Sensor management: coverage versus survivability - a multiple-objective optimization problem

2003 ◽  
Author(s):  
GeeWah Ng ◽  
Khin H. Ng ◽  
Ee Lan Chia
Keyword(s):  
Optimization Problem ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Sensor Management

ENHANCED INTERACTIVE SATISFYING OPTIMIZATION APPROACH TO MULTIPLE OBJECTIVE OPTIMIZATION WITH PREEMPTIVE PRIORITIES

2006 ◽  
Vol 05 (01) ◽  
pp. 47-63 ◽  
Author(s):  
CHAOFANG HU ◽  
SHAOYUAN LI
Keyword(s):  
Optimization Problem ◽  
Optimization Method ◽  
Previous Method ◽  
Optimization Models ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Optimization Approach ◽  
Fuzzy Relations ◽  
Objective Functions ◽  
Preemptive Priority

This paper proposes an enhanced interactive satisfying optimization method based on goal programming for the multiple objective optimization problem with preemptive priorities. Based on the previous method, the approach presented makes the higher priority achieve the higher satisfying degree. For three fuzzy relations of the objective functions, the corresponding optimization models are proposed. Not only can satisfying results for all the objectives be acquired, but the preemptive priority requirement can also be simultaneously actualized. The balance between optimization and priorities is realized. We demonstrate the power of this proposed method by illustrative examples.

TWO-PHASE INTERACTIVE SATISFYING METHOD OF FUZZY MULTIPLE OBJECTIVE OPTIMIZATION WITH LINGUISTIC PREFERENCE

2009 ◽  
Vol 08 (03) ◽  
pp. 427-443 ◽  
Author(s):  
CHAOFANG HU ◽  
SHAOYUAN LI
Keyword(s):  
Decision Maker ◽  
Optimization Problem ◽  
Optimization Method ◽  
Multiple Objectives ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Two Phase ◽  
Numerical Example ◽  
Complex Optimization ◽  
Important Objective

This paper presents a two-phase interactive satisfying optimization method for fuzzy multiple objectives optimization with linguistic preference. This proposed approach utilizes the view that the more important objective has the higher desirable satisfying degree. The originally complex optimization problem is simplified and divided into two parts that are solved one by one. The decision maker can acquire satisfying solution of all the objectives under linguistic preference. Numerical example shows the efficiency, flexibility, and sensitivity of the proposed method.

Multiple objective optimization for active sensor management

2005 ◽  
Author(s):  
Scott F. Page ◽  
Alexander N. Dolia ◽  
Chris J. Harris ◽  
Neil M. White
Keyword(s):  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Sensor Management ◽  
Active Sensor

A landscape lake flow pattern design approach based on automated CFD simulation and parallel multiple objective optimization

2016 ◽  
Vol 74 (5) ◽  
pp. 1155-1162
Author(s):  
Hao Guo ◽  
Yimei Tian ◽  
Hailiang Shen ◽  
Yi Wang ◽  
Mengxin Kang
Keyword(s):  
Flow Velocity ◽  
Flow Pattern ◽  
Optimization Problem ◽  
Design Approach ◽  
Water Volume ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Volume Percentage ◽  
Water Flow Velocity ◽  
Landscape Lake

A design approach for determining the optimal flow pattern in a landscape lake is proposed based on FLUENT simulation, multiple objective optimization, and parallel computing. This paper formulates the design into a multi-objective optimization problem, with lake circulation effects and operation cost as two objectives, and solves the optimization problem with non-dominated sorting genetic algorithm II. The lake flow pattern is modelled in FLUENT. The parallelization aims at multiple FLUENT instance runs, which is different from the FLUENT internal parallel solver. This approach: (1) proposes lake flow pattern metrics, i.e. weighted average water flow velocity, water volume percentage of low flow velocity, and variance of flow velocity, (2) defines user defined functions for boundary setting, objective and constraints calculation, and (3) parallels the execution of multiple FLUENT instances runs to significantly reduce the optimization wall-clock time. The proposed approach is demonstrated through a case study for Meijiang Lake in Tianjin, China.

scholarly journals System Dynamics-Multiple Objective Optimization Model for Water Resource Management: A Case Study in Jiaxing City, China

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 671
Author(s):  
Xiaoying Zhou ◽  
Feier Wang ◽  
Kuan Huang ◽  
Huichun Zhang ◽  
Jie Yu ◽  
...  
Keyword(s):  
Resource Management ◽  
System Dynamics ◽  
Water Resource ◽  
Water Allocation ◽  
Water Resource Management ◽  
Water Shortage ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Model Framework

Predicting and allocating water resources have become important tasks in water resource management. System dynamics and optimal planning models are widely applied to solve individual problems, but are seldom combined in studies. In this work, we developed a framework involving a system dynamics-multiple objective optimization (SD-MOO) model, which integrated the functions of simulation, policy control, and water allocation, and applied it to a case study of water management in Jiaxing, China to demonstrate the modeling. The predicted results of the case study showed that water shortage would not occur at a high-inflow level during 2018–2035 but would appear at mid- and low-inflow levels in 2025 and 2022, respectively. After we made dynamic adjustments to water use efficiency, economic growth, population growth, and water resource utilization, the predicted water shortage rates decreased by approximately 69–70% at the mid- and low-inflow levels in 2025 and 2035 compared to the scenarios without any adjustment strategies. Water allocation schemes obtained from the “prediction + dynamic regulation + optimization” framework were competitive in terms of social, economic and environmental benefits and flexibly satisfied the water demands. The case study demonstrated that the SD-MOO model framework could be an effective tool in achieving sustainable water resource management.

Multiple-objective optimization applied in extracting multiple-choice tests

2021 ◽  
Vol 105 ◽  
pp. 104439
Author(s):  
Tram Nguyen ◽  
Toan Bui ◽  
Hamido Fujita ◽  
Tzung-Pei Hong ◽  
Ho Dac Loc ◽  
...  
Keyword(s):  
Multiple Choice ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Multiple Choice Tests ◽  
Choice Tests

Optimization of the Robot Calibration Process

1992 ◽  
Author(s):  
G. Zak ◽  
R. G. Fenton ◽  
B. Benhabib
Keyword(s):  
Optimization Problem ◽  
Kinematic Model ◽  
Optimization Procedure ◽  
Calibration Procedure ◽  
Residual Error ◽  
Industrial Robots ◽  
Multiple Objective Optimization ◽  
Robot Calibration ◽  
Calibration Process ◽  
The Cost

Abstract Most industrial robots cannot be off-line programmed to carry out a task accurately, unless their kinematic model is suitably corrected through a calibration procedure. However, proper calibration is an expensive and time-consuming procedure due to the highly accurate measurement equipment required and due to the significant amount of data that must be collected. To improve the efficiency of robot calibration, an optimization procedure is proposed in this paper. The objective of minimizing the cost of the calibration is combined with the objective of minimizing the residual error after calibration in one multiple-objective optimization. Prediction of the residual error for a given calibration process presents the main difficulty for implementing the optimization. It is proposed that the residual error is expressed as a polynomial function. This function is obtained as a result of fitting a response surface to either experimental or simulated sample estimates of the residual error. The optimization problem is then solved by identifying a reduced set of possible solutions, thus greatly simplifying the decision maker’s choice of an effective calibration procedure. An application example of this method is also included.

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