ◽  
Qing Xuan Jia ◽  
Han Xu Sun ◽  
Bo Yang Yu
Keyword(s):  
Trajectory Optimization ◽  
Interpolation Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Joint Space ◽  
Motion Path ◽  
Objective Functions ◽  
Nsga Ii ◽  
Space Manipulator ◽  
Multi Objective

In this paper, a kind of multi-objective trajectory optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed for free-floating space manipulator. The aim is to optimize the motion path of the space manipulator with joint angle constraints and joint velocity constraints. Firstly, the kinematics and dynamics model are built. Secondly, the 3-5-3 piecewise polynomial is selected as interpolation method for trajectory planning of joint space. Thirdly, three objective functions are established to simultaneously minimize execution time, energy consumption and jerk of the joints. At last, the objective functions are combined with the NSGA-II algorithm to get the Pareto optimal solution set. The effectiveness of the mentioned method is verified by simulations.

Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

Kinematic Multi-Objective Optimization of Circular-Toothed Gerotor Pumps by Genetic Algorithm

2017 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Design Method ◽  
Third Party ◽  
Low Flow ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Multi Objective ◽  
Pareto Curve

A gerotor gear generation algorithm has been developed that evaluates key performance objective functions to be minimized or maximized, and then an optimization algorithm is applied to determine the best design. Because of their popularity, circular-toothed gerotors are the focus of this study, and future work can extend this procedure to other gear forms. Parametric equations defining the circular-toothed gear set have been derived and implemented. Two objective functions were used in this kinematic optimization: maximize the ratio of displacement to pump radius, which is a measure of compactness, and minimize the kinematic flow ripple, which can have a negative effect on system dynamics and could be a major source of noise. Designs were constrained to ensure drivability, so the need for additional synchronization gearing is eliminated. The NSGA-II genetic algorithm was then applied to the gear generation algorithm in modeFRONTIER, a commercial software that integrates multi-objective optimization with third-party engineering software. A clear Pareto front was identified, and a multi-criteria decision-making genetic algorithm was used to select three optimal designs with varying priorities of compactness vs low flow variation. In addition, three pumps used in industry were scaled and evaluated with the gear generation algorithm for comparison. The scaled industry pumps were all close to the Pareto curve, but the optimized designs offer a slight kinematic advantage, which demonstrates the usefulness of the proposed gerotor design method.

Multi-Objective Geometric Optimization of Elliptical-Toothed Gerotor Pumps for Kinematics and Wear by Genetic Algorithm

2018 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Pareto Front ◽  
Adhesive Wear ◽  
Geometric Optimization ◽  
Objective Functions ◽  
Computationally Efficient ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Flow Ripple ◽  
Gerotor Pumps

A computationally efficient gerotor gear generation algorithm has been developed that creates elliptical-toothed gerotor gear profiles, identifies conditions to guarantee a feasible geometry, evaluates several performance objectives, and is suitable to use for geometric optimization. Five objective functions are used in the optimization: minimize pump size, flow ripple, adhesive wear, subsurface fatigue (pitting), and tooth tip leakage. The gear generation algorithm is paired with the NSGA-II optimization algorithm to minimize each of the objective functions subject to the constraints to define a feasible geometry. The genetic algorithm is run with a population size of 1000 for a total of 500 generations, after which a clear Pareto front is established and displayed. A design has been selected from the Pareto front which is a good compromise between each of the design objectives and can be scaled to any desired displacement. The results of the optimization are also compared to two profile geometries found in literature. Two alternative geometries are proposed that offer much lower adhesive wear while respecting the size constraints of the published profiles and are thought to be an improvement in design.

