scholarly journals Optimal Bespoke CDO Design via NSGA-II

2009 ◽  
Vol 2009 ◽  
pp. 1-32
Author(s):  
Diresh Jewan ◽  
Renkuan Guo ◽  
Gareth Witten
Keyword(s):  
Research Work ◽  
Biological Evolution ◽  
Optimal Solution ◽  
Dependence Structure ◽  
Portfolio Optimisation ◽  
Nsga Ii ◽  
Clayton Copula ◽  
Computational Aspects ◽  
Similar Rating ◽  
Theoretical Foundations

This research work investigates the theoretical foundations and computational aspects of constructing optimal bespoke CDO structures. Due to the evolutionary nature of the CDO design process, stochastic search methods that mimic the metaphor of natural biological evolution are applied. For efficient searching the optimal solution, the nondominating sort genetic algorithm (NSGA-II) is used, which places emphasis on moving towards the true Paretooptimal region. This is an essential part of real-world credit structuring problems. The algorithm further demonstrates attractive constraint handling features among others, which is suitable for successfully solving the constrained portfolio optimisation problem. Numerical analysis is conducted on a bespoke CDO collateral portfolio constructed from constituents of the iTraxx Europe IG S5 CDS index. For comparative purposes, the default dependence structure is modelled via Gaussian and Clayton copula assumptions. This research concludes that CDO tranche returns at all levels of risk under the Clayton copula assumption performed better than the sub-optimal Gaussian assumption. It is evident that our research has provided meaningful guidance to CDO traders, for seeking significant improvement of returns over standardised CDOs tranches of similar rating.

scholarly journals Improvement of Daylight Factor Model for Window Size Optimization and Energy Efficient Building Envelope Designs

2021 ◽  
Vol 8 (2) ◽  
pp. 204-221
Author(s):  
Chahrazed Mebarki ◽  
◽  
Essaid Djakab ◽  
Abderrahmane Mejedoub Mokhtari ◽  
Youssef Amrane ◽  
...  
Keyword(s):  
Energy Demand ◽  
Research Work ◽  
Optimal Solution ◽  
Window Size ◽  
Building Envelope ◽  
Nsga Ii ◽  
New Approach ◽  
Building Model ◽  
Heating And Cooling ◽  
The Impact

Based on a new approach for the prediction of the Daylight Factor (DF), using existing empirical models, this research work presents an optimization of window size and daylight provided by the glazed apertures component for a building located in a hot and dry climate. The new approach aims to improve the DF model, considering new parameters for daylight prediction such as the orientation, sky conditions, daytime, and the geographic location of the building to fill in all the missing points that the standard DF, defined for an overcast sky, presents. The enhanced DF model is considered for the optimization of window size based on Non dominated Sorting Genetic Algorithm (NSGA II), for heating and cooling season, taking into account the impact of glazing type, space reflectance and artificial lighting installation. Results of heating and cooling demand are compared to a recommended building model for hot and dry climate with 10% Window to Wall Ratio (WWR) for single glazing. The optimal building model is then validated using a dynamic convective heat transfer simulation. As a result, a reduction of 48% in energy demand and 21.5% in CO2 emissions can be achieved. The present approach provides architects and engineers with a more accurate daylight prediction model considering the effect of several parameters simultaneously. The new proposed approach, via the improved DF model, gives an optimal solution for window design to minimize building energy demand while improving the indoor comfort parameters.

On the Optimal Precoder Design for Energy-Efficient and Secure MIMO Systems

2017 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Tung T. Vu ◽  
Ha Hoang Kha
Keyword(s):  
Energy Efficiency ◽  
Mimo Systems ◽  
Search Algorithm ◽  
Multiple Input Multiple Output ◽  
Research Work ◽  
Computational Cost ◽  
Optimal Solution ◽  
Secrecy Rate ◽  
Highly Nonlinear ◽  
Precoder Design

In this research work, we investigate precoder designs to maximize the energy efficiency (EE) of secure multiple-input multiple-output (MIMO) systems in the presence of an eavesdropper. In general, the secure energy efficiency maximization (SEEM) problem is highly nonlinear and nonconvex and hard to be solved directly. To overcome this difficulty, we employ a branch-and-reduce-and-bound (BRB) approach to obtain the globally optimal solution. Since it is observed that the BRB algorithm suffers from highly computational cost, its globally optimal solution is importantly served as a benchmark for the performance evaluation of the suboptimal algorithms. Additionally, we also develop a low-complexity approach using the well-known zero-forcing (ZF) technique to cancel the wiretapped signal, making the design problem more amenable. Using the ZF based method, we transform the SEEM problem to a concave-convex fractional one which can be solved by applying the combination of the Dinkelbach and bisection search algorithm. Simulation results show that the ZF-based method can converge fast and obtain a sub-optimal EE performance which is closed to the optimal EE performance of the BRB method. The ZF based scheme also shows its advantages in terms of the energy efficiency in comparison with the conventional secrecy rate maximization precoder design.

