scholarly journals A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Alejandra Ríos ◽  
Eusebio E. Hernández ◽  
S. Ivvan Valdez
Keyword(s):  
Genetic Algorithm ◽  
Performance Metrics ◽  
Tracking Error ◽  
Parallel Manipulators ◽  
Optimization Method ◽  
Statistical Hypothesis ◽  
Two Stage ◽  
Multi Objective ◽  
Second Stage ◽  
Conflicting Objectives

This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.

Hybrid Hydraulic Vehicle Parameter Optimization using Multi-Objective Genetic Algorithm

2019 ◽  
Vol 16 (3) ◽  
pp. 7007-7018 ◽  
Author(s):  
A. F. Hawary ◽  
M. I. Ramdan
Keyword(s):  
Genetic Algorithm ◽  
Fuel Economy ◽  
Combustion Engine ◽  
Search Algorithm ◽  
Optimization Method ◽  
Hydraulic Motor ◽  
Engine Torque ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Conflicting Objectives

Parameter optimizations of HHV torque distribution must deal with conflicting objectives between the engine torque and fuel economy without compromising the vehicle driving quality. The torque generation from an internal combustion engine (ICE)  is directly influenced by the amount of fuel burnt, hence cannot be solved using a classical single-objective optimization method. In this paper, multi-objective genetic algorithm (MOGA) is used to optimize the power split of a parallel hybrid hydraulic vehicle (HHV) that utilizes an ICE and a hydraulic motor. The simulation runs on three operating modes, engine only, power assist and regenerative modes to optimize two conflicting objectives, engine torque and fuel economy considering both highway and city drive cycles. Using a single unified formulation, a number of design objectives can be simultaneously optimized through a systematic search algorithm within a diverse parameter space. Simulation results have shown both objectives have good compromises that lie along the Pareto optimal front. In comparison, it is observed that there is a significant improvement on fuel economy for HHV as compared to a conventional ICE especially at low-torque operation when the hydraulic motor assists the vehicle for both highway and city drive cycles.    

Optimal Structural Design of a Heat Sink With Laminar Single-Phase Flow Using Computational Fluid Dynamics-Based Multi-Objective Genetic Algorithm

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Ya Ge ◽  
Feng Shan ◽  
Zhichun Liu ◽  
Wei Liu
Keyword(s):  
Genetic Algorithm ◽  
Heat Sink ◽  
Volumetric Flow Rate ◽  
Pareto Solutions ◽  
Nondominated Solutions ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Conflicting Objectives ◽  
Order Preference ◽  
General Method

This paper proposes a general method combining evolutionary algorithm and decision-making technique to optimize the structure of a minichannel heat sink (MCHS). Two conflicting objectives, the thermal resistance θ and the pumping power P, are simultaneously considered to assess the performance of the MCHS. In order to achieve the ultimate optimal design, multi-objective genetic algorithm is employed to obtain the nondominated solutions (Pareto solutions), while technique for order preference by similarity to an ideal solution (TOPSIS) is employed to determine which is the best compromise solution. Meanwhile, both the material cost and volumetric flow rate are fixed where this nonlinear problem is solved by applying the penalty function. The results show that θ of Pareto solutions varies from 0.03707 K W−1 to 0.10742 K W−1, while P varies from 0.00307 W to 0.05388 W, respectively. After the TOPSIS selection, it is found that P is significantly reduced without increasing too much θ. As a result, θ and P of the optimal MCHS determined by TOPSIS are 35.82% and 52.55% lower than initial one, respectively.

Multi-Objective Optimization of Two-Stage Thermo-Electric Cooler Using Differential Evolution

2016 ◽  
pp. 139-170
Author(s):  
Doan V. K. Khanh ◽  
Pandian Vasant ◽  
Irraivan Elamvazuthi ◽  
Vo N. Dieu
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Cooling Rate ◽  
Input Current ◽  
Coefficient Of Performance ◽  
Multi Objective Optimization ◽  
Two Stage ◽  
Thermo Electric ◽  
Multi Objective ◽  
Technical Issues

In this chapter, the technical issues of two-stage TEC were discussed. After that, a new method of optimizing the dimension of TECs using differential evolution to maximize the cooling rate and coefficient of performance was proposed. A input current to hot side and cold side of and the number ratio between the hot stage and cold stage are searched the optima solutions. Thermal resistance is taken into consideration. The results of optimization obtained by using differential evolution were validated by comparing with those obtained by using genetic algorithm and show better performance in terms of stability, computational efficiency, robustness. This work revealed that differential evolution more stable than genetic algorithm and the Pareto front obtained from multi-objective optimization balances the important role between cooling rate and coefficient of performance.

A Structure Optimization for Hooklift Arm Device Based on the Genetic Algorithm

2012 ◽  
Vol 455-456 ◽  
pp. 1504-1508
Author(s):  
Huan Ming Chen ◽  
Da Wei Liu
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Parameter Optimization ◽  
Structure Optimization ◽  
Optimization Method ◽  
Optimized Design ◽  
Design Decision ◽  
Original Design ◽  
Multi Objective ◽  
Engineering Constraint

Based on the theory of FEM, the hooklift arm is modeled with the FEM software, and the structure of the device is optimized with genetic algorithm in a multi-objective/multi-parameter optimization environment, which results in an optimal design decision of the hooklift arm device under the engineering constraint. Comparison between optimized design decision and original design decision shows that the optimization is correct and the proposed multi-objective/multi-parameter optimization method is effective in improving the hooklift arm device.