Objective Reduction Using Axiomatic Design and Product-Related Dependencies: A Layout Synthesis of an Autonomous Greenhouse Case Study

2020 ◽  
Author(s):  
Yann-Seing Law-Kam Cio ◽  
Yuanchao Ma ◽  
Aurelian Vadean ◽  
Giovanni Beltrame ◽  
Sofiane Achiche
Keyword(s):  
Optimization Problem ◽  
Real Life ◽  
Axiomatic Design ◽  
Objective Functions ◽  
Nsga Ii ◽  
Design Problems ◽  
Objective Reduction ◽  
Layout Synthesis ◽  
Design Perspective

Abstract Many-objective optimization problem (MaOP) is defined as optimization with more than 3 objective functions. This high number of objectives makes the comparing solutions more challenging. This holds true for design problems which are MaOPs by nature due to the inherent complexity and multifaceted nature of real-life applications. In the last decades, many strategies have attempted to overcome MaOPs such as removing objectives based on their impact on the optimization. However, from a design perspective, removing objectives could lead to an under optimal, unfeasible or unreliable design. Consequently, objective aggregation seems to be a better approach since objectives can be grouped based on design features controlled by the designers. The proposed methodology uses Axiomatic Design to decompose a system into subsystems or components, and Product-Related Dependencies Management to identify the dependencies between components and formulate the objectives. Then, these objectives are aggregated based on the subsystems found with the Axiomatic Design. The methodology, applied to the layout synthesis of an autonomous greenhouse, can trim down the number of objectives from 15 to 5. Then, using a modified non-dominated sorting genetic algorithm-II (NSGA-II) combined with the objective aggregation, we were able to increase the number of “good” concepts found from 9 to 33 out of a total of 50 obtained designs.

scholarly journals Hydraulic Optimization of Double Chamber Surge Tank Using NSGA-II

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 455 ◽  
Author(s):  
Resham Dhakal ◽  
Jianxu Zhou ◽  
Sunit Palikhe ◽  
Khem Prasad Bhattarai
Keyword(s):  
Water Level ◽  
Optimization Method ◽  
Water Levels ◽  
Transient Processes ◽  
Objective Functions ◽  
Surge Tank ◽  
Nsga Ii ◽  
Reservoir Water ◽  
High Head ◽  
Double Chamber

A surge tank effectively reduces water hammer but experiences water level oscillations during transient processes. A double chamber surge tank is used in high head plants with appreciable variations in reservoir water levels to limit the maximum amplitudes of oscillation by increasing the volume of the surge tank near the extremes of oscillation. Thus, the volume of the chambers and the design of an orifice are the most important factors for controlling the water level oscillations in a double chamber surge tank. Further, maximum/minimum water level in the surge tank and damping of surge waves have conflicting behaviors. Hence, a robust optimization method is required to find the optimum volume of chambers and the diameter of the orifice of the double chamber surge tank. In this paper, the maximum upsurge, the maximum downsurge, and the damping of surge waves are considered as the objective functions for optimization. The worst condition of upsurge and downsurge is determined through 1-D numerical simulation of the hydropower system by using method of characteristics (MOC). Moreover, the sensitivity of dimensions of a double chamber surge tank is studied to find their impact on objective functions; finally, the optimum dimensions of the double chamber surge tank are found using non-dominated sorting genetic algorithm II (NSGA-II) to control the water level oscillations in the surge tank under transient processes. The volume of the optimized double chamber surge tank is only 44.53% of the total volume of the simple surge tank, and it serves as an effective limiter of maximum amplitudes of oscillations. This study substantiates how an optimized double chamber surge tank can be used in high head plants with appreciable variations in reservoir water levels.

scholarly journals Hierarchical Decomposition Synthesis in Optimal Systems Design

1997 ◽  
Vol 119 (4) ◽  
pp. 448-457 ◽  
Author(s):  
R. S. Krishnamachari ◽  
P. Y. Papalambros
Keyword(s):  
Optimal Design ◽  
Optimization Model ◽  
Multicriteria Optimization ◽  
Systems Design ◽  
System Optimization ◽  
Hierarchical Decomposition ◽  
Objective Functions ◽  
Formal Process ◽  
Large Systems ◽  
System Objective

Optimal design of large systems is easier if the optimization model can be decomposed and solved as a set of smaller, coordinated subproblems. Casting a given design problem into a particular optimization model by selecting objectives and constraints is generally a subjective task. In system models where hierarchical decomposition is possible, a formal process for selecting objective functions can be made, so that the resulting optimal design model has an appropriate decomposed form and also possesses desirable properties for the scalar substitute functions used in multicriteria optimization. Such a process is often followed intuitively during the development of a system optimization model by summing selected objectives from each subsystem into a single overall system objective. The more formal process presented in this article is simple to implement and amenable to automation.

