scholarly journals Multi-Objective Optimisation under Uncertainty with Unscented Temporal Finite Elements

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3010
Author(s):  
Lorenzo A. Ricciardi ◽  
Christie Alisa Maddock ◽  
Massimiliano Vasile
Keyword(s):  
Finite Elements ◽  
Atmospheric Model ◽  
Model Parameters ◽  
Unscented Transformation ◽  
Multi Objective ◽  
Trade Offs ◽  
Collaborative Search ◽  
Multi Agent ◽  
Quantification Model ◽  
The Impact

This paper presents a novel method for multi-objective optimisation under uncertainty developed to study a range of mission trade-offs, and the impact of uncertainties on the evaluation of launch system mission designs. A memetic multi-objective optimisation algorithm, named MODHOC, which combines the Direct Finite Elements in Time transcription method with Multi Agent Collaborative Search, is extended to account for model uncertainties. An Unscented Transformation is used to capture the first two statistical moments of the quantities of interest. A quantification model of the uncertainty was developed for the atmospheric model parameters. An optimisation under uncertainty was run for the design of descent trajectories for a spaceplane-based two-stage launch system.

scholarly journals Multi-agent collaborative search: an agent-based memetic multi-objective optimization algorithm applied to space trajectory design

2011 ◽  
Vol 225 (11) ◽  
pp. 1211-1227 ◽  
Author(s):  
M Vasile ◽  
F Zuiani
Keyword(s):  
Selection Criterion ◽  
Search Space ◽  
Trajectory Design ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Agent Based ◽  
Multi Objective ◽  
Collaborative Search ◽  
Multi Agent ◽  
Space Trajectory Design

This article presents an algorithm for multi-objective optimization that blends together a number of heuristics. A population of agents combines heuristics that aim at exploring the search space both globally and in a neighbourhood of each agent. These heuristics are complemented with a combination of a local and global archive. The novel agent-based algorithm is tested at first on a set of standard problems and then on three specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi-objective optimization algorithms that use the Pareto dominance as selection criterion: non-dominated sorting genetic algorithm (NSGA-II), Pareto archived evolution strategy (PAES), multiple objective particle swarm optimization (MOPSO), and multiple trajectory search (MTS). The results demonstrate that the agent-based search can identify parts of the Pareto set that the other algorithms were not able to capture. Furthermore, convergence is statistically better although the variance of the results is in some cases higher.

Multi-Objective Optimization Under Uncertainty of Part Quality in Fused Filament Fabrication

2021 ◽  
Author(s):  
Berkcan Kapusuzoglu ◽  
Paromita Nath ◽  
Matthew Sato ◽  
Sankaran Mahadevan ◽  
Paul Witherell
Keyword(s):  
Process Parameters ◽  
Epistemic Uncertainty ◽  
Optimization Under Uncertainty ◽  
Model Parameters ◽  
Multi Objective Optimization ◽  
Bond Quality ◽  
Fused Filament Fabrication ◽  
Multi Objective ◽  
Part Quality ◽  
Trade Offs

Abstract This work presents a data-driven methodology for multi-objective optimization under uncertainty of process parameters in the fused filament fabrication (FFF) process. The proposed approach optimizes the process parameters with the objectives of minimizing the geometric inaccuracy and maximizing the filament bond quality of the manufactured part. First, experiments are conducted to collect data pertaining to the part quality. Then, Bayesian neural network (BNN) models are constructed to predict the geometric inaccuracy and bond quality as functions of the process parameters. The BNN model captures the model uncertainty caused by the lack of knowledge about model parameters (neuron weights) and the input variability due to the intrinsic randomness in the input parameters. Using the stochastic predictions from these models, different robustness-based design optimization formulations are investigated, wherein process parameters such as nozzle temperature, nozzle speed, and layer thickness are optimized under uncertainty for different multi-objective scenarios. Epistemic uncertainty in the prediction model and the aleatory uncertainty in the input are considered in the optimization. Finally, Pareto surfaces are constructed to estimate the trade-offs between the objectives. Both the BNN models and the effectiveness of the proposed optimization methodology are validated using actual manufacturing of the parts.

scholarly journals A Novel Framework for Applying Multiobjective GA and PSO Based Approaches for Simultaneous Area, Delay, and Power Optimization in High Level Synthesis of Datapaths

VLSI Design ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
D. S. Harish Ram ◽  
M. C. Bhuvaneswari ◽  
Shanthi S. Prabhu
Keyword(s):  
High Level Synthesis ◽  
Synthesis Process ◽  
Weighted Sum ◽  
Early Assessment ◽  
Nsga Ii ◽  
Multi Objective ◽  
Trade Offs ◽  
Evolutionary Technique ◽  
High Level ◽  
The Impact

