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.    

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.

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.

A New Interval Multi-Objective Optimization Method for Uncertain Problems with Dependent Interval Variables

2020 ◽  
Vol 17 (10) ◽  
pp. 2050007
Author(s):  
Guiping Liu ◽  
Rui Luo ◽  
Sheng Liu
Keyword(s):  
Genetic Algorithm ◽  
Iterative Algorithm ◽  
Correlation Coefficients ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Interval Model ◽  
Numerical Examples ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Interval Variables

In this paper, a new interval multi-objective optimization (MOO) method integrating with the multidimensional parallelepiped (MP) interval model has been proposed to handle the uncertain problems with dependent interval variables. The MP interval model is integrated to depict the uncertain domain of the problem, where the uncertainties are described by marginal intervals and the degree of the dependencies among the interval variables is described by correlation coefficients. Then an efficient multi-objective iterative algorithm combining the micro multi-objective genetic algorithm (MOGA) with an approximate optimization method is formulated. Three numerical examples are presented to demonstrate the efficiency of the proposed approach.

scholarly journals Twin Shaft-Geared Crankweb Crankshaft System with Optimization of Crankshaft Dimensions Using Integrated Artificial Neural Network-Multi Objective Genetic Algorithm

2017 ◽  
Vol 47 (1) ◽  
pp. 68-81
Author(s):  
Anierudh Vishwanathan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Journal Bearing ◽  
Combustion Engine ◽  
Design Parameters ◽  
Data Set ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Artificial Neural

This paper suggests a novel design of a multi cylinder internal combustion engine crankshaft which will convert the unnecessary/extra torque provided by the engine into speed of the vehicle. Transmission gear design has been incorporated with crankshaft design to enable the vehicle attain same speed and torque at lower R.P.M resulting in improved fuel economy provided the operating power remains same. This paper also depicts the reduction in the fuel consumption of the engine due to the proposed design of the crankshaft system. In order to accommodate the wear and tear of the crankshaft due to the gearing action, design parameters like crankpin diameter, journal bearing diameter, crankpin fillet radii and journal bearing fillet radii have been optimized for output parameters like stress which has been calculated using finite element analysis with ANSYS Mechanical APDL and minimum volume using integrated Artificial Neural Network-Multi objective genetic algorithm. The data set for the optimization process has been generated using Latin Hypercube Sampling technique.

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.

Optimizing of Turning Parameters Using Multi-Objective Genetic Algorithm

2010 ◽  
Vol 118-120 ◽  
pp. 359-363 ◽  
Author(s):  
Ramezan Ali Mahdavinejad
Keyword(s):  
Genetic Algorithm ◽  
Removal Rate ◽  
Target Function ◽  
Optimization Methods ◽  
Optimization Method ◽  
Surface Finishing ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Engineering Problems ◽  
Stock Removal

In this research, the turning parameters of steel are optimized via multi-objective genetic algorithm and multi-objective harmony research algorithm. These two algorithms are known as strong and powerful tools in optimization of engineering problems. The stock removal rate and surface roughness, as two main of output parameters are the target function and have been considered to be optimized. Since, there are two functions here; we can not use the ordinary optimization method with single-objective algorithm. In steel machining, the stock removal rate usually decreases with the surface finishing and visa versa. Therefore, it is necessary to define the weight of these parameters. In this paper the importance of each of these parameters are determined with weight sum method. In this research, the optimization methods to solve the problems via these two algorithms are discussed first. Then, the steel samples are machined and the output data are analyzed and optimized.

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