Study on Multi-Objective Reconstruction of Random Media

2016 ◽  
Vol 825 ◽  
pp. 153-160
Author(s):  
Adéla Hlobilová ◽  
Matěj Lepš
Keyword(s):  
Genetic Algorithm ◽  
Random Media ◽  
Probability Function ◽  
Volume Fraction ◽  
Weighted Sum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
Original Medium ◽  
Optimization Routine

This paper deals with a reconstruction of random media via multi-objective optimization. Two statistical descriptors, namely a two-point probability function and a two-point lineal path function, are repetitively evaluated for the original medium and the reconstructed image to appreciate the improvement in the optimization progress. Because of doubts of the weights setting in the weighted-sum method, purely multi-objective optimization routine Non-dominated Sorting Genetic Algorithm~II is utilized. Three operators are compared for creating new offspring populations that satisfy a prescribed volume fraction constraint. The main contribution is in the testing of the proposed methodology on several benchmark images.

Application of the Headstock of CNC Boring Machine for Tractor Engine Cylinder Block Based on Multi-Objective Genetic Algorithm

2021 ◽  
Vol 37 (2) ◽  
pp. 343-349
Author(s):  
Yahui   Wang ◽  
Ling   Shi ◽  
Yiqi   Dang ◽  
Shengkai   Sun ◽  
Huipeng   Zhang
Keyword(s):  
Genetic Algorithm ◽  
Numerical Control ◽  
Gear Train ◽  
List Type ◽  
Weighted Sum ◽  
Cnc Machine ◽  
Nsga Ii ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
Tooth Width

HighlightsThe headstock of the single-sided horizontal CNC boring machine specializing in processing tractor 6-cylinder engine cylinders is optimized.The constraint conditions such as tooth width and modulus are constructed. The model is optimized by the NSGA algorithm, and the optimization results are good.The optimization results of the NSGA algorithm are compared with the results of the weighted sum method and the GA, which highlights the superiority of the NSGA algorithm.ABSTRACT. The tractor is one of the most frequently used equipment in agricultural production, and its mass production is the general trend. With the continuous advancement of the global industrialization process, the importance of Computer Numerical Control (CNC) machine in the entire industrial production has become more and more prominent, and the application of CNC machine in tractor manufacturing has greatly improved production efficiency. This article takes the headstock of a single-sided horizontal CNC boring machine dedicated to processing tractor 6-cylinder engine cylinders as the research objective, takes the key parameters of the gear train in the headstock as the optimization design variables, constructs constraints, such as modulus, tooth width, etc., establishes a multi-objective optimization mathematical model, uses the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to process the model and obtains the Pareto solution set through multiple iterations. The optimization results show that the volume, center distance and the reciprocal of coincidence degree of the main shaft 1 transmission group are reduced in varying degrees. Finally, it is compared with the weighted sum method and genetic algorithm (GA) to highlight the superiority of NSGA-II. Keywords: Headstock, Multi-optimization, Non-dominated Sorting Genetic Algorithm II, Tractor cylinder.

Genetic and hybrid algorithms for optimization of non-singular 3PRR planar parallel kinematics mechanism for machining application

Robotica ◽  
2018 ◽  
Vol 36 (6) ◽  
pp. 839-864 ◽  
Author(s):  
Abdur Rosyid ◽  
Bashar El-Khasawneh ◽  
Anas Alazzam
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Weighted Sum ◽  
Parallel Kinematics ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Cartesian Stiffness Matrix ◽  
Optimal Value ◽  
Single Objective

