On dynamic multi-objective optimization, classification and performance measures

2011 ◽  
Author(s):  
Emilia Tantar ◽  
Alexandru-Adrian Tantar ◽  
Pascal Bouvry
Keyword(s):  
Performance Measures ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
And Performance

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.

Multi-day evaluation of space domain awareness architectures via decision analysis and multi-objective optimization

2022 ◽  
pp. 154851292110677
Author(s):  
Albert R Vasso ◽  
Richard G Cobb ◽  
John M Colombi ◽  
Bryan D Little ◽  
David W Meyer
Keyword(s):  
Decision Analysis ◽  
Performance Measures ◽  
Research Effort ◽  
National Policy ◽  
Space Environment ◽  
Multi Objective Optimization ◽  
Space Object ◽  
Multi Objective ◽  
Average Maximum ◽  
Us Government

The US Government is the world’s de facto provider of space object cataloging data, but it is challenged to maintain pace in an increasingly complex space environment. This work advances a multi-disciplinary approach to better understand and evaluate an underexplored solution recommended by national policy in which current collection capabilities are augmented with non-traditional sensors. System architecting techniques and extant literature identified likely needs, performance measures, and potential contributors to a conceptualized Augmented Network (AN). Multiple hypothetical architectures of ground- and space-based telescopes with representative capabilities were modeled and simulated on four separate days throughout the year, then evaluated against performance measures and constraints using Multi-Objective Optimization. Decision analysis and Pareto optimality identified a small, diverse set of high-performing architectures while preserving design flexibility. Should decision-makers adopt the AN approach, this research effort indicates (1) a threefold increase in average capacity, (2) a 55% improvement in coverage, and (3) a 2.5-h decrease in the average maximum time a space object goes unobserved.

Multi-objective optimization-based method for kinematic posture prediction: development and validation

Robotica ◽  
2010 ◽  
Vol 29 (2) ◽  
pp. 245-253 ◽  
Author(s):  
Jingzhou (James) Yang ◽  
Tim Marler ◽  
Salam Rahmatalla
Keyword(s):  
Performance Measures ◽  
Human Performance ◽  
Design Tool ◽  
Upper Body ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Validation Criteria ◽  
Posture Prediction ◽  
Development And Validation ◽  
Digital Human

SUMMARYPosture prediction plays an important role in product design and manufacturing. There is a need to develop a more efficient method for predicting realistic human posture. This paper presents a method based on multi-objective optimization (MOO) for kinematic posture prediction and experimental validation. The predicted posture is formulated as a multi-objective optimization problem. The hypothesis is that human performance measures (cost functions) govern how humans move. Twelve subjects, divided into four groups according to different percentiles, participated in the experiment. Four realistic in-vehicle tasks requiring both simple and complex functionality of the human simulations were chosen. The subjects were asked to reach the four target points, and the joint centers for the wrist, elbow, and shoulder and the joint angle of the elbow were recorded using a motion capture system. We used these data to validate our model. The validation criteria comprise R-square and confidence intervals. Various physics factors were included in human performance measures. The weighted sum of different human performance measures was used as the objective function for posture prediction. A two-domain approach was also investigated to validate the simulated postures. The coefficients of determinant for both within-percentiles and cross-percentiles are larger than 0.70. The MOO-based approach can predict realistic upper body postures in real time and can easily incorporate different scenarios in the formulation. This validated method can be deployed in the digital human package as a design tool.

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.

Patch size setup and performance/cost trade-offs in multi-objective EM-driven antenna optimization using sequential domain patching

2017 ◽  
Vol 34 (4) ◽  
pp. 1070-1081
Author(s):  
Slawomir Koziel ◽  
Adrian Bekasiewicz
Keyword(s):  
Control Parameter ◽  
Computational Cost ◽  
Design Procedure ◽  
Simulation Models ◽  
Multi Objective Optimization ◽  
Computationally Efficient ◽  
Content Type ◽  
Multi Objective ◽  
Trade Offs ◽  
And Performance

Purpose This paper aims to assess control parameter setup and its effect on computational cost and performance of deterministic procedures for multi-objective design optimization of expensive simulation models of antenna structures. Design/methodology/approach A deterministic algorithm for cost-efficient multi-objective optimization of antenna structures has been assessed. The algorithm constructs a patch connecting extreme Pareto-optimal designs (obtained by means of separate single-objective optimization runs). Its performance (both cost- and quality-wise) depends on the dimensions of the so-called patch, an elementary region being relocated in the course of the optimization process. The cost/performance trade-offs are studied using two examples of ultra-wideband antenna structures and the optimization results are compared to draw conclusions concerning the algorithm robustness and determine the most advantageous control parameter setups. Findings The obtained results indicate that the investigated algorithm is very robust, i.e. its performance is weakly dependent on the control parameters setup. At the same time, it is found that the most suitable setups are those that ensure low computational cost, specifically non-uniform ones generated on the basis of sensitivity analysis. Research limitations/implications The study provides recommendations for control parameter setup of deterministic multi-objective optimization procedure for computationally efficient design of antenna structures. This is the first study of this kind for this particular design procedure, which confirms its robustness and determines the most suitable arrangement of the control parameters. Consequently, the presented results permit full automation of the surrogate-assisted multi-objective antenna optimization process while ensuring its lowest possible computational cost. Originality/value The work is the first comprehensive validation of the sequential domain patching algorithm under various scenarios of its control parameter setup. The considered design procedure along with the recommended parameter arrangement is a robust and computationally efficient tool for fully automated multi-objective optimization of expensive simulation models of contemporary antenna structures.

Sign in / Sign up

Export Citation Format

Share Document