scholarly journals Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

2019 ◽  
Vol 11 (9) ◽  
pp. 2571
Author(s):  
Xujing Zhang ◽  
Lichuan Wang ◽  
Yan Chen
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Optimal Solution ◽  
Material Distribution ◽  
Low Carbon ◽  
Nsga Ii ◽  
Management Objectives ◽  
Multi Objective Genetic Algorithm ◽  
Distribution Process ◽  
The Cost

Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

Multi-Objective Trajectory Optimization of Free-Floating Space Manipulator Using NSGA-II

2015 ◽  
Vol 713-715 ◽  
pp. 800-804 ◽  
Author(s):  
Gang Chen ◽  
Cong Wei ◽  
Qing Xuan Jia ◽  
Han Xu Sun ◽  
Bo Yang Yu
Keyword(s):  
Trajectory Optimization ◽  
Interpolation Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Joint Space ◽  
Motion Path ◽  
Objective Functions ◽  
Nsga Ii ◽  
Space Manipulator ◽  
Multi Objective

In this paper, a kind of multi-objective trajectory optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed for free-floating space manipulator. The aim is to optimize the motion path of the space manipulator with joint angle constraints and joint velocity constraints. Firstly, the kinematics and dynamics model are built. Secondly, the 3-5-3 piecewise polynomial is selected as interpolation method for trajectory planning of joint space. Thirdly, three objective functions are established to simultaneously minimize execution time, energy consumption and jerk of the joints. At last, the objective functions are combined with the NSGA-II algorithm to get the Pareto optimal solution set. The effectiveness of the mentioned method is verified by simulations.

scholarly journals Multi-objective optimization of solar thermal photovoltaic hybrid power generation system based on NSGA-II algorithm

2021 ◽  
Vol 336 ◽  
pp. 02022
Author(s):  
Liang Meng ◽  
Wen Zhou ◽  
Yang Li ◽  
Zhibin Liu ◽  
Yajing Liu
Keyword(s):  
Power Generation ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Solar Thermal ◽  
Multi Objective Optimization ◽  
Generation System ◽  
Nsga Ii ◽  
Multi Objective ◽  
Hybrid Power ◽  
Power Generation System

In this paper, NSGA-Ⅱ is used to realize the dual-objective optimization and three-objective optimization of the solar-thermal photovoltaic hybrid power generation system; Compared with the optimal solution set of three-objective optimization, optimization based on technical and economic evaluation indicators belongs to the category of multi-objective optimization. It can be considered that NSGA-Ⅱ is very suitable for multi-objective optimization of solar-thermal photovoltaic hybrid power generation system and other similar multi-objective optimization problems.

scholarly journals A two-stage evolutionary optimization approach for an irrigation system design

2016 ◽  
Vol 19 (1) ◽  
pp. 115-122 ◽  
Author(s):  
Milan Cisty ◽  
Zbynek Bajtek ◽  
Lubomir Celar
Keyword(s):  
Water Distribution ◽  
Irrigation System ◽  
Optimal Solution ◽  
Water Distribution Network ◽  
Search Space ◽  
Optimization Method ◽  
Global Optimum ◽  
Optimization Approach ◽  
Nsga Ii ◽  
Irrigation Network

In this work, an optimal design of a water distribution network is proposed for large irrigation networks. The proposed approach is built upon an existing optimization method (NSGA-II), but the authors are proposing its effective application in a new two-step optimization process. The aim of the paper is to demonstrate that not only is the choice of method important for obtaining good optimization results, but also how that method is applied. The proposed methodology utilizes as its most important feature the ensemble approach, in which more optimization runs cooperate and are used together. The authors assume that the main problem in finding the optimal solution for a water distribution optimization problem is the very large size of the search space in which the optimal solution should be found. In the proposed method, a reduction of the search space is suggested, so the final solution is thus easier to find and offers greater guarantees of accuracy (closeness to the global optimum). The method has been successfully tested on a large benchmark irrigation network.

