scholarly journals Multi-objective optimization design of spur gear based on NSGA-II and decision making

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401882493 ◽  
Author(s):  
Qizhi Yao
Keyword(s):  
Decision Maker ◽  
Optimization Design ◽  
Optimal Solution ◽  
Spur Gear ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Deviation Index ◽  
Decision Variables

Optimization design of spur gear is a complicated work because the performance characteristics depend on different types of decision variables and objectives. Traditional single-objective optimization design of the spur gear always results in poor outcomes relative to other objectives due to objectives’ competition with each other. Therefore, this study works on the spur gear design based on the multi-objective optimization model of elitist non-dominated sorting genetic algorithm (NSGA-II). In the model, gear module, teeth number, and transmission ratio are decision variables, while center distance, bearing capacity coefficient, and meshing efficiency are objectives. Final optimal solutions are picked out from Pareto frontier calculated from NSGA-II using the decision makers of Shannon Entropy, linear programming technique for multidimensional analysis of preference (LINMAP), and technique of order preference by similarity to an ideal solution (TOPSIS). Meanwhile, a deviation index is used to evaluate the reasonable status of the optimal solutions. From triple-objective and dual-objective optimization results, it is found that the optimal solution selected from LINMAP decision maker shows a relatively small deviation index. It indicates that LINMAP decision maker may yield better optimal solution. This study could provide some beneficial information for spur design.

scholarly journals MOEA/D with Adaptive Weight Adjustment

2014 ◽  
Vol 22 (2) ◽  
pp. 231-264 ◽  
Author(s):  
Yutao Qi ◽  
Xiaoliang Ma ◽  
Fang Liu ◽  
Licheng Jiao ◽  
Jianyong Sun ◽  
...  
Keyword(s):  
Optimal Solution ◽  
Weight Vector ◽  
Test Problems ◽  
Great Success ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Decomposition Scheme ◽  
Adaptive Weight

Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved great success in the field of evolutionary multi-objective optimization and has attracted a lot of attention. It decomposes a multi-objective optimization problem (MOP) into a set of scalar subproblems using uniformly distributed aggregation weight vectors and provides an excellent general algorithmic framework of evolutionary multi-objective optimization. Generally, the uniformity of weight vectors in MOEA/D can ensure the diversity of the Pareto optimal solutions, however, it cannot work as well when the target MOP has a complex Pareto front (PF; i.e., discontinuous PF or PF with sharp peak or low tail). To remedy this, we propose an improved MOEA/D with adaptive weight vector adjustment (MOEA/D-AWA). According to the analysis of the geometric relationship between the weight vectors and the optimal solutions under the Chebyshev decomposition scheme, a new weight vector initialization method and an adaptive weight vector adjustment strategy are introduced in MOEA/D-AWA. The weights are adjusted periodically so that the weights of subproblems can be redistributed adaptively to obtain better uniformity of solutions. Meanwhile, computing efforts devoted to subproblems with duplicate optimal solution can be saved. Moreover, an external elite population is introduced to help adding new subproblems into real sparse regions rather than pseudo sparse regions of the complex PF, that is, discontinuous regions of the PF. MOEA/D-AWA has been compared with four state of the art MOEAs, namely the original MOEA/D, Adaptive-MOEA/D, [Formula: see text]-MOEA/D, and NSGA-II on 10 widely used test problems, two newly constructed complex problems, and two many-objective problems. Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.

Multi-objective optimization of squirrel cage fan for range hood based on Kriging model

2021 ◽  
pp. 095440622199586
Author(s):  
Qianhao Xiao ◽  
Jun Wang ◽  
Boyan Jiang ◽  
Weigang Yang ◽  
Xiaopei Yang
Keyword(s):  
Flow Rate ◽  
Optimization Design ◽  
Volume Flow Rate ◽  
Kriging Model ◽  
Volume Flow ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Squirrel Cage

In view of the multi-objective optimization design of the squirrel cage fan for the range hood, a blade parameterization method based on the quadratic non-uniform B-spline (NUBS) determined by four control points was proposed to control the outlet angle, chord length and maximum camber of the blade. Morris-Mitchell criteria were used to obtain the optimal Latin hypercube sample based on the evolutionary operation, and different subsets of sample numbers were created to study the influence of sample numbers on the multi-objective optimization results. The Kriging model, which can accurately reflect the response relationship between design variables and optimization objectives, was established. The second-generation Non-dominated Sorting Genetic algorithm (NSGA-II) was used to optimize the volume flow rate at the best efficiency point (BEP) and the maximum volume flow rate point (MVP). The results show that the design parameters corresponding to the optimization results under different sample numbers are not the same, and the fluctuation range of the optimal design parameters is related to the influence of the design parameters on the optimization objectives. Compared with the prototype, the optimized impeller increases the radial velocity of the impeller outlet, reduces the flow loss in the volute, and increases the diffusion capacity, which improves the volume flow rate, and efficiency of the range hood system under multiple working conditions.

