Integrated Optimization of ACLD Treatment for Shape Control of Flexible Beams Using Multi-Objective Genetic Algorithm

Aerospace ◽  
2003 ◽  
Author(s):  
L. C. Hau ◽  
Eric H. K. Fung
Keyword(s):  
Genetic Algorithm ◽  
Shape Control ◽  
Optimization Problem ◽  
Viscoelastic Layer ◽  
Flexible Beams ◽  
Pareto Solution ◽  
Integrated Optimization ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables

This paper presents the use of multi-objective genetic algorithm (MOGA) to solve an integrated optimization problem for the shape control of flexible beams with Active Constrained Layer Damping (ACLD) treatment. The design objectives are to minimize the total weight of the system, the input voltage and the steady-state error between the achieved and desired shapes. Design variables include the thickness of the constraining layer and viscoelastic layer, the length and location of the ACLD patches, as well as the control gains. In order to evaluate the effect of different combinations of design variables on the system performance, the finite element method, in conjunction with the Golla-Hughes-McTavish (GHM) method, is employed to model a clamped-free beam with ACLD patches. As a result of the optimization, a Pareto solution is successfully obtained. It is shown that the MOGA is applicable to the present integrated optimization problem, and ACLD treatment is suitable for shape control of flexible structures.

Integrated Optimization of a Smart Hanger for Garment Inspection Using Multi-Objective Genetic Algorithm

2006 ◽  
Author(s):  
C. W. M. Yuen ◽  
E. H. K. Fung ◽  
W. K. Wong ◽  
L. C. Hau ◽  
K. F. Au
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Transient Behavior ◽  
Settling Time ◽  
Design And Optimization ◽  
Integrated Optimization ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Geometrical Constraints ◽  
Stretching Process

This paper presents the formulation and application of Multi-Objective Genetic Algorithm (MOGA) for the design and optimization of a smart hanger, which simulates the manual handling of garments to facilitate the inspection process. Due to the symmetrical nature of clothes, the hanger is expected to expand only half of the clothes. Thus, a four-link robot consisting of three revolute joints and one prismatic joint is employed to model the hanger. With a Proportional and Derivative (PD) controller, the hanger can regulate the link positions by adjusting the applied torques and force. The objective functions are to (1) maximize the link portions within the "effective regions" at final state, (2) minimize the force or torque required to finish the stretching process, and (3) minimize the settling time of the stretching process. In addition to the control gains, the lengths of the four links as well as the desired movements of the joints are the design variables in this optimization problem. The required transient behavior of the system is defined by the constraints on the settling time and the maximum overshoot. Besides, to prevent the clothes from being destroyed by the links when stretching, geometrical constraints are imposed to the motions of the links. MOGA is applied to tackle this integrated optimization problem. The optimization results are presented in the form of Pareto solutions. After analysis, optimal parameters are selected, and numerical simulations are conducted. Results show the feasibility of the hanger.

Multiobjective Genetic Algorithm Approach to the Optimization of a Rotating Flexible Arm With ACLD Patch

Aerospace ◽  
2004 ◽  
Author(s):  
Eric H. K. Fung ◽  
L. C. Hau ◽  
David T. W. Yau
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Viscoelastic Layer ◽  
Control Voltage ◽  
Constrained Layer Damping ◽  
Integrated Optimization ◽  
Flexible Arm ◽  
Design Variables ◽  
Centrifugal Stiffening ◽  
Viscoelastic Layers

This paper describes the use of multi-objective genetic algorithm (MOGA) to solve an integrated optimization problem of a rotating flexible arm with Active Constrained Layer Damping (ACLD) treatment. The arm is rotating in a horizontal plane with triangular velocity profiles. The ACLD patch is placed at the clamped end of the arm. The design objectives are to minimize the total treatment weight, the control voltage and the tip displacement of the arm, as well as to maximize the passive damping characteristic of the arm. Design variables include the control gains, the maximum angular velocity, the shear modulus of the viscoelastic layer, the thickness of the piezoelectric constraining and viscoelastic layers, and the length of the ACLD patch. The finite element method, in conjunction with the Golla-Hughes-McTavish (GHM) method, is employed to model the ACLD flexible arm to predict its dynamic behavior, in which the effects of centrifugal stiffening due to the rotation are taken into account. Reasonable Pareto solutions are successfully obtained. It is shown that MOGA is applicable to the present integrated optimization problem.

