Kinematic Optimization of the Arm of a Working Machine

2014 ◽  
Author(s):  
Antonio Alba ◽  
Francesco Bucchi ◽  
Francesco Frendo ◽  
Marco Gabiccini
Keyword(s):  
Cost Function ◽  
Optimization Problem ◽  
Link Length ◽  
Weighting Method ◽  
Kinematic Synthesis ◽  
Multi Objective ◽  
Optimization Methodology ◽  
The Cost ◽  
Joint Limits ◽  
Kinematic Optimization

The aim of this work is to develop an optimization methodology for the design of the arm of a small-sized working machine. The workspace and a reference maneuver are firstly defined together with a pre-defined redundant kinematic topology. The kinematic synthesis is framed as a constrained multi-objective problem with respect to link length variables. The constraints consider the capability of the machine to follow the assigned trajectory and to fulfill the joint limits. The cost function incorporates the solution of the inverse kinematics and uses several indices, e.g., total link lengths, manipulability, energy consumption. The multi-objective optimization problem is solved employing the weighting method, converting the initial problem into a single-objective one. The final scalar cost function is minimized by the Nelder-Mead method. On the basis of the outcomes of numerical simulations, the effectiveness and versatility of the developed procedure for the design of novel working machine arms is verified.

scholarly journals Solving the Asymmetry Multi-Objective Optimization Problem in PPPs under LPVR Mechanism by Bi-Level Programing

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1667
Author(s):  
Feiran Liu ◽  
Jun Liu ◽  
Xuedong Yan
Keyword(s):  
Optimization Problem ◽  
Optimal Strategies ◽  
Decision Makers ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Present Value ◽  
Public And Private ◽  
The Public ◽  
Multi Objective ◽  
The Cost

Optimizing the cost and benefit allocation among multiple players in a public-private partnership (PPP) project is recognized to be a multi-objective optimization problem (MOP). When the least present value of revenue (LPVR) mechanism is adopted in the competitive procurement of PPPs, the MOP presents asymmetry in objective levels, control variables and action orders. This paper characterizes this asymmetrical MOP in Stackelberg theory and builds a bi-level programing model to solve it in order to support the decision-making activities of both the public and private sectors in negotiation. An intuitive algorithm based on the non-dominated sorting genetic algorithm III (NSGA III) framework is designed to generate Pareto solutions that allow decision-makers to choose optimal strategies from their own criteria. The effectiveness of the model and algorithm is validated via a real case of a highway PPP project. The results reveal that the PPP project will be financially infeasible without the transfer of certain amounts of exterior benefits into supplementary income for the private sector. Besides, the strategy of transferring minimum exterior benefits is more beneficial to the public sector than to users.

A Study on Multi-Objective Optimization of Abrasive Blasting Systems

2010 ◽  
Vol 126-128 ◽  
pp. 29-34 ◽  
Author(s):  
Vu Ngoc Pi ◽  
Tran Minh Duc
Keyword(s):  
Boron Carbide ◽  
Optimization Problem ◽  
Nozzle Diameter ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Nozzle Wear ◽  
The Cost ◽  
Cost Components ◽  
Abrasive Blasting

This paper introduces a study on a multi-objective optimization problem of abrasive blasting systems. The aim of the study is to find the optimum exchanged diameter of boron carbide nozzles. In the study, the effects of several parameters such as the maximum nozzle diameter, the nozzle wear and the cost components on the optimum initial nozzle diameter were taken into account. From the study, a regression model for determination of the optimum initial diameter of boron carbide nozzles was introduced.

