Unbiased Selection of Decision Variables for Optimization

2017 ◽  
pp. 253-258
Author(s):  
Mikael Nolin ◽  
Niklas Andersson ◽  
Bernt Nilsson ◽  
Mark Max-Hansen ◽  
Oleg Pajalic
Keyword(s):  
Decision Variables ◽  
Selection Of

Multi-Objective Optimization of Large Pipeline Networks Using Genetic Algorithm

2004 ◽  
Author(s):  
K. K. Botros ◽  
D. Sennhauser ◽  
K. J. Jungowski ◽  
G. Poissant ◽  
H. Golshan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Gas Pipeline ◽  
Multi Objective Optimization ◽  
Maximum Throughput ◽  
Multi Objective ◽  
Decision Variables ◽  
Pipeline Networks ◽  
Multiple Unit ◽  
Pareto Fronts ◽  
Selection Of

This paper presents application of Genetic Algorithm (GA) methodologies to multi-objective optimization of two complex gas pipeline networks to achieve specific operational objectives. The first network contains 10 compressor stations resulting in 20 decision variables and an optimization space of 6.3 × 1029 cases. The second system contains 25 compressor stations resulting in 54 decision variables and an optimization space of 1.85 × 1078 cases. Compressor stations generally included multiple unit sites, where the compressor characteristics of each unit is taken into account constraining the solution by the surge and stonewall limits, maximum and minimum speeds and maximum power available. A key challenge to the optimization of such large systems is the number of constraints and associated penalty functions, selection of the GA operators such as crossover, mutation, selection criteria and elitism, as well as the population size and number of generations. The paper discusses the approach taken to arrive at optimal values for these parameters for large gas pipeline networks. Examples for two-objective optimizations, referred to as Pareto fronts, include maximum throughput and minimum fuel, as well as, minimum linepack and maximum throughput in typical linepack/throughput/fuel envelopes.

scholarly journals Short-term choice behaviour in a mixed fishery: investigating métier selection in the Danish gillnet fishery

2012 ◽  
Vol 69 (1) ◽  
pp. 131-143 ◽  
Author(s):  
Bo Sølgaard Andersen ◽  
Clara Ulrich ◽  
Ole Ritzau Eigaard ◽  
Anne-Sofie Christensen
Keyword(s):  
Choice Model ◽  
Decision Function ◽  
Survey Questionnaire ◽  
Target Species ◽  
Choice Behaviour ◽  
Short Term ◽  
Decision Variables ◽  
Fishing Strategies ◽  
Fishing Area ◽  
Selection Of

Abstract Andersen, B. S., Ulrich, C., Eigaard, O. R., and Christensen, A-S. 2012. Short-term choice behaviour in a mixed fishery: investigating métier selection in the Danish gillnet fishery. – ICES Journal of Marine Science, 69: 131–143. The study presents a short-term effort allocation modelling approach based on a discrete choice random utility model combined with a survey questionnaire to examine the selection of métiers (a combination of fishing area and target species) in the Danish North Sea gillnet fishery. Key decision variables were identified from the survey questionnaire, and relevant proxies for the decision function were identified based on available landings and effort information. Additional variables from the survey questionnaire were further used to validate and verify the outcome of the choice model. Commercial fishers in a mixed fishery make use of a number of decision variables used previously in the literature, but also a number of decision parameters rarely explicitly accounted for, such as price, weather, and management regulation. The seasonal availability of individual target species and within-year changes in monthly catch ration were the main explanatory drivers, but gillnetters were also responsive to information on the whole fishery, fish prices, and distance travelled to fishing grounds. Heterogeneous responses were evident from geographic differences in home harbour, which underpins the need to understand alternative fishing strategies among individual gillnetters better.

scholarly journals A DSS for Operational Management of Wastewaters under Uncertain Conditions

10.29007/n3cc ◽  
2018 ◽  
Author(s):  
António Pereira ◽  
José Pinho ◽  
Rolando Faria ◽  
Jose Vieira
Keyword(s):  
Treatment Plant ◽  
Monitoring Network ◽  
System A ◽  
Scada System ◽  
Sewer Network ◽  
Decision Variables ◽  
Operational Management ◽  
First Results ◽  
Treatment Facilities ◽  
Selection Of