An Improved Constrained Optimization Multi-Objective Genetic Algorithm and its Application in Engineering

2014 ◽  
Vol 962-965 ◽  
pp. 2903-2908
Author(s):  
Yun Lian Liu ◽  
Wen Li ◽  
Tie Bin Wu ◽  
Yun Cheng ◽  
Tao Yun Zhou ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Crossover Operator ◽  
Multi Objective Optimization ◽  
Constrained Optimization Problem ◽  
Great Ability ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

An improved multi-objective genetic algorithm is proposed to solve constrained optimization problems. The constrained optimization problem is converted into a multi-objective optimization problem. In the evolution process, our algorithm is based on multi-objective technique, where the population is divided into dominated and non-dominated subpopulation. Arithmetic crossover operator is utilized for the randomly selected individuals from dominated and non-dominated subpopulation, respectively. The crossover operator can lead gradually the individuals to the extreme point and improve the local searching ability. Diversity mutation operator is introduced for non-dominated subpopulation. Through testing the performance of the proposed algorithm on 3 benchmark functions and 1 engineering optimization problems, and comparing with other meta-heuristics, the result of simulation shows that the proposed algorithm has great ability of global search. Keywords: multi-objective optimization;genetic algorithm;constrained optimization problem;engineering application

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.

Dynamic Optimization of a Three Translational Degrees of Freedom Parallel Robot Based on a Multi-Objective Genetic Algorithm

2015 ◽  
Vol 789-790 ◽  
pp. 723-734
Author(s):  
Xing Guo Lu ◽  
Ming Liu ◽  
Min Xiu Kong
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Optimization ◽  
Degrees Of Freedom ◽  
Optimization Problem ◽  
Parallel Robot ◽  
Nsga Ii ◽  
Multi Objective ◽  
Global Dynamic ◽  
Design Variables ◽  
Dynamic Fluctuation

This work tends to deal with the multi-objective dynamic optimization problem of a three translational degrees of freedom parallel robot. Two global dynamic indices are proposed as the objective functions for the dynamic optimization: the index of dynamic dexterity, the index describing the dynamic fluctuation effects. The length of the linkages and the circumradius of the platforms were chosen as the design variables. A multi-objective optimal design problem, including constrains on the actuating and passive joint angle limits and geometrical interference is then formulated to find the Pareto solutions for the robot in a desired workspace. The Non-dominated Sorting Genetic Algorithm (NSGA-II) is adopted to solve the constrained nonlinear multi-objective optimization problem. The simulation results obtained shows that the robot can achieve better dynamic dexterity and less dynamic fluctuation simultaneously after the optimization.

Multi-Objective Optimization of a Segmented Lunar Wheel Concept

2011 ◽  
Author(s):  
Michele Faragalli ◽  
Damiano Pasini ◽  
Peter Radzizsewski
Keyword(s):  
Optimization Problem ◽  
Element Model ◽  
Multi Objective Optimization ◽  
Stress Concentrations ◽  
Multi Objective ◽  
Optimization Framework ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Rider Comfort ◽  
Original Concept

The goal of this work is to develop a systematic method for optimizing the structural design of a segmented wheel concept to improve its operating performance. In this study, a wheel concept is parameterized into a set of size and shape design variables, and a finite element model of the wheel component is created. A multi-objective optimization problem is formulated to optimize its directional compliance and reduce stress concentrations, which has a direct affect on the efficiency, traction, rider comfort, maneuverability, and reliability of the wheel. To solve the optimization problem, a Matlab-FE simulation loop is built and a multi-objective genetic algorithm is used to find the Pareto front of optimal solutions. A trade-off design is selected which demonstrates an improvement from the original concept. Finally, recommendations will be made to apply the structural optimization framework to alternative wheel conceptual designs.

Applications of Multi-Objective Genetic Algorithm in Optimizing Steering Performances for Submersible

2013 ◽  
Vol 756-759 ◽  
pp. 3136-3140
Author(s):  
Zhuo Yi Yang ◽  
Yong Jie Pang ◽  
Shao Lian Ma
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Approximation Model ◽  
Horizontal Movement ◽  
Nsga Ii ◽  
Hydrodynamic Coefficient ◽  
Pareto Solution ◽  
Multi Objective ◽  
Minimum Drag ◽  
Multi Objective Genetic Algorithm

Multi-objective arithmetic NSGA-II based on Pareto solution is investigated to deal with integrated optimal design of speedability and manoeuvre performances for submersible. Approximation model of resistance for serial revolving shape is constructed by hydrodynamic numerical calculations. The appraisement criterions of stability and mobility are calculated from linear equation of horizontal movement by estimating hydrodynamic coefficient of submersible. After optimization, the scattered Pareto solution of drag and turning diameter are gained, and from the solutions designer can select the reasonable one based on the actual requirement. The Pareto solution can ensure the minimum drag in this manoeuvre performance or the best manoeuvre performance in this drag value.