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1454
Keyword(s):  
Fault Detection ◽  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Optimal Solution ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Test

Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

Multi-Objective Optimization Design of Control Devices to Suppress Tall Buildings Vibrations against Earthquake Excitations Using Fuzzy Logic and Genetic Algorithms

2015 ◽  
pp. 787-817
Author(s):  
Saeid Pourzeynali ◽  
Shide Salimi
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Optimization Design ◽  
Base Isolation ◽  
Control Strategies ◽  
Optimal Solution ◽  
Base Shear ◽  
Viscous Dampers ◽  
Nsga Ii ◽  
Isolation Systems

The main objective of this chapter is to find the optimal values of the parameters of the base isolation systems and that of the semi-active viscous dampers using genetic algorithms (GAs) and fuzzy logic in order to simultaneously minimize the buildings' selected responses such as displacement of the top story, base shear, and so on. In this study, performance of base isolation systems, and semi-active viscous dampers are studied separately as different vibration control strategies. In order to simultaneously minimize the objective functions, a fast and elitist non-dominated sorting genetic algorithm (NSGA-II) approach is used to find a set of Pareto-optimal solution. To study the performance of semi-active viscous dampers, the torsional effects exist in the building due to irregularities, and unsymmetrical placement of the dampers is taken into account through 3D modeling of the building.

scholarly journals Multi Objective for PMU Placement in Compressed Distribution Network Considering Cost and Accuracy of State Estimation

2019 ◽  
Vol 9 (7) ◽  
pp. 1515 ◽  
Author(s):  
Kong ◽  
Wang ◽  
Yuan ◽  
Yu
Keyword(s):  
State Estimation ◽  
Estimation Error ◽  
Distribution Network ◽  
Measurement Data ◽  
Optimal Solution ◽  
Pareto Set ◽  
Measurement Unit ◽  
Nsga Ii ◽  
Multi Objective ◽  
Pmu Placement

A phasor measurement unit (PMU) can provide phasor measurements to the distribution network to improve observability. Based on pre-configuration and existing measurements, a network compression method is proposed to reduce PMU candidate locations. Taking the minimum number of PMUs and the lowest state estimation error as the objective functions and taking full observability of distribution network as the constraint, a multi objective model of optimal PMU placement (OPP) is proposed. A hybrid state estimator based on supervisory control and data acquisition (SCADA) and PMU measurements is proposed. To reduce the number of PMUs required for full observability, SCADA measurement data are also considered into the constraint by update and equivalent. In addition, a non-dominated sorting genetic algorithm-II (NSGA-II) is applied to solve the model to get the Pareto set. Finally, the optimal solution is selected from the Pareto set by the technique for order preference by similarity to ideal solution (TOPSIS). The effectiveness of the proposed method is verified by IEEE standard bus systems.

Research on multi-objective optimization of switched flux motor based on improved NSGA-II algorithm

2019 ◽  
Vol 233 (6) ◽  
pp. 1268-1279 ◽  
Author(s):  
Liying Jin ◽  
Shengdun Zhao ◽  
Wei Du ◽  
Xuesong Yang ◽  
Wensheng Wang ◽  
...  
Keyword(s):  
Permanent Magnets ◽  
Optimal Solution ◽  
Torque Ripple ◽  
Maximum Output ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Tooth Width ◽  
The Individual ◽  
Rotor Tooth

In order to optimize the local search efficiency of multi-objective parameters of flux switching permanent motor based on traditional NSGA-II algorithm, an improved NSGA-II (iNSGA-II) algorithm is proposed, with an anti-redundant mutation operator and forward comparison operation designed for quick identification of non-dominated individuals. In the initial stage of the iNSGA-II algorithm, half of the individual populations were randomly generated, while the other half was generated according to feature distribution information. Taking the flux switching permanent motor stator/rotor gap, permanent magnets width, stator tooth width, rotor tooth width and other parameters as optimization variables, the flux switching permanent motor maximum output shaft torque and minimum torque ripple are taken as optimization objectives, thus a multi-objective optimization model is established. Real number coding was adopted for obtaining the Pareto optimal solution of flux switching permanent motor structure parameters. The results showed that the iNSGA-II algorithm is better than the traditional NSGA-II on convergence. A 1.8L TOYOTA PRIUS model was selected as the prototype vehicle. By using the optimized parameters, a joint optimization simulation model was established by calling ADVISOR’s back-office function. The simulation results showed that the entire vehicle’s 100-km acceleration time is under 8 s and the battery’s SOC value maintains at 0.5–0.7 in the entire cycle, implying that the iNSGA-II algorithm optimizes the flux switching permanent motor design and is suitable for the initial design and optimizing calculation of the flux switching permanent motor.

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