Cognitive Radio Decision Engine Based on Multi-Objective Genetic Algorithm

2011 ◽  
Vol 48-49 ◽  
pp. 314-317
Author(s):  
Di Wu ◽  
Sheng Yao Yang ◽  
J.C. Liu
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
Performance Optimization ◽  
Optimization Problem ◽  
Simulation Analysis ◽  
Multi Objective Optimization ◽  
Weighting Method ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Decision Engine

The performance optimization of cognitive radio is a multi-objective optimization problem. Existing genetic algorithms are difficult to assign the weight of each objective when the linear weighting method is used to simplify the multi-objective optimization problem into a single objective optimization problem. In this paper, we propose a new cognitive decision engine algorithm using multi-objective genetic algorithm with population adaptation. A multicarrier system is used for simulation analysis, and experimental results show that the proposed algorithm is effective and meets the real-time requirement.

Architecture optimization of 4PUS+1PS parallel manipulator

Robotica ◽  
2010 ◽  
Vol 29 (5) ◽  
pp. 683-690 ◽  
Author(s):  
G. Abbasnejad ◽  
H. M. Daniali ◽  
A. Fathi
Keyword(s):  
Parallel Manipulator ◽  
Optimization Problem ◽  
Quantum Particle ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Passive Joint ◽  
A Minor ◽  
The Cost ◽  
Joint Limits ◽  
Architecture Optimization

SUMMARYThe main goal of this paper is the design of 4PUS+1PS parallel manipulator, using an optimization problem that takes into accounts the characteristics of the workspace and dexterity. The optimization problem is formulated considering constraints on actuated and passive joint limits. A comparison between quantum particle swarm Optimization (QPSO) and PSO is developed. Two numerical examples are presented, which reveal the advantages of QPSO to PSO. Moreover, it is shown that by introducing the dexterity index as a quality measure throughout the workspace, the parallel manipulator is improved at the cost of a minor reduction in its workspace.

scholarly journals Multi-objective optimization of retaining wall using genetic algorithm

2021 ◽  
Vol 8 (1-2) ◽  
pp. 58-65
Author(s):  
Filip Dodigović ◽  
Krešo Ivandić ◽  
Jasmin Jug ◽  
Krešimir Agnezović
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Retaining Wall ◽  
Silty Sand ◽  
Embedment Depth ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Definition Of ◽  
The Cost

The paper investigates the possibility of applying the genetic algorithm NSGA-II to optimize a reinforced concrete retaining wall embedded in saturated silty sand. Multi-objective constrained optimization was performed to minimize the cost, while maximizing the overdesign factors (ODF) against sliding, overturning, and soil bearing resistance. For a given change in ground elevation of 5.0 m, the width of the foundation and the embedment depth were optimized. Comparing the algorithm's performance in the cases of two-objective and three objective optimizations showed that the number of objectives significantly affects its convergence rate. It was also found that the verification of the wall against the sliding yields a lower ODF value than verifications against overturning and soil bearing capacity. Because of that, it is possible to exclude them from the definition of optimization problem. The application of the NSGA-II algorithm has been demonstrated to be an effective tool for determining the set of optimal retaining wall designs.

scholarly journals Proliferation of non-linear excitations in the piecewise-linear perceptron

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Antonio Sclocchi ◽  
Pierfrancesco Urbani
Keyword(s):  
Cost Function ◽  
Optimization Problem ◽  
Hard Spheres ◽  
Piecewise Linear ◽  
Power Laws ◽  
Universality Class ◽  
Local Minima ◽  
Convex Optimization Problem ◽  
Non Linear ◽  
The Cost

We investigate the properties of local minima of the energy landscape of a continuous non-convex optimization problem, the spherical perceptron with piecewise linear cost function and show that they are critical, marginally stable and displaying a set of pseudogaps, singularities and non-linear excitations whose properties appear to be in the same universality class of jammed packings of hard spheres. The piecewise linear perceptron problem appears as an evolution of the purely linear perceptron optimization problem that has been recently investigated in [1]. Its cost function contains two non-analytic points where the derivative has a jump. Correspondingly, in the non-convex/glassy phase, these two points give rise to four pseudogaps in the force distribution and this induces four power laws in the gap distribution as well. In addition one can define an extended notion of isostaticity and show that local minima appear again to be isostatic in this phase. We believe that our results generalize naturally to more complex cases with a proliferation of non-linear excitations as the number of non-analytic points in the cost function is increased.

