A Closeness Degree Method for Multi-Objective Optimization of Belt Transmission

1992 ◽  
Author(s):  
Lin Qun ◽  
Wu Meijuan
Keyword(s):  
Mathematical Model ◽  
Objective Function ◽  
Optimization Design ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Objective Function Values ◽  
Belt Transmission ◽  
Fuzzy Subsets ◽  
The Ideal

Abstract A mathematical model for multi objective optimization design of belt transmission is proposed in this paper. The normal fuzzy distribution is used to convert the ideal and non-inferior solutions into fuzzy subsets over the space of objective function values. The optimal solution which is closest to the ideal one could then be found on the basis of closeness degree method.

Multi-Objective Optimization Design of Screw Conveyor Using Genetic Algorithm

2013 ◽  
Vol 732-733 ◽  
pp. 402-406
Author(s):  
Duan Yi Wang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimization Design ◽  
Optimization Problem ◽  
Machine Design ◽  
Screw Conveyor ◽  
Optimized Design ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
The Mathematical Model

The weight minimum and drive efficiency maxima1 of screw conveyor were considered as double optimizing objects in this paper. The mathematical model of the screw conveyor has been established based on the theory of the machine design, and the genetic algorithm was adopted to solving the multi-objective optimization problem. The results show that the mass of spiral shaft reduces 13.6 percent, and the drive efficiency increases 6.4 percent because of the optimal design based on genetic algorithm. The genetic algorithm application on the screw conveyor optimized design can provided the basis for designing the screw conveyor.

scholarly journals Research on airport multi-objective optimization of stand allocation based on simulated annealing algorithm

2021 ◽  
Vol 18 (6) ◽  
pp. 8314-8330
Author(s):  
Ningning Zhao ◽  
◽  
Mingming Duan
Keyword(s):  
Mathematical Model ◽  
Simulated Annealing ◽  
Objective Function ◽  
Simulated Annealing Algorithm ◽  
Allocation Scheme ◽  
Multi Objective Optimization ◽  
Practical Application ◽  
Allocation Decision ◽  
Multi Objective ◽  
Annealing Algorithm

<abstract> <p>In this study, a multi-objective optimized mathematical model of stand pre-allocation is constructed with the shortest travel distance for passengers, the lowest cost for airlines and the efficiency of stand usage as the overall objectives. The actual data of 12 flights at Lanzhou Zhongchuan Airport are analyzed by application and solved by simulated annealing algorithm. The results of the study show that the total objective function of the constructed model allocation scheme is reduced by 40.67% compared with the actual allocation scheme of the airport, and the distance traveled by passengers is reduced by a total of 4512 steps, while one stand is saved and the efficiency of stand use is increased by 31%, in addition to the reduction of airline cost by 300 RMB. In summary, the model constructed in the study has a high practical application value and is expected to be used for airport stand pre-allocation decision in the future.</p> </abstract>

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.

Study on Multi-Objective Optimization Design of Planetary Gear Reducer

2011 ◽  
Vol 199-200 ◽  
pp. 1180-1184
Author(s):  
Han Zhang ◽  
Xiao Qin Shen ◽  
Fu Sheng Yu ◽  
Jian Hua Liu
Keyword(s):  
Optimization Model ◽  
Optimization Design ◽  
Optimal Solution ◽  
Planetary Gear ◽  
Minimum Volume ◽  
Discrete Variables ◽  
Multi Objective Optimization ◽  
Contact Ratio ◽  
Multi Objective ◽  
Maximum Contact

Various elements in planetary gear reducer design are discussed. A multi-objective optimization model is created. The model requires the minimum volume and the maximum contact ratio. The method of multiplication and division is used to solve the multi-objective problem, and the method of feasibility enumeration is used to handle the discrete variables. The results show that the optimal solution meets the actual requirements.

The Multi-Objective Optimization Design of Leaf Spring of few Piece Variable Cross-Section

2011 ◽  
Vol 213 ◽  
pp. 231-235
Author(s):  
Qin Man Fan ◽  
Qin Man Fan
Keyword(s):  
Cross Section ◽  
Optimization Design ◽  
Optimal Solution ◽  
Variable Cross Section ◽  
Leaf Spring ◽  
Variable Cross ◽  
Multi Objective Optimization ◽  
Solution Quality ◽  
Front Suspension ◽  
Multi Objective

Being the ability of global optimization, MOPSO algorithm have some virtue such as high calculate velocity, good solution quality, great robustness, and so on. In allusion to a leaf spring of few piece variable cross-section, its multi-objective optimization mathematical model was built regarding minimum mass and minimum stiffness deviation as sub-objective functions. Taking the leaf spring of front suspension of a light truck as an example, the Pareto optimal solution set of optimization problem was obtained by using MOPSO algorithm. The optimization results show that the mass of the leaf spring reduced by 24.2% and the stiffness deviation is only 0.32% after optimization by using MOPSO algorithm.

