Single and Multi-Objective Optimization Issues and Analysis of a 3UPU Parallel Manipulator

2014 ◽  
Author(s):  
Dan Zhang ◽  
Bin Wei
Keyword(s):  
Differential Evolution ◽  
Parallel Manipulator ◽  
Pareto Front ◽  
Multi Objective Optimization ◽  
Multi Objective

Differential evolution (DE) and Pareto front theory are used to optimize stiffness in three directions and workspace of a 3UPU manipulator. Stiffness of the mechanism in each direction is analyzed, and it comes to the fact when stiffness in x and y directions increase, stiffness in z direction will decrease and therefore, the sum of stiffness in x and y and stiffness in z are optimized simultaneously by applying DE, but those two objectives are not in the same scale, a normalization of objectives is therefore considered. Furthermore, workspace volume of the mechanism is analyzed and optimized by using DE. By comparing landscapes of stiffness and workspace volume, one finds stiffness in z has same trend with workspace volume whereas stiffness in x and y and workspace volume conflicts. By employing Pareto front theory and DE, the sum of stiffness in x and y and workspace volume are optimized simultaneously.

Multi-Objective Optimization of a 2-DoF Purely Translational Parallel Manipulator

2011 ◽  
Vol 317-319 ◽  
pp. 794-798
Author(s):  
Zhi Bin Li ◽  
Yun Jiang Lou ◽  
Yong Sheng Zhang ◽  
Ze Xiang Li
Keyword(s):  
Genetic Algorithm ◽  
Kinematic Analysis ◽  
Parallel Manipulator ◽  
Pareto Front ◽  
Parallel Robot ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Evolution Algorithm

The paper addresses the multi-objective optimization of a 2-DoF purely translational parallel manipulator. The kinematic analysis of the Proposed T2 parallel robot is introduced briefly. The objective functions are optimized simultaneously to improve Regular workspace Share (RWS) and Global Conditioning Index (GCI). A Multi-Objective Evolution Algorithm (MOEA) based on the Control Elitist Non-dominated Sorting Genetic Algorithm (controlled ENSGA-II) is used to find the Pareto front. The optimization results show that this method is efficient. The parallel manipulator prototype is also exhibited here.

Aerothermal Optimization of Turbine Squealer Tip Geometries with Arbitrary Cooling Injection

2021 ◽  
pp. 1-12
Author(s):  
Cis De Maesschalck ◽  
Valeria Andreoli ◽  
Guillermo Paniagua ◽  
Tyler Gillen ◽  
Brett Barker
Keyword(s):  
Heat Transfer ◽  
Differential Evolution ◽  
Pareto Front ◽  
Differential Evolution Algorithm ◽  
Thermal Characteristics ◽  
Multi Objective Optimization ◽  
Flow Topology ◽  
Aerodynamic Efficiency ◽  
Multi Objective ◽  
Evolution Algorithm

Abstract The optimization of the turbine tip geometry remains vital to create more efficient and durable engines. Balancing the aerodynamic and thermal aspects, while maintaining mechanical integrity is key to reshape one of the most vulnerable parts of the entire engine. The increasing turbine gas temperatures, combined with the aerodynamically penalizing overtip leakage vortex, makes the design of the tip a truly multidisciplinary challenge. While many earlier efforts focused on uncooled geometries, or studied the aerothermal impact with a fixed cooling configuration, the current paper presents the outcome of a multi-objective optimization where both the squealer rim geometry and cooling injection pattern were allowed to vary simultaneously. This study seeks a further synergistic aerothermal benefit through combining a quasi-fully arbitrary cooling arrangement, with mutating squealer rim structures. Specifically, the current manuscript presents the results of over 330 cooled and uncooled squealer tip geometries. The turbine tip was automatically altered using a novel parametrization strategy, varying both the squealer rim structures as well as the size and location of the cooling holes. The aerodynamic and thermal characteristics of every design were evaluated through 3D CFD, adopting high-density hexahedral grids. A multi-objective differential evolution algorithm was used to obtain a Pareto front which maximizes the aerodynamic efficiency, while minimizing the overtip thermal loads. Eventually, a detailed investigation and robustness study was performed on a set of prime squealer geometries, to further investigate the aerodynamic flow topology, and the effect of various cooling injection schemes on the heat transfer patterns.

DESIGN FOR OPTIMAL PERFORMANCE OF 3-RPS PARALLEL MANIPULATOR USING EVOLUTIONARY ALGORITHMS

2013 ◽  
Vol 37 (2) ◽  
pp. 135-160 ◽  
Author(s):  
Sabbavarapu Ramana Babu ◽  
Vegesina Ramachandra Raju ◽  
Koona Ramji
Keyword(s):  
Parallel Manipulator ◽  
Pareto Front ◽  
General Type ◽  
Jacobian Matrix ◽  
Search Method ◽  
Stiffness Index ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Working Capacity ◽  
Multi Objective

This paper presents an optimal kinematic design for a general type of 3-RPS spatial parallel manipulator based on multi-objective optimization. The objective functions considered are Global Conditioning Index (GCI), Global stiffness Index (GSI) and Workspace volume. The objective functions are optimized simultaneously to improve the dexterity as well as the workspace volume which represents the working capacity of a parallel manipulator. A multi-objective Evolutionary Algorithm based on the control elitist non-dominated sorting genetic algorithm is adopted to find the true optimal Pareto front. A constraint Jacobian matrix is derived analytically and the manipulator workspace is generated by numerical search method. The static analysis of the manipulator is also carried out to determine the compliance of the end-effecter.