Sensors distribution optimization for impact localization using NSGA-II

Sensor Review ◽  
2015 ◽  
Vol 35 (4) ◽  
pp. 409-418 ◽  
Author(s):  
Peng Li ◽  
Yuhua Wang ◽  
Jingru Hu ◽  
Jianmin Zhou
Keyword(s):  
Principal Component ◽  
Optimization Method ◽  
Objective Functions ◽  
Nsga Ii ◽  
Element Analysis ◽  
Content Type ◽  
Time Frequency ◽  
Real World Problem ◽  
Conflicting Objectives ◽  
Impact Localization

Purpose – The purpose of this study which resulted in this work is to propose an optimization method of sensors distribution for structural impact localization. Design/methodology/approach – This paper presents a multi-objective optimization study of a novel sensors distribution technique, where two optimization objective functions are considered: sensors number and sensors location optimization performance index. In addition, the finite element analysis, the time-frequency transform and the principal component analysis are combined to quantize the above objective functions. The non-dominated sorting genetic algorithm II (NSGA-II) is used to acquire Pareto solutions. Findings – The effectiveness of this method is validated through a prototype laboratory called the piezoelectric intelligent structure where promising results are obtained. Originality/value – An optimization method of this novel sensors distribution technique is built and produced a set of efficiency solutions for the real-world problem of impact localization where two conflicting objectives are involved.

Full Three-Dimensional Optimization Platform of Turbine Blades Considering the Film Cooling

2013 ◽  
Author(s):  
Shaopeng Lu ◽  
Zhongran Chi ◽  
Songtao Wang ◽  
Fengbo Wen ◽  
Guotai Feng
Keyword(s):  
Film Cooling ◽  
Turbine Blades ◽  
Three Dimensional ◽  
Optimization Process ◽  
Pareto Optimal ◽  
Cooling Efficiency ◽  
Objective Functions ◽  
Nsga Ii ◽  
Aerodynamic Efficiency ◽  
Film Cooling Efficiency

In this paper, an optimization platform was established with Isight, cfx and the self-programming program which is used to generate the mesh. Film cooling effect can be taken into account. 15 parameters are selected as optimization variables. During the optimization process, the baseline blade and cooling holes are given by parameterized method. There are two objective functions during the optimization process. The first one is aerodynamic efficiency and the second one is film cooling efficiency. As there are two objective functions, NSGA-II is chosen as the multi-objective optimization algorithm. Then the Pareto-optimal front can be got. The results show that aerodynamic efficiency and film cooling efficiency restrict each other. It’s impossible to get the best solutions in one example, so the Pareto optimal set can provide a lot of choices. Different shapes make different effects on the aerodynamic efficiency and film cooling efficiency. From the above, it can be seen that the platform is helpful especially in the case that aerodynamic efficiency and film cooling efficiency restrict each other. This paper also discusses the prospects for platform applications.

scholarly journals NSGA-II algorithm and application for multi-objective flexible workshop scheduling

2020 ◽  
Vol 14 ◽  
pp. 174830262094246
Author(s):  
Wang Yahui ◽  
Shi Ling ◽  
Zhang Cai ◽  
Fu Liuqiang ◽  
Jin Xiangjie
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Objective Functions ◽  
Nsga Ii ◽  
Industrial Processing ◽  
Multi Objective ◽  
Processing Cost

Based on the study of multi-objective flexible workshop scheduling problem and the learning of traditional genetic algorithm, a non-dominated sorting genetic algorithm is proposed to solve and optimize the scheduling model with the objective functions of processing cycle, advance/delay penalty and processing cost. In the process of optimization, non-dominated fast ranking operator and competition operator are used to select the descendant operator, which improves the computational efficiency and optimization ability of the algorithm. Non-repetitive non-dominant solutions and frontier sets are found in the iteration operation to retain the optimal results. Finally, taking a manufacturing workshop as an example, the practicability of the proposed algorithm is verified by the simulation operation of the workshop scheduling information and the comparison with other algorithms. The results show that the algorithm can obtain the optimal solution more quickly than the unimproved algorithm. The improved algorithm is faster and more effective in searching, and has certain feasibility in solving the job shop scheduling problem, which is more suitable for industrial processing and production.

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