Wastewater treatment facilities of the Ave River basin (located in NW Portugal) are especially vulnerable to infiltration since they present considerable extensions of sewers installed in streams and rivers and collect wastewaters from longstanding sewer networks of five municipalities. The operational management of this complex system involves decision variables such as the selection of the treatment plant where collected wastewater will be treated, with implications for pumped volumes and consequent energy consumption. Aiming to reduce these inflows and increase the management performance of TRATAVE, the company responsible for operating the system, a monitoring network that includes the entire drainage network and treatment facilities operated by the company was designed and implemented. Several flow measurement devices were installed at strategic locations within the sewer network and integrated with a SCADA system responsible for its operation. All measured data was organized in databases. This monitoring platform will support the implementation of a decision support system (DSS) based on a hydrological model of the basin, a hydrodynamic model of the river network and the sewer network. The DSS is being implemented using the Delft-FEWS platform, integrating monitoring data and models. The DSS conceptual framework and the first results of the estimated infiltration volumes are presented.

scholarly journals An Approach for Optimal Coordination of Over-Current Relays in Microgrids with Distributed Generation

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1740 ◽  
Author(s):  
Sergio D. Saldarriaga-Zuluaga ◽  
Jesús M. López-Lezama ◽  
Nicolás Muñoz-Galeano
Keyword(s):  
Network Security ◽  
Distributed Generation ◽  
Single Type ◽  
Renewable Distributed Generation ◽  
High Penetration ◽  
Decision Variables ◽  
Optimal Coordination ◽  
New Challenges ◽  
Operational Modes ◽  
Selection Of

The ever increasing presence of renewable distributed generation (DG) in microgrids is imposing new challenges in protection coordination. The high penetration of renewable DG enables microgrids to operate under different topologies, giving rise to bidirectional power flows and in consequence, rendering traditional coordination approaches inappropriate to guarantee network security. This paper proposes an approach for the optimal coordination of directional over-current relays (OCRs) in microgrids that integrate renewable DG and feature several operational modes. As a main contribution, the characteristic curves of directional OCRs are considered to be decision variables, instead of fixing a single type of curve for all relays as considered in previous works. The proposed approach allows for the selection of several IEC and IEEE curves which combination results in the best protection coordination. Several tests were carried out on an IEC benchmark microgrid in order to show the applicability of the proposed approach. Furthermore, a comparison with other coordination approaches evidenced that the proposed approach is able to find lower operation times and, at the same time, guarantee the suitable operation of protections under different condition faults and operational modes.

An efficient methodology to generate optimal inputs for the preliminary design of centrifugal compressor impellers

2020 ◽  
Vol 234 (4) ◽  
pp. 353-366
Author(s):  
Lakhdar Bourabia ◽  
Smail Khalfallah ◽  
Mahfoudh Cerdoun ◽  
Taha Chettibi
Keyword(s):  
Input Parameter ◽  
Substantial Part ◽  
Preliminary Design ◽  
Design Code ◽  
Design Time ◽  
Decision Variables ◽  
Optimal Inputs ◽  
Input Parameters ◽  
Work Input ◽  
Selection Of

Preliminary design of centrifugal compressors is an emphasized step, which initiates the design process. Even with the use of quick analysis methods such as one-dimensional models, preliminary design still occupies a substantial part of the total design time. Among the factors that can lengthen this time or even cause the design failure is the inappropriate selection of the design input parameters. The present paper proposes a methodology to generate optimal inputs for the preliminary design, which reduces the design time and optimizes its overall performance. This is achieved, firstly, by performing an aerothermodynamic analysis that defines the appropriate input parameters, which will be used by a preliminary design code. This analysis has allowed identifying three pilot parameters (inlet relative Mach number, work input factor, and slip factor) to guide the generation of adequate input parameters. This input parameter generator, which reduces considerably the failure rate, is then exploited efficiently in an optimization process considering the pilot parameters as decision variables. Therefore, the proposed input parameter generator is coupled with a preliminary design code and an optimization algorithm. The proposed input parameter generator was validated on four existing compressors, showing a gain of more than 90% of the design time. Mainly, the proposed optimization has created a preliminary design trade-off having the target requirements and with optimized off-design performance.