A Multi-Objective Optimization Genetic Algorithm for SOPC Hardware-Software Partitioning

2012 ◽  
Vol 457-458 ◽  
pp. 1142-1148
Author(s):  
Fu Yang ◽  
Liu Xin ◽  
Pei Yuan Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Multi Objective Optimization ◽  
Key Technology ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Partitioning Problem ◽  
System On Programmable Chip ◽  
Software Partitioning

Hardware-software partitioning is the key technology in hardware-software co-design; the results will determine the design of system directly. Genetic algorithm is a classical search algorithm for solving such combinatorial optimization problem. A Multi-objective genetic algorithm for hardware-software partitioning is presented in this paper. This method can give consideration to both system performance and indicators such as time, power, area and cost, and achieve multi-objective optimization in system on programmable chip (SOPC). Simulation results show that the method can solve the SOPC hardware-software partitioning problem effectively.

Geometric Size Optimization of Annular Step Fin Using Multi-Objective Genetic Algorithm

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Abhijit Deka ◽  
Dilip Datta
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Heat Transfer Rate ◽  
Transfer Rate ◽  
Pareto Optimal ◽  
Objective Functions ◽  
Cross Sectional ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables

Although an annular stepped fin can produce better cooling effect in comparison to an annular disk fin, it is yet to be studied in detail. In the present work, one-dimensional heat transfer in a two-stepped rectangular cross-sectional annular fin with constant base temperature and variable thermal conductivity is modeled as a multi-objective optimization problem. Taking cross-sectional half-thicknesses and outer radii of the two fin steps as design variables, an attempt is made to obtain the efficient fin geometry primarily by simultaneously maximizing the heat transfer rate and minimizing the fin volume. For further assessment of the fin performance, three more objective functions are studied, which are minimization of the fin surface area and maximization of the fin efficiency and effectiveness. Evaluating the heat transfer rate through the hybrid spline difference method, the well-known multi-objective genetic algorithm, namely, nondominated sorting genetic algorithm II (NSGA-II), is employed for approximating the Pareto-optimal front containing a set of tradeoff solutions in terms of different combinations of the considered five objective functions. The Pareto-optimal sensitivity is also analyzed for studying the influences of the design variables on the objective functions. As an outcome, it can be concluded that the proposed procedure would give an open choice to designers to lead to a practical stepped fin configuration.

Decomposition Templates and Joint Morphing Operators for Genetic Algorithm Optimization of Multicomponent Structural Topology

2013 ◽  
Vol 136 (2) ◽  
Author(s):  
Zebin Zhou ◽  
Karim Hamza ◽  
Kazuhiro Saitou
Keyword(s):  
Genetic Algorithm ◽  
Search Space ◽  
Structural Topology ◽  
Algorithm Optimization ◽  
Two Stage ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Loaded Structure ◽  
Monolithic Structure

This paper presents a continuum-based approach for multi-objective topology optimization of multicomponent structures. Objectives include minimization of compliance, weight, and cost of assembly and manufacturing. Design variables are partitioned into two main groups: those pertaining to material allocation within a design domain (base topology problem), and those pertaining to decomposition of a monolithic structure into multiple components (joint allocation problem). Generally speaking, the two problems are coupled in the sense that the decomposition of an optimal monolithic structure is not always guaranteed to produce an optimal multicomponent structure. However, for spot-welded sheet-metal structures (such as those often found in automotive applications), certain assumptions can be made about the performance of a monolithic structure that favor the adoption of a two-stage approach that decouples the base topology and joint allocation problems. A multi-objective genetic algorithm (GA) is used throughout the studies in this paper. While the problem decoupling in two-stage approaches significantly reduces the size of the search space and allows better performance of the GA, the size of the search space can still be quite enormous in the second stage. To further improve the performance, a new mutation operator based on decomposition templates and localized joints morphing is proposed. A cantilever-loaded structure is used to study and compare various setups of single and two-stage GA approaches and establish the merit of the proposed GA operators. The approach is then applied to a simplified model of an automotive vehicle floor subject to global bending loading condition.

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