scholarly journals Study on Parameter Optimization Design of Drum Brake Based on Hybrid Cellular Multiobjective Genetic Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-23 ◽  
Author(s):  
Yi Zhang ◽  
Hu Zhang ◽  
Chao Lu
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Parameter Optimization ◽  
Optimization Design ◽  
Pareto Front ◽  
Nsga Ii ◽  
Multiobjective Genetic Algorithm ◽  
Drum Brake ◽  
Multiobjective Problems ◽  
Parameter Optimization Design

In consideration of the significant role the brake plays in ensuring the fast and safe running of vehicles, and since the present parameter optimization design models of brake are far from the practical application, this paper proposes a multiobjective optimization model of drum brake, aiming at maximizing the braking efficiency and minimizing the volume and temperature rise of drum brake. As the commonly used optimization algorithms are of some deficiency, we present a differential evolution cellular multiobjective genetic algorithm (DECell) by introducing differential evolution strategy into the canonical cellular genetic algorithm for tackling this problem. For DECell, the gained Pareto front could be as close as possible to the exact Pareto front, and also the diversity of nondominated individuals could be better maintained. The experiments on the test functions reveal that DECell is of good performance in solving high-dimension nonlinear multiobjective problems. And the results of optimizing the new brake model indicate that DECell obviously outperforms the compared popular algorithm NSGA-II concerning the number of obtained brake design parameter sets, the speed, and stability for finding them.

Brake Parameter Optimization Design Based on MOEA/D

2014 ◽  
Vol 945-949 ◽  
pp. 484-489
Author(s):  
Yong Jian Zhang ◽  
Yi Zhang ◽  
Xing Yu Wan ◽  
Xiao Dong Zheng
Keyword(s):  
Parameter Optimization ◽  
Temperature Rise ◽  
Optimization Design ◽  
Pareto Front ◽  
Multi Objective Optimization ◽  
Practical Application ◽  
Drum Brake ◽  
Engineering Problems ◽  
Simulation Results ◽  
Parameter Optimization Design

The design level of the brake is important in ensuring the vehicles’ fast and safe running. Since the present brake parameter optimization design model can not meet the requirements of the practical application very well, this paper proposes a drum brake multi-objective optimization model aiming at minimizing the volume of the drum brake and minimizing the brake’s temperature rise. The algorithm of MOEA/D is adopted in the simulation. The simulation results indicate that the Pareto front we get is suitable for real and engineering problems, which is helpful for designers to choose appropriate parameters to optimize the brake and provides designers with great consultative value.

scholarly journals Coordinated Control of PV Generation and EVs Charging Based on Improved DECell Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Guo Zhao ◽  
Xueliang Huang ◽  
Hao Qiang
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Control Strategy ◽  
Optimization Model ◽  
Pareto Front ◽  
Optimal Solution ◽  
Coordinated Control ◽  
Nsga Ii ◽  
Nondominated Solutions ◽  
Pv Generation

Recently, the coordination of EVs’ charging and renewable energy has become a hot research all around the globe. Considering the requirements of EV owner and the influence of the PV output fluctuation on the power grid, a three-objective optimization model was established by controlling the EVs charging power during charging process. By integrating the meshing method into differential evolution cellular (DECell) genetic algorithm, an improved differential evolution cellular (IDECell) genetic algorithm was presented to solve the multiobjective optimization model. Compared to the NSGA-II and DECell, the IDECell algorithm showed better performance in the convergence and uniform distribution. Furthermore, the IDECell algorithm was applied to obtain the Pareto front of nondominated solutions. Followed by the normalized sorting of the nondominated solutions, the optimal solution was chosen to arrive at the optimized coordinated control strategy of PV generation and EVs charging. Compared to typical charging pattern, the optimized charging pattern could reduce the fluctuations of PV generation output power, satisfy the demand of EVs charging quantity, and save the total charging cost.