scholarly journals Adaptive dropout for high-dimensional expensive multiobjective optimization

2021 ◽  
Author(s):  
Jianqing Lin ◽  
Cheng He ◽  
Ran Cheng
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
High Dimensional ◽  
Benchmark Problems ◽  
Decision Space ◽  
Decision Variables ◽  
Training Samples ◽  
Multiobjective Optimization Problems ◽  
Selection Of

AbstractVarious works have been proposed to solve expensive multiobjective optimization problems (EMOPs) using surrogate-assisted evolutionary algorithms (SAEAs) in recent decades. However, most existing methods focus on EMOPs with less than 30 decision variables, since a large number of training samples are required to build an accurate surrogate model for high-dimensional EMOPs, which is unrealistic for expensive multiobjective optimization. To address this issue, we propose an SAEA with an adaptive dropout mechanism. Specifically, this mechanism takes advantage of the statistical differences between different solution sets in the decision space to guide the selection of some crucial decision variables. A new infill criterion is then proposed to optimize the selected decision variables with the assistance of surrogate models. Moreover, the optimized decision variables are extended to new full-length solutions, and then the new candidate solutions are evaluated using expensive functions to update the archive. The proposed algorithm is tested on different benchmark problems with up to 200 decision variables compared to some state-of-the-art SAEAs. The experimental results have demonstrated the promising performance and computational efficiency of the proposed algorithm in high-dimensional expensive multiobjective optimization.

scholarly journals Performance analyses of existing sanitary sewers with the selection of household water-saving schemes

2020 ◽  
Vol 82 (11) ◽  
pp. 2400-2414
Author(s):  
Innocent Basupi
Keyword(s):  
Water Use ◽  
Rainwater Harvesting ◽  
Planning Horizon ◽  
Water Saving ◽  
Decision Variables ◽  
Water Demand And Supply ◽  
Household Water ◽  
Sanitary Sewers ◽  
The Cost ◽  
Selection Of

Abstract The widespread uptake of household water-saving systems (i.e. appliances, fittings, rainwater harvesting tanks, etc.) usually aims to reduce the gap between water demand and supply without considering the performances of downstream sanitary sewers (SSs). This paper presents an analysis approach that examines the lifespan interaction of water-saving schemes (WSSs) and operation of existing SSs. Examined are three probable ways of using (or not using) these water systems, including the conventional (baseline), full application and optimal selection of efficient WSSs. For optimality, a method that maximises the WSS potential efficiency (overall) and minimises the cost of WSSs including the associated savings across the entire existing SS subject to constraints at the end of the planning horizon has been formulated. The problem is solved using a non-dominated genetic algorithm to obtain optimal solutions. Decision variables include various water use (or saving) capacities of water-saving schemes at different inflow nodes (locations). The method was demonstrated on the subsystem of the Tsholofelo Extension SS. The results indicate impactful and revealing interactions between water use efficiency, instantaneous hydraulic performances and existing SS upgrade requirements due to different applications of WSSs. The impacts and revelations observed would inform decisions during lifespan operations and management of SSs.

Decision Variables Selection of Aluminum Electrolytic Process Based on FNN and RM

2013 ◽  
Vol 765-767 ◽  
pp. 469-472
Author(s):  
Li Zhong Yao ◽  
Tai Fu Li ◽  
De Yong Wu ◽  
Ying Ying Su ◽  
Jun Yi
Keyword(s):  
Variable Selection ◽  
Industrial Process ◽  
Selection Strategy ◽  
Significance Level ◽  
Electrolytic Process ◽  
Decision Variables ◽  
Energy Consumption Model ◽  
Variables Selection ◽  
False Nearest Neighbors ◽  
Selection Of

To establish an accurate model of aluminum electrolytic process, an novel variable selection strategy is proposed based on the false nearest neighbors (FNN) and randomization method (RM), which is abbreviated as FR. Firstly, the FNN is used to calculate the similarity measure of the respective variable; secondly, the RM is employed to test the significance level of each variable in turn; lastly, technical energy consumption model is established to verify the proposed method. The experimental results show that the method selects the best decision-making variables. Therefore, it provides a new method for the variable selection for complex industrial process.

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