scholarly journals Multi-objective optimization of solar thermal photovoltaic hybrid power generation system based on NSGA-II algorithm

2021 ◽  
Vol 336 ◽  
pp. 02022
Author(s):  
Liang Meng ◽  
Wen Zhou ◽  
Yang Li ◽  
Zhibin Liu ◽  
Yajing Liu
Keyword(s):  
Power Generation ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Solar Thermal ◽  
Multi Objective Optimization ◽  
Generation System ◽  
Nsga Ii ◽  
Multi Objective ◽  
Hybrid Power ◽  
Power Generation System

In this paper, NSGA-Ⅱ is used to realize the dual-objective optimization and three-objective optimization of the solar-thermal photovoltaic hybrid power generation system; Compared with the optimal solution set of three-objective optimization, optimization based on technical and economic evaluation indicators belongs to the category of multi-objective optimization. It can be considered that NSGA-Ⅱ is very suitable for multi-objective optimization of solar-thermal photovoltaic hybrid power generation system and other similar multi-objective optimization problems.

scholarly journals Multi-Objective Optimization Design of a Notch Filter Based on Improved NSGA-II for Conducted Emissions

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 83213-83223 ◽  
Author(s):  
Lu Zhang ◽  
Hongjuan Ge ◽  
Ying Ma ◽  
Jianliang Xue ◽  
Huang Li ◽  
...  
Keyword(s):  
Optimization Design ◽  
Notch Filter ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Conducted Emissions

Hybrid QPSO-NNIA2 Algorithm for Multi-Objective Optimization Problem

2019 ◽  
Vol 33 (08) ◽  
pp. 1959025
Author(s):  
Lan Zhang
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Nearest Neighbor ◽  
Superior Performance ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Evolution Algebra ◽  
Neighbor List

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

Research on multi-objective optimization of switched flux motor based on improved NSGA-II algorithm

2019 ◽  
Vol 233 (6) ◽  
pp. 1268-1279 ◽  
Author(s):  
Liying Jin ◽  
Shengdun Zhao ◽  
Wei Du ◽  
Xuesong Yang ◽  
Wensheng Wang ◽  
...  
Keyword(s):  
Permanent Magnets ◽  
Optimal Solution ◽  
Torque Ripple ◽  
Maximum Output ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Tooth Width ◽  
The Individual ◽  
Rotor Tooth

In order to optimize the local search efficiency of multi-objective parameters of flux switching permanent motor based on traditional NSGA-II algorithm, an improved NSGA-II (iNSGA-II) algorithm is proposed, with an anti-redundant mutation operator and forward comparison operation designed for quick identification of non-dominated individuals. In the initial stage of the iNSGA-II algorithm, half of the individual populations were randomly generated, while the other half was generated according to feature distribution information. Taking the flux switching permanent motor stator/rotor gap, permanent magnets width, stator tooth width, rotor tooth width and other parameters as optimization variables, the flux switching permanent motor maximum output shaft torque and minimum torque ripple are taken as optimization objectives, thus a multi-objective optimization model is established. Real number coding was adopted for obtaining the Pareto optimal solution of flux switching permanent motor structure parameters. The results showed that the iNSGA-II algorithm is better than the traditional NSGA-II on convergence. A 1.8L TOYOTA PRIUS model was selected as the prototype vehicle. By using the optimized parameters, a joint optimization simulation model was established by calling ADVISOR’s back-office function. The simulation results showed that the entire vehicle’s 100-km acceleration time is under 8 s and the battery’s SOC value maintains at 0.5–0.7 in the entire cycle, implying that the iNSGA-II algorithm optimizes the flux switching permanent motor design and is suitable for the initial design and optimizing calculation of the flux switching permanent motor.

PD/PID Controller Design for Seismic Control of High-Rise Buildings Using Multi-Objective Optimization: A Comparative Study with LQR Controller

2017 ◽  
Vol 11 (03) ◽  
pp. 1750009 ◽  
Author(s):  
Sadegh Etedali ◽  
Saeed Tavakoli
Keyword(s):  
Optimization Design ◽  
Tall Buildings ◽  
Pid Controllers ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Seismic Control ◽  
High Rise ◽  
Lqr Controller ◽  
Structural Responses

This paper developed multi-objective optimization design of proportional–derivative (PD) and proportional–integral–derivative (PID) controllers for seismic control of high-rise buildings. The case study is an 11-story realistic building equipped with active tuned mass damper (ATMD). Four earthquakes and nine performance indices are taken into account to assess the performance of the controllers. To create a good trade-off between the performance and robustness of the closed-loop structural system, a non-dominated sorting genetic algorithm, NSGA-II, is employed. To evaluate the degree of robustness of the controllers, four structural models with uncertainties in the nominal model of the structure is considered. For comparison purposes, a linear quadratic regulator (LQR) controller is also designed in the numerical simulations. Simulation results show that the proposed PD and PID controllers significantly perform better than the LQR in reduction of structural responses. Also, it is shown that the LQR does not provide a good performance in strong earthquakes. However, PD and PID controllers are able to significantly reduce structural responses. Moreover, it is shown that the PID has a better performance than the PD. The results also show that the proposed controllers are capable of maintaining a desired performance in the presence of modeling errors. They also have several advantages over modern controllers in terms of simplicity and reduction of required sensors and computational resources in tall buildings.