Concurrent Optimization of Product Module Selection and Assembly Line Configuration: A Multi-Objective Approach

2005 ◽  
Vol 127 (4) ◽  
pp. 875-884 ◽  
Author(s):  
Zhonghui Xu ◽  
Ming Liang
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Solution Procedure ◽  
Programming Approach ◽  
Reconfigurable Manufacturing ◽  
Multi Objective ◽  
Modular Product ◽  
Conflicting Objectives ◽  
Module Selection ◽  
Line Design

Both modular product design and reconfigurable manufacturing have a great potential to enhance responsiveness to market changes and to reduce production cost. However, the two issues have thus far mostly been investigated separately, thereby causing possible mismatch between the modular product structure and the manufacturing or assembly system. Therefore, the potential benefits of product modularity may not be materialized due to such mismatch. For this reason, this paper presents a concurrent approach to the product module selection and assembly line design problems to provide a set of harmonic solutions to the two problems and hence avoid the mismatch between design and manufacturing. The integrated nature of the problem leads to several noncommensurable and often conflicting objectives. The modified Chebyshev goal programming approach is applied to solve the multi-objective problem. A genetic algorithm is further developed to provide quick and near-optimum solutions. The proposed approach and the solution procedure have been applied to an ABS motor problem. The performance of the genetic algorithm has also been examined.

A Novel Two-Stage Design Framework for 2D Spatial Packing of Interconnected Components

2020 ◽  
Author(s):  
Satya R. T. Peddada ◽  
Kai A. James ◽  
James T. Allison
Keyword(s):  
Performance Metrics ◽  
Fluid Pressure ◽  
Optimization Method ◽  
Design Framework ◽  
Two Stage ◽  
Routing Problems ◽  
Routing Problem ◽  
Shortest Path Algorithms ◽  
3D Packing ◽  
Stage 1

Abstract Packing and routing problems separately are each challenging NP-hard problems. Therefore, solving the coupled packing and routing problem simultaneously will require disruptive methods to better address pressing related challenges, such as system volume reduction, interconnect length reduction, ensuring non-intersection, and physics (heat, fluid pressure or electromagnetic) considerations. Here we present a novel two-stage sequential design framework to perform simultaneous physics-based packing and routing optimization. Stage 1 is comprised of generating interference-free initial layouts that are fed to stage 2 as starting points to perform continuous physics-based optimization. Three distinct strategies for stage 1 have been introduced recently, 1) the force-directed layout method (FDLM), 2) an extension of the shortest path algorithms (SPAs) and 3) a unique geometric topology (UGT) generation algorithm. In stage 2, a gradient-based topology optimization method is used to simultaneously optimize both component locations and routing paths of component interconnects. In addition to geometric considerations, this method supports optimization based on system behavior by including physics-based objectives and constraints (e.g., modeled using 1D lumped parameter and 2D finite element physics models). The three layout generation methods developed for stage 1 are compared here with respect to system performance metrics obtained from stage 2. In summary, the design automation framework presented here integrates several elements together as a step toward a more comprehensive solution of 3D packing and routing problems with both geometric and physics considerations.

Multi-Objective Design Optimization of Rotating Regenerative Air Preheater Using Genetic Algorithm

2018 ◽  
Author(s):  
Limin Wang ◽  
Yufan Bu ◽  
Xun Chen ◽  
Xiaoyang Wei ◽  
Dechao Li ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Pressure Drop ◽  
Design Optimization ◽  
Hydraulic Calculation ◽  
Maximum Heat ◽  
Multi Objective ◽  
Upper Limit ◽  
Maximum Heat Transfer ◽  
Conflicting Objectives

In previous references, no study has been done on the optimization of rotary regenerative air preheaters (RAPHs) used in coal-fired power plants yet. The key structure parameters of RAPH include rotor radius, fluid section angles and matrix layer heights. In this study, work on the multi-objective design optimization of an RAPH was conducted by combing the thermal hydraulic calculation program which is developed to calculate the temperature and the pressure drop and the non-dominated sorting genetic algorithm (NSGA-II). The maximum heat transfer rate and the minimum friction, namely minimum outlet gas temperature and pressure drop, are considered as the conflicting objectives in the multi-optimization. The layer heights, rotor radius, angles of fluid sections and heights of matrix layers are involved in the design variables in the optimization. The optimization includes three cases in which the rotor radius upper limits are 7 m, 8 m and 9 m respectively. Sets of the Pareto-optimal front points were obtained for the different cases. The obtained optimal air-preheaters with larger upper limit of rotor radius would have better Pareto results. The optimum rotor radius is the upper limit value for different design range of rotor radius. The air-preheaters with larger upper design limit of rotor radius would have better Pareto results. In other words, if the upper design limit of rotor radius is too small, all the Pareto points in this case could not satisfy the performance requirements of heat transfer and friction, and the only way is to increase the upper design limit of rotor radius. The ratio of each optimum fluid section angle is determined by the fluid flow rate of each section.

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