scholarly journals Efficient multi-objective optimization methods for computationally intensive urban water resources models

2014 ◽  
Vol 17 (1) ◽  
pp. 36-55 ◽  
Author(s):  
Mohammad Mortazavi-Naeini ◽  
George Kuczera ◽  
Lijie Cui
Keyword(s):  
Ant Colony Optimization ◽  
Water Resource ◽  
Optimization Methods ◽  
Optimization Method ◽  
Ant Colony ◽  
Urban Water ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

Multi-objective optimization methods require many thousands of objective function evaluations. For urban water resource problems such evaluations can be computationally very expensive. The question as to which optimization method is the best choice for a given function evaluations budget in urban water resource problems remains unexplored. The main objective of this paper is to address this question. The second objective is to develop a new optimization algorithm, efficient multi-objective ant colony optimization-I (EMOACO-I), which exploits the good performance of ant colony optimization enhanced using ideas borrowed from evolutionary optimization. Its performance was compared against three established methods (NSGA-II, SMPSO, εMOEA) using two case studies based on the urban water resource systems serving two major Australian cities. The case study problems involved two or three objectives and 10 or 13 decision variables affecting infrastructure investment and system operation. The results show that NSGA-II was the worst performing method. However, none of the remaining methods was unambiguously superior. For example, while EMOACO-I converged more rapidly, its diversity was comparable but not superior to the other methods. Greater differences in performance were found as the number of objectives and case study complexity increased. This suggests that pooling the results from a number of methods could help guard against the vagaries in performance of individual methods.

scholarly journals Comparison of the Economic and Environmental Performance of V2H and Residential Stationary Battery: Development of a Multi-Objective Optimization Method for Homes of EV Owners

2019 ◽  
Vol 10 (4) ◽  
pp. 78
Author(s):  
Ryosuke Kataoka ◽  
Akira Shichi ◽  
Hiroyuki Yamada ◽  
Yumiko Iwafune ◽  
Kazuhiko Ogimoto
Keyword(s):  
Electric Vehicles ◽  
Environmental Performance ◽  
Optimization Method ◽  
Energy Costs ◽  
Multi Objective Optimization ◽  
Environmental Aspects ◽  
Multi Objective ◽  
Investment Cost ◽  
Detached House

The use of batteries of electric vehicles (EVs) for home electricity applications using a bidirectional charger, a process called vehicle-to-home (V2H), is attracting the attention of EV owners as a valuable additional benefit of EVs. To motivate owners to invest in V2H, a quantitative evaluation to compare the performance of EV batteries with that of residential stationary batteries (SBs) is required. In this study, we developed a multi-objective optimization method for the household of EV owners using energy costs including investment and CO2 emissions as indices and compared the performances of V2H and SB. As a case study, a typical detached house in Japan was assumed, and we evaluated the economic and environmental aspects of solar power self-consumption using V2H or SB. The results showed that non-commuting EV owners should invest in V2H if the investment cost of a bidirectional charger is one third of the current cost as compared with inexpensive SB, in 2030. In contrast, our results showed that there were no advantages for commuting EV owners. The results of this study contribute to the rational setting of investment costs to increase the use of V2H by EV owners.

scholarly journals Multi-Objective Optimization of Aircraft Taxiing on the Airport Surface with Consideration to Taxiing Conflicts and the Airport Environment

2019 ◽  
Vol 11 (23) ◽  
pp. 6728 ◽  
Author(s):  
Zhang ◽  
Huang ◽  
Liu ◽  
Li
Keyword(s):  
Fuel Consumption ◽  
High Efficiency ◽  
Optimization Method ◽  
Pollutant Emissions ◽  
Multi Objective Optimization ◽  
Optimization Scheme ◽  
Nsga Ii ◽  
Multi Objective ◽  
International Airport ◽  
And Control

High-efficiency taxiing for safe operations is needed by all types of aircraft in busy airports to reduce congestion and lessen fuel consumption and carbon emissions. This task is a challenge in the operation and control of the airport’s surface. Previous studies on the optimization of aircraft taxiing on airport surfaces have rarely integrated waiting constraints on the taxiway into the multi-objective optimization of taxiing time and fuel emissions. Such studies also rarely combine changes to the airport’s environment (such as airport elevation, field pressure, temperature, etc.) with the multi-objective optimization of aircraft surface taxiing. In this study, a multi-objective optimization method for aircraft taxiing on an airport surface based on the airport’s environment and traffic conflicts is proposed. This study aims to achieve a Pareto optimized taxiing scheme in terms of taxiing time, fuel consumption, and pollutant emissions. This research has the following contents: (1) Previous calculations of aircraft taxiing pathways on the airport’s surface have been based on unimpeded aircraft taxiing. Waiting on the taxiway is excluded from the multi-objective optimization of taxiing time and fuel emissions. In this study, the waiting points were selected, and the speed curve was optimized. A multi-objective optimization scheme under aircraft taxiing obstacles was thus established. (2) On this basis, the fuel flow of different aircraft engines was modified with consideration to the aforementioned environmental airport differences, and a multi-objective optimization scheme for aircraft taxiing under different operating environments was also established. (3) A multi-objective optimization of the taxiing time and fuel consumption of different aircraft types was realized by acquiring their parameters and fuel consumption indexes. A case study based on the Shanghai Pudong International Airport was also performed in the present study. The taxiway from the 35R runway to the 551# stand in the Shanghai Pudong International Airport was optimized by the non-dominant sorting genetic algorithm II (NSGA-II). The taxiing time, fuel consumption, and pollutant emissions at this airport were compared with those of the Kunming Changshui International Airport and Lhasa Gonggar International Airport, which have different airport environments. Our research conclusions will provide the operations and control departments of airports a reference to determine optimal taxiing schemes.