Parameter Optimization Design of Soil Nail Supporting to Deep Foundation Pits

2013 ◽  
Vol 671-674 ◽  
pp. 235-239
Author(s):  
Wei Xu ◽  
Jian Sheng Feng
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Optimization Design ◽  
Hybrid Genetic Algorithm ◽  
Design Parameters ◽  
Deep Foundation ◽  
Soil Nail ◽  
Soil Nail Wall ◽  
Parameter Optimization Design ◽  
The Cost

In order to reduce the cost of soil nail supporting to deep foundation pits and ensure safety as well as stability, a model is established to optimize the design parameters for soil nail wall. This paper is concerned with the analysis of soil nail channel number, diameter, length, horizontal spacing, vertical spacing and dip angle. For parameter optimization design of soil nail wall is a complex problem, the traditional method is easy to fall into local optima. The genetic algorithm is a global optimization method. To some extent it has the drawback of appearing premature convergence and oscillation so as to slow iterative process. Therefore, a forward and backward search algorithm is proposed, which is combined with genetic algorithm. Furthermore some improvement measures are put forward by means of improved hybrid genetic algorithm. As a result engineering studies arrive at an optimum design. It shows that the optimized design results of IHGA not only ensure the stability of foundation, but greatly reduce the cost of engineering materials.

Multi-Objective Geometric Optimization of Elliptical-Toothed Gerotor Pumps for Kinematics and Wear by Genetic Algorithm

2018 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Pareto Front ◽  
Adhesive Wear ◽  
Geometric Optimization ◽  
Objective Functions ◽  
Computationally Efficient ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Flow Ripple ◽  
Gerotor Pumps

A computationally efficient gerotor gear generation algorithm has been developed that creates elliptical-toothed gerotor gear profiles, identifies conditions to guarantee a feasible geometry, evaluates several performance objectives, and is suitable to use for geometric optimization. Five objective functions are used in the optimization: minimize pump size, flow ripple, adhesive wear, subsurface fatigue (pitting), and tooth tip leakage. The gear generation algorithm is paired with the NSGA-II optimization algorithm to minimize each of the objective functions subject to the constraints to define a feasible geometry. The genetic algorithm is run with a population size of 1000 for a total of 500 generations, after which a clear Pareto front is established and displayed. A design has been selected from the Pareto front which is a good compromise between each of the design objectives and can be scaled to any desired displacement. The results of the optimization are also compared to two profile geometries found in literature. Two alternative geometries are proposed that offer much lower adhesive wear while respecting the size constraints of the published profiles and are thought to be an improvement in design.

scholarly journals A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Alberto Pajares ◽  
Xavier Blasco ◽  
Juan M. Herrero ◽  
Gilberto Reynoso-Meza
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Decision Maker ◽  
Pareto Front ◽  
Optimization Problem ◽  
Engineering Problem ◽  
Multiobjective Optimization Problem ◽  
Optimal Solutions ◽  
Multiobjective Genetic Algorithm ◽  
Pi Controllers

Traditionally, in a multiobjective optimization problem, the aim is to find the set of optimal solutions, the Pareto front, which provides the decision-maker with a better understanding of the problem. This results in a more knowledgeable decision. However, multimodal solutions and nearly optimal solutions are ignored, although their consideration may be useful for the decision-maker. In particular, there are some of these solutions which we consider specially interesting, namely, the ones that have distinct characteristics from those which dominate them (i.e., the solutions that are not dominated in their neighborhood). We call these solutions potentially useful solutions. In this work, a new genetic algorithm called nevMOGA is presented, which provides not only the optimal solutions but also the multimodal and nearly optimal solutions nondominated in their neighborhood. This means that nevMOGA is able to supply additional and potentially useful solutions for the decision-making stage. This is its main advantage. In order to assess its performance, nevMOGA is tested on two benchmarks and compared with two other optimization algorithms (random and exhaustive searches). Finally, as an example of application, nevMOGA is used in an engineering problem to optimally adjust the parameters of two PI controllers that operate a plant.

scholarly journals An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Ze Dong ◽  
Hao Jia ◽  
Miao Liu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Majority Vote ◽  
Clustering Algorithms ◽  
Nsga Ii ◽  
Number Of Clusters ◽  
Automatic Data ◽  
Multiobjective Genetic Algorithm ◽  
Fuzzy Clustering Method

This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved.

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