Genetic algorithm in finding Pareto frontier of optimizing data transfer versus job execution in grids

2012 ◽  
Vol 28 (6) ◽  
pp. 1715-1736 ◽  
Author(s):  
Javid Taheri ◽  
Albert Y. Zomaya ◽  
Samee U. Khan
Keyword(s):  
Genetic Algorithm ◽  
Data Transfer ◽  
Pareto Frontier

scholarly journals Designing optimal greenhouse gas observing networks that consider performance and cost

2014 ◽  
Vol 4 (2) ◽  
pp. 705-749
Author(s):  
D. D. Lucas ◽  
C. Yver Kwok ◽  
P. Cameron-Smith ◽  
H. Graven ◽  
D. Bergmann ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Atmospheric Transport ◽  
Pareto Frontier ◽  
Ghg Emissions ◽  
Emission Rates ◽  
Monitoring Networks ◽  
Transport Models ◽  
Hfc 134A ◽  
Scientific Accuracy ◽  
Better Than

Abstract. Emission rates of greenhouse gases (GHGs) entering into the atmosphere can be inferred using mathematical inverse approaches that combine observations from a network of stations with forward atmospheric transport models. Some locations for collecting observations are better than others for constraining GHG emissions through the inversion, but the best locations for the inversion may be inaccessible or limited by economic and other non-scientific factors. We present a method to design an optimal GHG observing network in the presence of multiple objectives that may be in conflict with each other. As a demonstration, we use our method to design a prototype network of six stations to monitor summertime emissions in California of the potent GHG 1,1,1,2-tetrafluoroethane (CH2FCF3, HFC-134a). We use a multiobjective genetic algorithm to evolve network configurations that seek to jointly maximize the scientific accuracy of the inferred HFC-134a emissions and minimize the associated costs of making the measurements. The genetic algorithm effectively determines a set of "optimal" observing networks for HFC-134a that satisfy both objectives (i.e., the Pareto frontier). The Pareto frontier is convex, and clearly shows the tradeoffs between performance and cost, and the diminishing returns in trading one for the other. Without difficulty, our method can be extended to design optimal networks to monitor two or more GHGs with different emissions patterns, or to incorporate other objectives and constraints that are important in the practical design of atmospheric monitoring networks.

Pareto frontier for job execution and data transfer time in hybrid clouds

2014 ◽  
Vol 37 ◽  
pp. 321-334 ◽  
Author(s):  
Javid Taheri ◽  
Albert Y. Zomaya ◽  
Howard Jay Siegel ◽  
Zahir Tari
Keyword(s):  
Data Transfer ◽  
Pareto Frontier ◽  
Transfer Time ◽  
Hybrid Clouds

scholarly journals Using a Goal-Switching Selection Operator in Multi-Objective Genetic Algorithm Optimization Problems

2013 ◽  
Author(s):  
Daniel Shaefer ◽  
Scott Ferguson
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Product Line ◽  
Algorithm Optimization ◽  
Nsga Ii ◽  
Solution Quality ◽  
Design Variables ◽  
Mp3 Player ◽  
Selection Operator

This paper demonstrates how solution quality for multiobjective optimization problems can be improved by altering the selection phase of a multiobjective genetic algorithm. Rather than the traditional roulette selection used in algorithms like NSGA-II, this paper adds a goal switching technique to the selection operator. Goal switching in this context represents the rotation of the selection operator among a problem’s various objective functions to increase search diversity. This rotation can be specified over a set period of generations, evaluations, CPU time, or other factors defined by the designer. This technique is tested using a set period of generations before switching occurs, with only one objective considered at a time. Two test cases are explored, the first as identified in the Congress on Evolutionary Computation (CEC) 2009 special session and the second a case study concerning the market-driven design of a MP3 player product line. These problems were chosen because the first test case’s Pareto frontier is continuous and concave while being relatively easy to find. The second Pareto frontier is more difficult to obtain and the problem’s design space is significantly more complex. Selection operators of roulette and roulette with goal switching were tested with 3 to 7 design variables for the CEC 09 problem, and 81 design variables for the MP3 player problem. Results show that goal switching improves the number of Pareto frontier points found and can also lead to improvements in hypervolume and/or mean time to convergence.

scholarly journals Design Optimization of Plate-Fin Heat Exchanger in a Gas Turbine and Supercritical Carbon Dioxide Combined Cycle with Thermal Oil Loop