NARMAX Model Identification Using Multi-Objective Optimization Differential Evolution

2018 ◽  
Vol 10 (7) ◽  
Author(s):  
Mohd Zakimi Zakaria ◽  
◽  
Zakwan Mansor ◽  
Azuwir Mohd Nor ◽  
Mohd Sazli Saad ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Model Identification ◽  
Multi Objective Optimization ◽  
Multi Objective

PERTURBATION PARAMETERS TUNING OF MULTI-OBJECTIVE OPTIMIZATION DIFFERENTIAL EVOLUTION AND ITS APPLICATION TO DYNAMIC SYSTEM MODELING

2015 ◽  
Vol 75 (11) ◽  
Author(s):  
Mohd Zakimi Zakaria ◽  
Hishamuddin Jamaluddin ◽  
Robiah Ahmad ◽  
Azmi Harun ◽  
Radhwan Hussin ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Dynamic System ◽  
Dynamic Systems ◽  
System Modeling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Structure Selection ◽  
Dynamic System Modeling ◽  
Perturbation Parameters ◽  
Model Structure Selection

This paper presents perturbation parameters for tuning of multi-objective optimization differential evolution and its application to dynamic system modeling. The perturbation of the proposed algorithm was composed of crossover and mutation operators.  Initially, a set of parameter values was tuned vigorously by executing multiple runs of algorithm for each proposed parameter variation. A set of values for crossover and mutation rates were proposed in executing the algorithm for model structure selection in dynamic system modeling. The model structure selection was one of the procedures in the system identification technique. Most researchers focused on the problem in selecting the parsimony model as the best represented the dynamic systems. Therefore, this problem needed two objective functions to overcome it, i.e. minimum predictive error and model complexity.  One of the main problems in identification of dynamic systems is to select the minimal model from the huge possible models that need to be considered. Hence, the important concepts in selecting good and adequate model used in the proposed algorithm were elaborated, including the implementation of the algorithm for modeling dynamic systems. Besides, the results showed that multi-objective optimization differential evolution performed better with tuned perturbation parameters.

Applications of Multiple Objective Genetic Algorithms in the Optimization Design of Tracked Self-Moving Power’s Layout

2013 ◽  
Vol 307 ◽  
pp. 161-165
Author(s):  
Hai Jin ◽  
Jin Fa Xie
Keyword(s):  
Optimization Design ◽  
Pareto Front ◽  
Layout Optimization ◽  
Multiple Objective ◽  
Multi Objective Optimization ◽  
Layout Problem ◽  
Basic Principles ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Set Up

A multi-objective genetic algorithm is applied into the layout optimization of tracked self-moving power. The layout optimization mathematical model was set up. Then introduced the basic principles of NSGA-Ⅱ, which is a Pareto multi-objective optimization algorithm. Finally, NSGA-Ⅱwas presented to solve the layout problem. The algorithm was proved to be effective by some practical examples. The results showed that the algorithm can spread toward the whole Pareto front, and provide many reasonable solutions once for all.

scholarly journals Multi-Objective Optimization Design of an Electrohydrostatic Actuator Based on a Particle Swarm Optimization Algorithm and an Analytic Hierarchy Process

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2426 ◽  
Author(s):  
Bo Yu ◽  
Shuai Wu ◽  
Zongxia Jiao ◽  
Yaoxing Shang
Keyword(s):  
Particle Swarm Optimization ◽  
Power Consumption ◽  
Analytic Hierarchy Process ◽  
Optimization Design ◽  
Pareto Front ◽  
Multi Objective Optimization ◽  
Analytic Hierarchy ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Hierarchy Process

During the last few years, the concept of more-electric aircraft has been pushed ahead by industry and academics. For a more-electric actuation system, the electrohydrostatic actuator (EHA) has shown its potential for better reliability, low maintenance cost and reducing aircraft weight. Designing an EHA for aviation applications is a hard task, which should balance several inconsistent objectives simultaneously, such as weight, stiffness and power consumption. This work presents a method to obtain the optimal EHA, which combines multi-objective optimization with a synthetic decision method, that is, a multi-objective optimization design method, that can combine designers’ preferences and experiences. The evaluation model of an EHA in terms of weight, stiffness and power consumption is studied in the first section. Then, a multi-objective particle swarm optimization (MOPSO) algorithm is introduced to obtain the Pareto front, and an analytic hierarchy process (AHP) is applied to help find the optimal design in the Pareto front. A demo of an EHA design illustrates the feasibility of the proposed method.

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