High-Level Synthesis deals with the translation of algorithmic descriptions into an RTL implementation. It is highly multi-objective in nature, necessitating trade-offs between mutually conflicting objectives such as area, power and delay. Thus design space exploration is integral to the High Level Synthesis process for early assessment of the impact of these trade-offs. We propose a methodology for multi-objective optimization of Area, Power and Delay during High Level Synthesis of data paths from Data Flow Graphs (DFGs). The technique performs scheduling and allocation of functional units and registers concurrently. A novel metric based technique is incorporated into the algorithm to estimate the likelihood of a schedule to yield low-power solutions. A true multi-objective evolutionary technique, “Nondominated Sorting Genetic Algorithm II” (NSGA II) is used in this work. Results on standard DFG benchmarks indicate that the NSGA II based approach is much faster than a weighted sum GA approach. It also yields superior solutions in terms of diversity and closeness to the true Pareto front. In addition a framework for applying another evolutionary technique: Weighted Sum Particle Swarm Optimization (WSPSO) is also reported. It is observed that compared to WSGA, WSPSO shows considerable improvement in execution time with comparable solution quality.

scholarly journals MULTIOBJECTIVE OPTIMIZATION MODEL FOR SCHEDULING OF CONSTRUCTION PROJECTS UNDER EXTREME WEATHER

2016 ◽  
Vol 22 (3) ◽  
pp. 373-381 ◽  
Author(s):  
Ahmed B. SENOUCI ◽  
Saleh A. MUBARAK
Keyword(s):  
Optimization Model ◽  
Construction Projects ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Construction Time ◽  
Multi Objective ◽  
Trade Offs ◽  
The Impact ◽  
Construction Schedules ◽  
Cost Of Construction

Extreme weather significantly impacts construction schedules and costs and can be a source of schedule de­lays and budget overruns. A multi-objective optimization model, presented herein for the scheduling of construction projects under extreme weather conditions, can generate optimal/near optimal schedules that minimize the time and cost of construction projects in extreme weather regions. The model computations are organized as follows: (1) a scheduling module for developing practical schedules for construction projects, (2) a cost module for computing total project cost, and (3) a multi-objective module for determining optimal/near optimal trade-offs between project time and cost. Two practical examples of the effects of extreme weather on construction time and direct cost are provided, the first of which shows the impact of extreme weather on construction time and cost, and the second of which demonstrates the ability of the model to generate and visually present the optimal trade-offs between the duration and costs of construction projects under extreme weather conditions.

scholarly journals Robust Aerodynamic Design of Nacelles for Future Civil Aero-Engines

2020 ◽  
Author(s):  
B. Deneys J. Schreiner ◽  
Fernando Tejero ◽  
David G. MacManus ◽  
Christopher Sheaf
Keyword(s):  
Design Method ◽  
Operating Conditions ◽  
Propulsive Efficiency ◽  
Multi Objective ◽  
Fuel Burn ◽  
Trade Offs ◽  
Specific Thrust ◽  
Aero Engines ◽  
The Impact ◽  
Bypass Ratio

Abstract As the growth of aviation continues it is necessary to minimise the impact on the environment, through reducing NOx emissions, fuel-burn and noise. In order to achieve these goals, the next generation of Ultra-High Bypass Ratio engines are expected to increase propulsive efficiency through operating at reduced specific thrust. Consequently, there is an expected increase in fan diameter and the associated potential penalties of nacelle drag and weight. In order to ensure that these penalties do not negate the benefits obtained from the new engine cycles, it is envisaged that future civil aero-engines will be mounted in compact nacelles. While nacelle design has traditionally been tackled by multi-objective optimisation at different flight conditions within the cruise segment, it is anticipated that compact configurations will present larger sensitivity to off-design conditions. Therefore, a design method that considers the different operating conditions that are met within the full flight envelope is required for the new nacelle design challenge. The method is employed to carry out multi-point multi-objective optimisation of axisymmetric aero-lines at different transonic and subsonic operating conditions. It considers mid-cruise conditions, end-of-cruise conditions, the sensitivity to changes in flight Mach number, windmilling conditions with a cruise engine-out case and an engine-out diversion scenario. Optimisation routines were conducted for a conventional nacelle and a future aero-engine architecture, upon which the aerodynamic trade-offs between the different flight conditions are discussed. Subsequently, the tool has been employed to identify the viable nacelle design space for future compact civil aero-engines for a range of nacelle lengths.

scholarly journals A utility-based analysis of equilibria in multi-objective normal-form games

2020 ◽  
Vol 35 ◽  
Author(s):  
Roxana Rădulescu ◽  
Patrick Mannion ◽  
Yijie Zhang ◽  
Diederik M. Roijers ◽  
Ann Nowé
Keyword(s):  
Normal Form ◽  
Optimization Criterion ◽  
Expected Returns ◽  
Objective Functions ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Normal Form Games ◽  
Multi Objective ◽  
Trade Offs ◽  
Multi Agent