Research on Multi-Objective Optimization Design of Thrust Vector Control Actuator

2012 ◽  
Vol 591-593 ◽  
pp. 15-20
Author(s):  
Jun Zhou ◽  
Jiao Long Zhang ◽  
Feng Qi Zhou
Keyword(s):  
Objective Function ◽  
Vector Control ◽  
Optimization Design ◽  
Nonlinear Problems ◽  
Multi Objective Optimization ◽  
Weighting Method ◽  
Thrust Vector Control ◽  
Multi Objective ◽  
Functional Relations ◽  
Thrust Vector

Aiming at the seriously nonlinear problems of the single nozzle thrust vector control servo system, this paper detailedly deduced the functional relations between the layout of actuators and system dynamic parameters, on the basis of which, a multi-objective optimization model was established with coupling degree, angular asymmetry, as well as length and variation degree of initial swinging arm taken into consideration. Linear weighting method was adopted to convert the multi-objective function into a uni-objective one and an improved genetic algorithm with good robustness was utilized to solve the optimization problem. Calculation results demonstrated that, with this optimization algorithm, sub-objective functions all reach the ideal effects when uni-objective function achieves optimum. The optimization method guarantees that coupling degree, angular asymmetry and swinging arm variation achieve minimum when the initial swinging arm length is at its maximum, which provides theoretical basis for the actuator layout of thrust vector control.

Modified Particle Swarm Optimization Algorithm for Multi-Objective Optimization Design of Hybrid Journal Bearings

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Chia-Wen Chan
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Optimization Design ◽  
High Efficiency ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Modified Particle Swarm Optimization

The objective of design optimization is to determine the design that minimizes the objective function by changing design variables and satisfying design constraints. During multi-objective optimization, which has been widely applied to improve bearing designs, designers must consider several design criteria or objective functions simultaneously. The particle swarm optimization (PSO) method is known for its simple implementation and high efficiency in solving multifactor but single-objective optimization problems. This paper introduces a new multi-objective algorithm (MOA) based on the PSO and Pareto methods that can greatly reduce the number of objective function calls when a suitable swarm size is set.

scholarly journals Locating abrupt disaster emergency logistics centres using improved artificial bee colony (IABC) algorithm

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110162
Author(s):  
Qiang Sun ◽  
Shupei Liu
Keyword(s):  
Objective Function ◽  
Optimal Solution ◽  
Global Search ◽  
Emergency Relief ◽  
Multi Objective ◽  
Bee Colony ◽  
Emergency Logistics ◽  
Objective Function Values ◽  
The Stability ◽  
Better Than

Emergency management is conceptualized as a complex, multi-objective optimization problem related to facility location. However, little research has been performed on the horizontal transportation of emergency logistics centres. This study makes contributions to the multi-objective locating abrupt disaster emergency logistics centres model with the smallest total cost and the largest customer satisfaction. The IABC algorithm is proposed in this paper to solve the multi-objective emergency logistics centres locating problem. IABC algorithm can effectively calculate the optimal location of abrupt disaster emergency logistics centres and the demand for relief materials, and it can solve the rescue time satisfaction for different rescue sites. (1) IABC has better global search capabilities to avoid premature convergence and provide a faster convergence speed, and it has optimal solution accuracy, solution diversity and robustness. (2) From the three optimal objective function values obtained, the optimal objective function values obtained by IABC algorithm are obviously better than ABC and GABC algorithms. (3) From the convergence curves of three objective functions the global search ability and the stability of IABC algorithm are better than those of ABC and GABC algorithm. The improved ABC algorithm has proven to be effective and feasible. However, emergency relief logistics systems are very complex and involve many factors, the proposed model needs to be refined further in the future.

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.

Multi-Objective Optimization of Fine-Pitch Gear Based on Variable-Grain Strategy

2010 ◽  
Vol 44-47 ◽  
pp. 4191-4195
Author(s):  
Fu Qiang Zhao ◽  
Tie Wang
Keyword(s):  
Optimization Design ◽  
Optimal Solution ◽  
Complex Problem ◽  
Friction Torque ◽  
Coarse Grained ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fine Grained ◽  
Fine Pitch ◽  
Tooth Flank

A variable-grain strategy is proposed to tackle the complex problem of optimization design of fine-pitch gear with multiple objectives and restrictions involved. Multi-objective optimization mathematical models of various grains are established, in order to ensure the tooth profile optimal and reduce the noise generated by the slide of tooth flank meshing and the mutation of friction torque as criteria to evaluate different schemes. The coarse-grained model is utilized in the early design to efficiently determine the direction to the globally optimal solution. Then more accurate model can be utilized and finally the optimal scheme can be acquired on the basis of the fine-grained model. Based on the theory of meshing noise and the superiority criteria, the algorithm is put forward for the fine-pitch gear design problem, and the Pareto optimal sets are obtained.

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