scholarly journals Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 220 ◽  
Author(s):  
Juan Chen ◽  
Yuxuan Yu ◽  
Qi Guo
Keyword(s):  
Model Predictive Control ◽  
Fuel Consumption ◽  
Predictive Control ◽  
Traffic Congestion ◽  
Control Method ◽  
Optimization Method ◽  
Speed Limit ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

This paper proposes a model predictive control method based on dynamic multi-objective optimization algorithms (MPC_CPDMO-NSGA-II) for reducing freeway congestion and relieving environment impact simultaneously. A new dynamic multi-objective optimization algorithm based on clustering and prediction with NSGA-II (CPDMO-NSGA-II) is proposed. The proposed CPDMO-NSGA-II algorithm is used to realize on-line optimization at each control step in model predictive control. The performance indicators considered in model predictive control consists of total time spent, total travel distance, total emissions and total fuel consumption. Then TOPSIS method is adopted to select an optimal solution from Pareto front obtained from MPC_CPDMO-NSGA-II algorithm and is applied to the VISSIM environment. The control strategies are variable speed limit (VSL) and ramp metering (RM). In order to verify the performance of the proposed algorithm, the proposed algorithm is tested under the simulation environment originated from a real freeway network in Shanghai with one on-ramp. The result is compared with fixed speed limit strategy and single optimization method respectively. Simulation results show that it can effectively alleviate traffic congestion, reduce emissions and fuel consumption, as compared with fixed speed limit strategy and classical model predictive control method based on single optimization method.

scholarly journals A Smart-Distributed Pareto Front Using the ev-MOGA Evolutionary Algorithm

2014 ◽  
Vol 23 (02) ◽  
pp. 1450002 ◽  
Author(s):  
J. M. Herrero ◽  
G. Reynoso-Meza ◽  
M. Martínez ◽  
X. Blasco ◽  
J. Sanchis
Keyword(s):  
Pareto Front ◽  
Optimization Methods ◽  
Optimization Method ◽  
Initial Condition ◽  
Multi Objective Optimization ◽  
Computational Burden ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Normalized Normal Constraint ◽  
Normal Constraint

Obtaining multi-objective optimization solutions with a small number of points smartly distributed along the Pareto front is a challenge. Optimization methods, such as the normalized normal constraint (NNC), propose the use of a filter to achieve a smart Pareto front distribution. The NCC optimization method presents several disadvantages related with the procedure itself, initial condition dependency, and computational burden. In this article, the epsilon-variable multi-objective genetic algorithm (ev-MOGA) is presented. This algorithm characterizes the Pareto front in a smart way and removes the disadvantages of the NNC method. Finally, examples of a three-bar truss design and controller tuning optimizations are presented for comparison purposes.

scholarly journals Problems of a Utopia Point Setting in Transformation of Individual Objective Functions in Multi-Objective Optimization

2019 ◽  
Vol 27 (45) ◽  
pp. 64-71
Author(s):  
Pavel Važan ◽  
Zuzana Červeňanská ◽  
Janette Kotianová ◽  
Jiří Holík
Keyword(s):  
Production Control ◽  
A Priori ◽  
Optimization Method ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
The Individual ◽  
Utopia Point

Abstract In an optimal processes control, where the considered goals are in general observed as concurrently conflicted, a multi-objective approach fits the best. Commonly used scalarization techniques in multi-objective optimization need a transformation of the individual single-objective functions involved into a scalar multi-criteria objective function. There are many parameters which can influence the optimization results solutions, including an unreachable utopia point value. In this study, the authors compare the multi-objective problem solutions found via two ways of the individual objectives transformation with the respect to setting the utopia point. The methods are used in the area of production control in a case study for a batch production system. To find the solutions, The Weighted Sum Method with a priori articulated preferences under specific constraints as the scalar multi-objective optimization method is applied in simulation optimization.

Multi-Objective Optimization of the Impeller Geometry for an Automotive Torque Converter Using RBF and NSGA-II Methods

2020 ◽  
Author(s):  
Jie Chen ◽  
Guangqiang Wu
Keyword(s):  
Great Influence ◽  
Optimization Method ◽  
Torque Converter ◽  
Numerical Characteristic ◽  
Multi Objective Optimization ◽  
Peak Efficiency ◽  
Nsga Ii ◽  
Characteristic Curves ◽  
Multi Objective ◽  
Impeller Geometry

Abstract Since impeller shape has great influence on hydraulic performance of a torque converter, a multi-objective optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) has been used to redesign the impeller geometry. Radial basis function (RBF) is attempted to establish the surrogate models for performance responses in impeller design. A sophisticated automotive torque converter case is exemplified, which demonstrates that RBF provides a better surrogate accuracy and NSGA-II is more effective than the other methods studied. To verify the optimization results, the complete numerical characteristic curves of the torque converter with the optimized impeller are compared to the validated numerical characteristic curves of the initial torque converter. The numerical results show that the stall torque ratio and peak efficiency are increased by 3.18% and 1.4%, respectively. The results indicate a reasonable improvement in the optimal design of torque converter impeller and a higher performance using the NSGA-II method.

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