Abstract In multi-objective multi-agent systems (MOMASs), agents explicitly consider the possible trade-offs between conflicting objective functions. We argue that compromises between competing objectives in MOMAS should be analyzed on the basis of the utility that these compromises have for the users of a system, where an agent’s utility function maps their payoff vectors to scalar utility values. This utility-based approach naturally leads to two different optimization criteria for agents in a MOMAS: expected scalarized returns (ESRs) and scalarized expected returns (SERs). In this article, we explore the differences between these two criteria using the framework of multi-objective normal-form games (MONFGs). We demonstrate that the choice of optimization criterion (ESR or SER) can radically alter the set of equilibria in a MONFG when nonlinear utility functions are used.

scholarly journals Multi-Objective Optimisation for Power System Planning Integrating Sustainability Indicators

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2199
Author(s):  
Taimur Al Shidhani ◽  
Anastasia Ioannou ◽  
Gioia Falcone
Keyword(s):  
Power Generation ◽  
Land Use ◽  
Renewable Energy ◽  
Power System ◽  
Carbon Emissions ◽  
Fossil Fuels ◽  
Objective Functions ◽  
Multi Objective ◽  
Trade Offs ◽  
The Impact

The increase in global electricity demand, along with its impact on climate change, call for integrating sustainability aspects in the power system expansion planning. Sustainable power generation planning needs to fulfill different, often contradictory, objectives. This paper proposes a multi-objective optimisation model integrating four objective functions, including minimisation of total discounted costs, carbon emissions, land use, and social opposition. Other factors addressed in the model include renewable energy share, jobs created, mortality rates, and energy diversity, among others. Single-objective linear optimisations are initially performed to investigate the impact of each objective function on the resulting power generation mix. Minimising land use and discounted total costs favoured fossil fuels technologies, as opposed to minimising carbon emissions, which resulted in increased renewable energy shares. Minimising social opposition also favoured renewable energy shares, except for hydropower and onshore wind technologies. Accordingly, to investigate the trade-offs among the objective functions, Pareto front candidates for each pair of objective functions were generated, indicating a strong correlation between the minimisation of carbon emissions and the social opposition. Limited trade-offs were also observed between the minimisation of costs and land use. Integrating the objective functions in the multi-objective model resulted in various non-dominated solutions. This tool aims to enable decision-makers identify the trade-offs when optimising the power system under different objectives and determine the most suitable electricity generation mix.

Estimating OLED Display Device Lifetime from pixel and screen brightness and its application

2019 ◽  
Vol 2019 (1) ◽  
pp. 331-338 ◽  
Author(s):  
Jérémie Gerhardt ◽  
Michael E. Miller ◽  
Hyunjin Yoo ◽  
Tara Akhavan
Keyword(s):  
Image Processing ◽  
Power Consumption ◽  
Model Parameters ◽  
Display Device ◽  
Image Processing Algorithm ◽  
Pixel Value ◽  
Device Lifetime ◽  
The Impact ◽  
Oled Display

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.

scholarly journals Carbon Dynamics in the Northeastern Qinghai–Tibetan Plateau from 1990 to 2030 Using Landsat Land Use/Cover Change Data

2020 ◽  
Vol 12 (3) ◽  
pp. 528 ◽  
Author(s):  
Jingye Li ◽  
Jian Gong ◽  
Jean-Michel Guldmann ◽  
Shicheng Li ◽  
Jie Zhu
Keyword(s):  
Land Use ◽  
Tibetan Plateau ◽  
Carbon Storage ◽  
Sharp Decrease ◽  
Total Carbon ◽  
Qinghai Lake ◽  
Lake Basin ◽  
Ecosystem Carbon ◽  
Trade Offs ◽  
The Impact

Land use/cover change (LUCC) has an important impact on the terrestrial carbon cycle. The spatial distribution of regional carbon reserves can provide the scientific basis for the management of ecosystem carbon storage and the formulation of ecological and environmental policies. This paper proposes a method combining the CA-based FLUS model and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to assess the temporal and spatial changes in ecosystem carbon storage due to land-use changes over 1990–2015 in the Qinghai Lake Basin (QLB). Furthermore, future ecosystem carbon storage is simulated and evaluated over 2020–2030 under three scenarios of natural growth (NG), cropland protection (CP), and ecological protection (EP). The long-term spatial variations in carbon storage in the QLB are discussed. The results show that: (1) Carbon storage in the QLB decreased at first (1990–2000) and increased later (2000–2010), with total carbon storage increasing by 1.60 Tg C (Teragram: a unit of mass equal to 1012 g). From 2010 to 2015, carbon storage displayed a downward trend, with a sharp decrease in wetlands and croplands as the main cause; (2) Under the NG scenario, carbon reserves decrease by 0.69 Tg C over 2020–2030. These reserves increase significantly by 6.77 Tg C and 7.54 Tg C under the CP and EP scenarios, respectively, thus promoting the benign development of the regional ecological environment. This study improves our understanding on the impact of land-use change on carbon storage for the QLB in the northeastern Qinghai–Tibetan Plateau (QTP).

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