SUMMARYThis paper proposes a special non-symmetric topology of a 3PRR planar parallel kinematics mechanism, which naturally avoids singularity within the workspace and can be utilized for hybrid kinematics machine tools. Subsequently, single-objective and multi-objective optimizations are conducted to improve the performance. The workspace area and minimum eigenvalue, as well as the condition number of the homogenized Cartesian stiffness matrix across the workspace, have been chosen as the objectives in the optimization based on their relevance to the machining application. The single-objective optimization is conducted by using a single-objective genetic algorithm and a hybrid algorithm, whereas the multi-objective optimization is conducted by using a multi-objective genetic algorithm, a weighted sum single-objective genetic algorithm, and a weighted sum hybrid algorithm. It is shown that the single-objective optimization gives superior value in the optimized objective, while sacrificing the other objectives, whereas the multi-objective optimization compromises the improvement of all objectives by providing non-dominated values. In terms of the algorithms, it is shown that a hybrid algorithm can either verify or refine the optimal value obtained by a genetic algorithm.

scholarly journals Goal-Programming-Based Multi-Objective Optimization in Off-Grid Microgrids

2020 ◽  
Vol 12 (19) ◽  
pp. 8119
Author(s):  
Akhtar Hussain ◽  
Hak-Man Kim
Keyword(s):  
Energy Storage ◽  
Goal Programming ◽  
Optimization Problems ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Weighted Sum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
The Impact

Renewable-based off-grid microgrids are considered as a potential solution for providing electricity to rural and remote communities in an environment-friendly manner. In such systems, energy storage is commonly utilized to cope with the intermittent nature of renewable energy sources. However, frequent usage may result in the fast degradation of energy storage elements. Therefore, a goal-programming-based multi-objective optimization problem has been developed in this study, which considers both the energy storage system (battery and electric vehicle) degradation and the curtailment of loads and renewables. Initially, goals are set for each of the parameters and the objective of the developed model is to minimize the deviations from those set goals. Degradation of battery and electric vehicles is quantified using deep discharging, overcharging, and cycling frequency during the operation horizon. The developed model is solved using two of the well-known approaches used for solving multi-optimization problems, the weighted-sum approach and the priority approach. Five cases are simulated for each of the methods by varying weight/priority of different objectives. Besides this, the impact of weight and priority values selected by policymakers is also analyzed. Simulation results have shown the superiority of the weighted-sum method over the priority method in solving the formulated problem.

Bilevel Adaptive Weighted Sum Method for Multidisciplinary Multi-Objective Optimization

AIAA Journal ◽  
2008 ◽  
Vol 46 (10) ◽  
pp. 2611-2622 ◽  
Author(s):  
Ke-Shi Zhang ◽  
Zhong-Hua Han ◽  
Wei-Ji Li ◽  
Wen-Ping Song
Keyword(s):  
Weighted Sum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
Adaptive Weighted Sum

scholarly journals Multi-load cases topological optimization by weighted sum method based on load case severity degree and ideality

2020 ◽  
Vol 12 (8) ◽  
pp. 168781402094751
Author(s):  
Yongxin Li ◽  
Quanwei Yang ◽  
Tao Chang ◽  
Tao Qin ◽  
Fenghe Wu
Keyword(s):  
Topology Optimization ◽  
Optimization Problem ◽  
Weighted Sum ◽  
Weight Factor ◽  
Multi Objective Optimization ◽  
Load Case ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
Loading Case ◽  
Severity Degree

Mechanical structures always bear multiple loads under working conditions. Topology optimization in multi-load cases is always treated as a multi-objective optimization problem, which is solved by the weighted sum method. However, different weight factor allocation strategies have led to discrepant optimization results, and when ill loading case problems appear, some unreasonable results are obtained by those alternatives. Moreover, many multi-objective optimization problems have certain optimization objective, and an evaluation formula to measure Pareto solution in the multi-objective optimization problem area is lacking. Regarding these two problems, a new method for calculating the weight factor is proposed based on the definition of load case severity degree. Additionally, an amplified load increment is derived and suggested in the minimum compliance with a volume constraint problem. Ideality is formulized from Pareto front to the ideal solution to evaluate the different optimization results. Benchmark topology optimization examples are solved and discussed. The results show that the load case severity degree is less affected by the different weighted sum functions and can avoid ill loading case phenomena, and the ideality of optimization result obtained by the load case severity degree is the best.

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