2021 ◽  
Vol 12 (1) ◽  
pp. 42
Author(s):  
Yue Cao ◽  
Jun Zhan ◽  
Jianxin Zhou ◽  
Fengqi Si
Keyword(s):  
Heat Transfer ◽  
Carbon Dioxide ◽  
Genetic Algorithm ◽  
Heat Flux ◽  
Supercritical Carbon Dioxide ◽  
Pareto Frontier ◽  
Combined Cycle ◽  
Nsga Ii ◽  
Thermal Oil ◽  
Supercritical Carbon

This paper presents an investigation on the optimum design for a plate-fin heat exchanger (PFHE) of a gas and supercritical carbon dioxide combined cycle which uses thermal oil as intermediate heat-transfer fluid. This may promote the heat transfer from low heat-flux exhaust to a high heat-flux supercritical carbon dioxide stream. The number of fin layers, plate width and geometrical parameters of fins on both sides of PFHE are selected as variables to be optimized by a non-dominated sorting genetic algorithm-II (NSGA-II), which is a multi-objective genetic algorithm. For the confliction of heat transfer area and pressure drop on the exhaust side, which are the objective indexes, the result of NSGA-II is a Pareto frontier. The technique for order of preference by similarity to ideal solution (TOPSIS) approach is applied to choose the optimum solution from the Pareto frontier. Finally, further simulation is performed to analyze the effect of each parameter to objective indexes and confirm the rationality of optimization results.

Benchmarking the Performance of a Machine Learning Classifier Enabled Multiobjective Genetic Algorithm on Six Standard Test Functions

2017 ◽  
Author(s):  
Kayla Zeliff ◽  
Walter Bennette ◽  
Scott Ferguson
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Objective Function ◽  
Hybrid Approach ◽  
Pareto Frontier ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Multiobjective Genetic Algorithm ◽  
Learning Classifier ◽  
Function Calls

Previous work tested a multi-objective genetic algorithm that was integrated with a machine learning classifier to reduce the number of objective function calls. Four machine learning classifiers and a baseline “No Classifier” option were evaluated. Using a machine learning classifier to create a hybrid multiobjective genetic algorithm reduced objective function calls by 75–85% depending on the classifier used. This work expands the analysis of algorithm performance by considering six standard benchmark problems from the literature. The problems are designed to test the ability of the algorithm to identify the Pareto frontier and maintain population diversity. Results indicate a tradeoff between the objectives of Pareto frontier identification and solution diversity. The “No Classifier” baseline multiobjective genetic algorithm produces the frontier with the closest proximity to the true frontier while a classifier option provides the greatest diversity when the number of generations is fixed. However, there is a significant reduction in computational expense as the number of objective function calls required is significantly reduced, highlighting the advantage of this hybrid approach.

Multi-Objective Optimization Design of Tanker Ships via a Genetic Algorithm

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Marcelo Ramos Martins ◽  
Diego F. Sarzosa Burgos
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Ship Design ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Total Cost ◽  
Multi Objective ◽  
The Cost

The cost of a new ship design heavily depends on the principal dimensions of the ship; however, dimensions minimization often conflicts with the minimum oil outflow (in the event of an accidental spill). This study demonstrates one rational methodology for selecting the optimal dimensions and coefficients of form of tankers via the use of a genetic algorithm. Therein, a multi-objective optimization problem was formulated by using two objective attributes in the evaluation of each design, specifically, total cost and mean oil outflow. In addition, a procedure that can be used to balance the designs in terms of weight and useful space is proposed. A genetic algorithm was implemented to search for optimal design parameters and to identify the nondominated Pareto frontier. At the end of this study, three real ships are used as case studies.

scholarly journals Designing optimal greenhouse gas observing networks that consider performance and cost

2015 ◽  
Vol 4 (1) ◽  
pp. 121-137 ◽  
Author(s):  
D. D. Lucas ◽  
C. Yver Kwok ◽  
P. Cameron-Smith ◽  
H. Graven ◽  
D. Bergmann ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Atmospheric Transport ◽  
Pareto Frontier ◽  
Ghg Emissions ◽  
Emission Rates ◽  
Monitoring Networks ◽  
Transport Models ◽  
Hfc 134A ◽  
Scientific Accuracy ◽  
Better Than

