scholarly journals Multi-Objective Optimization of ORC Systems and Performance Analysis under Off-Design Condition

2015 ◽  
Vol 04 (02) ◽  
pp. 25-35 ◽  
Author(s):  
兰胸 聂
Keyword(s):  
Performance Analysis ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
And Performance

A 5-DOF Hybrid Parallel Robotic Manipulator: Design, Analysis, Performance Modeling and Multi-Objective Optimization

2014 ◽  
Author(s):  
Zhen Gao ◽  
Dan Zhang
Keyword(s):  
Performance Analysis ◽  
Performance Modeling ◽  
Robotic Manipulator ◽  
Structure Design ◽  
Multi Objective Optimization ◽  
System Configuration ◽  
Performance Indices ◽  
Multi Objective ◽  
Manipulator Design ◽  
And Performance

The progress of the 21st century advanced and integrated manufacturing technology highly relies on the development of higher performance robotic system for rapidly adapting to the dramatic change of manufacturing environment and performance-critical applications. Based on this scenario, this research is focused on system configuration, performance analysis and multi-objective optimization of a new hybrid parallel robotic manipulator with two rotations and three translations. The structure design and the kinematic analysis are conducted. The key performance indices including local/global stiffness, local/global dexterity and workspace are modeled, visualized and optimized. The proposed method provides a unique viewpoint for the design optimization of multi-axis machine center based on system hybridization.

scholarly journals A rapid design space exploration approach for multi-objective optimization of DSP filter designs

2021 ◽  
Author(s):  
Aakriti Tarun Sharma
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Systems Design ◽  
Design Solution ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Time Period ◽  
And Performance ◽  
High Level

The process of converting a behavioral specification of an application to its equivalent system architecture is referred to as High Level-Synthesis (HLS). A crucial stage in embedded systems design involves finding the trade off between resource utilization and performance. An exhaustive search would yield the required results, but would take a huge amount of time to arrive at the solution even for smaller designs. This would result in a high time complexity. We employ the use of Design Space Exploration (DSE) in order to reduce the complexity of the design space and to reach the desired results in less time. In reality, there are multiple constraints defined by the user that need to be satisfied simultaneously. Thus, the nature of the task at hand is referred to as Multi-Objective Optimization. In this thesis, the design process of DSP benchmarks was analyzed based on user defined constraints such as power and execution time. The analyzed outcome was compared with the existing approaches in DSE and an optimal design solution was derived in a shorter time period.

On Benchmark Problems and Metrics for Decision Space Performance Analysis in Multi-Objective Optimization

2017 ◽  
Vol 16 (01) ◽  
pp. 1750006 ◽  
Author(s):  
Bin Zhang ◽  
Kamran Shafi ◽  
Hussein Abbass
Keyword(s):  
Performance Analysis ◽  
Fitness Landscape ◽  
Benchmark Problems ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Decision Space ◽  
Nsga Ii ◽  
Multi Objective ◽  
Objective Space ◽  
Problem Instances

A number of benchmark problems exist for evaluating multi-objective evolutionary algorithms (MOEAs) in the objective space. However, the decision space performance analysis is a recent and relatively less explored topic in evolutionary multi-objective optimization research. Among other implications, such analysis can lead to designing more realistic test problems, gaining better understanding about optimal and robust design areas, and design and evaluation of knowledge-based optimization algorithms. This paper complements the existing research in this area and proposes a new method to generate multi-objective optimization test problems with clustered Pareto sets in hyper-rectangular defined areas of decision space. The test problem is parametrized to control number of decision variables, number and position of optimal areas in the decision space and modality of fitness landscape. Three leading MOEAs, including NSGA-II, NSGA-III, and MOEA/D, are evaluated on a number of problem instances with varying characteristics. A new metric is proposed that measures the performance of algorithms in terms of their coverage of the optimal areas in the decision space. The empirical analysis presented in this research shows that the decision space performance may not necessarily be reflective of the objective space performance and that all algorithms are sensitive to population size parameter for the new test problems.

Sign in / Sign up

Export Citation Format

Share Document