MULTI-OBJECTIVE OPTIMIZATION OF THERMO-ECOLOGICAL CRITERIA BASED PERFORMANCE PARAMETERS OF REHEAT AND REGENERATIVE BRAYSSON CYCLE

2021 ◽  
pp. 1-36
Author(s):  
R Chandramouli ◽  
G Ravi Kiran Sastry ◽  
S. K. Gugulothu ◽  
M S S Srinivasa Rao
Keyword(s):  
Power Density ◽  
Pressure Ratio ◽  
Optimal Solution ◽  
Coefficient Of Performance ◽  
Optimal Solutions ◽  
Performance Parameters ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Multi Stage ◽  
Ecological Criteria

Abstract The reheat and regenerative Braysson cycle being an alternative for combined cycle power plants needs to be optimized for its efficient utilization of energy resources. Therefore, to obtain the best possible overall pressure ratio, regenerator effectiveness and pressure ratio across multi-stage compression in order to simultaneously maximize exergy efficiency, non-dimensional power density and ecological coefficient of performance for three different maximum temperature situations, multi-objective optimization of the above cycle is carried out using Non-dominated sorting genetic algorithm-II (NSGA-II). The optimal solutions given by the Pareto frontier are further assessed through widely used decision makers namely LINMAP, TOPSIS and Bellman-Zadeh techniques. The optimal solutions attained by the decision making process are further evaluated for their deviation from the non-ideal and ideal solutions. The optimal solution obtained through TOPSIS possess the minimum deviation index. Finally the results are authenticated by performing an error analysis. Such optimal scenarios achieved for the three maximum temperatures are further analysed to achieve the final objective of the most optimal solution which happens to be at 1200K. The simultaneous optimization of performance parameters which reflect the thermo-ecological criteria to be satisfied by a power plant has resulted in values of 0.479, 0.327 & 0.922 for exergy efficiency, non-dimensional power density and ecological coefficient of performance respectively. These optimized performance parameters are obtained for an overall pressure ratio of 7.5, regenerator effectiveness of 0.947 and pressure ratio across multi-stage compression of 1.311.

The Multi-Objective Optimization Design of a Forklift Steering Mechanism

2014 ◽  
Vol 1049-1050 ◽  
pp. 884-887
Author(s):  
Qin Man Fan ◽  
Yong Hai Wu
Keyword(s):  
Hydraulic System ◽  
Optimization Design ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Oil Pump ◽  
System Pressure ◽  
Steering Mechanism ◽  
Steering Torque

The design and quality of steering mechanism is directly related to forklift traction, mobility, steering stability and safe operation. A multi-objective optimization model of the forklift steering mechanism is established in this paper. The objective function is minimum oil cylinder stroke difference and the minimum power oil pump. Steering torque, geometrical angles, geometry size and the hydraulic system pressure are used as constraint conditions. We use non dominated sorting genetic algorithm (NSGA II) based on the Pareto optimal concept to optimize and calculate model and get the optimal design of steering mechanism.

Multi-Objective Optimization Design of Steel Frames Based on Multiple Attribute Decision-Making Method

2013 ◽  
Vol 368-370 ◽  
pp. 830-837
Author(s):  
Mao Qiao Cui ◽  
Hai Yan Huang ◽  
Fu Lai Wang ◽  
Yong Qiu
Keyword(s):  
Decision Making ◽  
Optimization Design ◽  
Intuitionistic Fuzzy Set ◽  
Optimal Solution ◽  
Multiple Attribute Decision Making ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Multiple Attribute ◽  
High Level

This paper describes in detail a multi-objective optimization strategy and decision-making method in the process of steel frame optimization design. A step-by-step analysis process integrating optimization algorithm and model analysis is proposed to solve the present problem. A multi-objective algorithm method using fast Non-dominated Sorting Genetic Algorithm is employed to obtain the Pareto-optimal solution set through an evolutionary optimization process. A high-level multiple attribute decision-making method based on intuitionistic fuzzy set theory is adopted to rank these solutions from the best to worst, and to determine the best solution. An example is used to demonstrate the proposed optimization model and decision-making method.

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