Abstract. Emission rates of greenhouse gases (GHGs) entering into the atmosphere can be inferred using mathematical inverse approaches that combine observations from a network of stations with forward atmospheric transport models. Some locations for collecting observations are better than others for constraining GHG emissions through the inversion, but the best locations for the inversion may be inaccessible or limited by economic and other non-scientific factors. We present a method to design an optimal GHG observing network in the presence of multiple objectives that may be in conflict with each other. As a demonstration, we use our method to design a prototype network of six stations to monitor summertime emissions in California of the potent GHG 1,1,1,2-tetrafluoroethane (CH2FCF3, HFC-134a). We use a multiobjective genetic algorithm to evolve network configurations that seek to jointly maximize the scientific accuracy of the inferred HFC-134a emissions and minimize the associated costs of making the measurements. The genetic algorithm effectively determines a set of "optimal" observing networks for HFC-134a that satisfy both objectives (i.e., the Pareto frontier). The Pareto frontier is convex, and clearly shows the tradeoffs between performance and cost, and the diminishing returns in trading one for the other. Without difficulty, our method can be extended to design optimal networks to monitor two or more GHGs with different emissions patterns, or to incorporate other objectives and constraints that are important in the practical design of atmospheric monitoring networks.

scholarly journals Genetic Algorithm for Effective Optimization of Delay Performance in Wireless Sensor Networks

2018 ◽  
Vol 26 (10) ◽  
pp. 281-308
Author(s):  
Saif Khalid Musluh ◽  
Alaa Abid Muslam ◽  
Raid Abd Alreda Shekan
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Wireless Networks ◽  
Sensor Networks ◽  
Network Performance ◽  
Data Transfer ◽  
Relay Node ◽  
Network Capacity ◽  
Wireless Sensor ◽  
Delay Performance

Wireless sensor networks (WSNs) play an important role in many real-world applications like surveillance. Wireless networks are also used to have data transfer. In such cases, there are problems with  resourcece-constraintnednetworks. The problems include a delay in communication and reduction in Quality of Service (QoS). Topology control can solve this problem to some extent. However, the delay performance and QoS need to be improved further to support intended operations in wireless networks. When relay node concept is considered, it is possible to optimize performance in such networks. In this paper, we proposed a Genetic Algorithm (GA) based relay configuration for optimizing delay performance in WSN. Relay nodes compute optimal positions using the proposed algorithm so as to improve QoS and reduce delay as much as possible. We implemented the algorithm using NS2 simulations. The results revealed that the proposed approach is able to improve QoS, reduce delay besides improving network performance in terms of throughput, network capacity, and energy efficiency.

scholarly journals A multiobjective approach to cost effective long-term groundwater monitoring using an elitist nondominated sorted genetic algorithm with historical data

2001 ◽  
Vol 3 (2) ◽  
pp. 71-89 ◽  
Author(s):  
Patrick Reed ◽  
Barbara S. Minsker ◽  
David E. Goldberg
Keyword(s):  
Genetic Algorithm ◽  
Groundwater Monitoring ◽  
Historical Data ◽  
Pareto Frontier ◽  
Monitoring Network ◽  
Cost Effective ◽  
Local Concentration ◽  
Decision Space ◽  
Estimation Errors ◽  
Concentration Estimation

This study presents a methodology for quantifying the tradeoffs between sampling costs and local concentration estimation errors in an existing groundwater monitoring network. The method utilizes historical data at a single snapshot in time to identify potential spatial redundancies within a monitoring network. Spatially redundant points are defined to be monitoring locations that do not appreciably increase local estimation errors if they are not sampled. The study combines nonlinear spatial interpolation with the nondominated sorted genetic algorithm (NSGA) to identify the tradeoff curve (or Pareto frontier) between sampling costs and local concentration estimation errors. Guidelines are given for using theoretical relationships from the field of genetic and evolutionary computation for population sizing and niching to ensure that the NSGA is competently designed to navigate the problem's decision space. Additionally, both a selection pressure analysis and a niching-based elitist enhancement of the NSGA are presented, which were integral to the algorithm's efficiency in quantifying the Pareto frontier for costs and estimation errors. The elitist NSGA identified 34 of 36 members of the Pareto optimal set attained from enumerating the monitoring application's decision space; this represents a substantial improvement over the standard NSGA, which found at most 21 of 36 members.

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