scholarly journals Gradient Span Analysis Method: Application to the Multipoint Aerodynamic Shape Optimization of a Turbine Cascade

2015 ◽  
Vol 137 (9) ◽  
Author(s):  
Hadrien Montanelli ◽  
Marc Montagnac ◽  
François Gallard
Keyword(s):  
Cost Function ◽  
Optimization Problem ◽  
Stokes Equations ◽  
Single Point ◽  
Operating Conditions ◽  
Design Variables ◽  
Wide Range ◽  
The Cost ◽  
Multipoint Optimization ◽  
Utopia Point

This paper presents the application of the gradient span analysis (GSA) method to the multipoint optimization of the two-dimensional LS89 turbine distributor. The cost function (total pressure loss) and the constraint (mass flow rate) are computed from the resolution of the Reynolds-averaged Navier–Stokes equations. The penalty method is used to replace the constrained optimization problem with an unconstrained problem. The optimization process is steered by a gradient-based quasi-Newton algorithm. The gradient of the cost function with respect to design variables is obtained with the discrete adjoint method, which ensures an efficient computation time independent of the number of design variables. The GSA method gives a minimal set of operating conditions to insert into the weighted sum model to solve the multipoint optimization problem. The weights associated to these conditions are computed with the utopia point method. The single-point optimization at the nominal condition and the multipoint optimization over a wide range of conditions of the LS89 blade are compared. The comparison shows the strong advantages of the multipoint optimization with the GSA method and utopia-point weighting over the traditional single-point optimization.

Multi-Objective Optimization Design of Tanker Ships via a Genetic Algorithm

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Marcelo Ramos Martins ◽  
Diego F. Sarzosa Burgos
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Ship Design ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Total Cost ◽  
Multi Objective ◽  
The Cost

The cost of a new ship design heavily depends on the principal dimensions of the ship; however, dimensions minimization often conflicts with the minimum oil outflow (in the event of an accidental spill). This study demonstrates one rational methodology for selecting the optimal dimensions and coefficients of form of tankers via the use of a genetic algorithm. Therein, a multi-objective optimization problem was formulated by using two objective attributes in the evaluation of each design, specifically, total cost and mean oil outflow. In addition, a procedure that can be used to balance the designs in terms of weight and useful space is proposed. A genetic algorithm was implemented to search for optimal design parameters and to identify the nondominated Pareto frontier. At the end of this study, three real ships are used as case studies.

scholarly journals Shared Steering Control for Lane Keeping and Obstacle Avoidance Based on Multi-Objective MPC

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4671
Author(s):  
Yang Liang ◽  
Zhishuai Yin ◽  
Linzhen Nie
Keyword(s):  
Cost Function ◽  
Obstacle Avoidance ◽  
Assessment Method ◽  
Reference Trajectory ◽  
Current Steering ◽  
Steering Control ◽  
Multi Objective ◽  
Driver’S Intention ◽  
The Cost ◽  
Lane Keeping

This paper presents a shared steering control framework for lane keeping and obstacle avoidance based on multi-objective model predictive control. One of the control objectives is to track the reference trajectory, which is updated continuously by the trajectory planning module; whereas the other is to track the driver’s current steering command, so as to consider the driver’s intention. By adding the two control objectives to the cost function of an MPC shared controller, a smooth combination of the commands of the driver and the automation can be achieved through the optimization. The authority of the driver and the automation is allocated by adjusting the weights of the objective terms in the cost function, which is determined by the proposed situation assessment method considering the longitudinal and lateral risks simultaneously. The results of the CarSim-Matlab/Simulink joint simulations show that the proposed shared controller can assist the driver to complete the tasks of lane keeping and obstacle avoidance smoothly while maintaining a good level of vehicle stability.

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