A Scheduling Model with Multi-Objective Optimization for Computational Grids using NSGA-II

2010 ◽  
Vol 1 (2) ◽  
pp. 74-94 ◽  
Author(s):  
Zahid Raza ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Optimization Problems ◽  
Turnaround Time ◽  
Computational Grid ◽  
Computational Grids ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Scheduling Model ◽  
Comparison Of The Results ◽  
The Cost

Scheduling a job on the grid is an NP Hard problem, and hence a number of models on optimizing one or other characteristic parameters have been proposed in the literature. It is expected from a computational grid to complete the job quickly in most reliable grid environment owing to the number of participants in the grid and the scarcity of the resources available. Genetic algorithm is an effective tool in solving problems that requires sub-optimal solutions and finds uses in multi-objective optimization problems. This paper addresses a multi-objective optimization problem by introducing a scheduling model for a modular job on a computational grid with a dual objective, minimizing the turnaround time and maximizing the reliability of the job execution using NSGA – II, a GA variant. The cost of execution on a node is measured on the basis of the node characteristics, the job attributes and the network properties. Simulation study and a comparison of the results with other similar models reveal the effectiveness of the model.

A Scheduling Model with Multi-Objective Optimization for Computational Grids using NSGA-II

2012 ◽  
pp. 143-164
Author(s):  
Zahid Raza ◽  
Deo P. Vidyarthi
Keyword(s):  
Optimization Problems ◽  
Turnaround Time ◽  
Computational Grid ◽  
Computational Grids ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Scheduling Model ◽  
Comparison Of The Results ◽  
The Cost

Scheduling a job on the grid is an NP Hard problem, and hence a number of models on optimizing one or other characteristic parameters have been proposed in the literature. It is expected from a computational grid to complete the job quickly in most reliable grid environment owing to the number of participants in the grid and the scarcity of the resources available. Genetic algorithm is an effective tool in solving problems that requires sub-optimal solutions and finds uses in multi-objective optimization problems. This paper addresses a multi-objective optimization problem by introducing a scheduling model for a modular job on a computational grid with a dual objective, minimizing the turnaround time and maximizing the reliability of the job execution using NSGA – II, a GA variant. The cost of execution on a node is measured on the basis of the node characteristics, the job attributes and the network properties. Simulation study and a comparison of the results with other similar models reveal the effectiveness of the model.

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 An Effective Couple Method for Reliability-Based Multi-Objective Optimization of Truss Structures with Static and Dynamic Constraints

2019 ◽  
Vol 17 (06) ◽  
pp. 1950016 ◽  
Author(s):  
T. Vo-Duy ◽  
D. Duong-Gia ◽  
V. Ho-Huu ◽  
T. Nguyen-Thoi
Keyword(s):  
Cross Section ◽  
Optimization Problems ◽  
Computational Cost ◽  
Probabilistic Constraints ◽  
Truss Structures ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Dynamic Constraints ◽  
Multi Objective ◽  
Couple Method

This paper proposes an effective couple method for solving reliability-based multi-objective optimization problems of truss structures with static and dynamic constraints. The proposed coupling method integrates a single-loop deterministic method (SLDM) into the nondominated sorting genetic algorithm II (NSGA-II) algorithm to give the so-called SLDM-NSGA-II. Thanks to the advantage of SLDM, the probabilistic constraints are treated as approximating deterministic constraints. And therefore the reliability-based multi-objective optimization problems can be transformed into the deterministic multi-objective optimization problems of which the computational cost is reduced significantly. In these reliability-based multi-objective optimization problems, the conflicting objective functions are to minimize the weight and the displacements of the truss. The design variables are cross-section areas of the bars and contraints include static and dynamic constraints. For reliability analysis, the effect of uncertainty of parameters such as force, added mass in the nodes, material properties and cross-section areas of the bars are taken into account. The effectiveness and reliability of the proposed method are demonstrated through three benchmark-type truss structures including a 10-bar planar truss, a 72-bar spatial truss and a 200-bar planar truss. Moreover, the influence of parameters on the reliability-based Pareto optimal fronts is also carried out.

Optimization of Vehicle-Borne Radar Antenna Pedestal Based on Modified NSGA-II

2014 ◽  
Vol 945-949 ◽  
pp. 2241-2247
Author(s):  
De Gao Zhao ◽  
Qiang Li
Keyword(s):  
Pareto Front ◽  
Optimization Problems ◽  
Population Diversity ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Sorting Process ◽  
Definition Of ◽  
Radar Antenna ◽  
Modified Algorithm

This paper deals with application of Non-dominated Sorting Genetic Algorithm with elitism (NSGA-II) to solve multi-objective optimization problems of designing a vehicle-borne radar antenna pedestal. Five technical improvements are proposed due to the disadvantages of NSGA-II. They are as follow: (1) presenting a new method to calculate the fitness of individuals in population; (2) renewing the definition of crowding distance; (3) introducing a threshold for choosing elitist; (4) reducing some redundant sorting process; (5) developing a self-adaptive arithmetic cross and mutation probability. The modified algorithm can lead to better population diversity than the original NSGA-II. Simulation results prove rationality and validity of the modified NSGA-II. A uniformly distributed Pareto front can be obtained by using the modified NSGA-II. Finally, a multi-objective problem of designing a vehicle-borne radar antenna pedestal is settled with the modified algorithm.

scholarly journals Multi-objective optimization of retaining wall using genetic algorithm

2021 ◽  
Vol 8 (1-2) ◽  
pp. 58-65
Author(s):  
Filip Dodigović ◽  
Krešo Ivandić ◽  
Jasmin Jug ◽  
Krešimir Agnezović
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Retaining Wall ◽  
Silty Sand ◽  
Embedment Depth ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Definition Of ◽  
The Cost

The paper investigates the possibility of applying the genetic algorithm NSGA-II to optimize a reinforced concrete retaining wall embedded in saturated silty sand. Multi-objective constrained optimization was performed to minimize the cost, while maximizing the overdesign factors (ODF) against sliding, overturning, and soil bearing resistance. For a given change in ground elevation of 5.0 m, the width of the foundation and the embedment depth were optimized. Comparing the algorithm's performance in the cases of two-objective and three objective optimizations showed that the number of objectives significantly affects its convergence rate. It was also found that the verification of the wall against the sliding yields a lower ODF value than verifications against overturning and soil bearing capacity. Because of that, it is possible to exclude them from the definition of optimization problem. The application of the NSGA-II algorithm has been demonstrated to be an effective tool for determining the set of optimal retaining wall designs.

Nature Inspired Methods for Multi-Objective Optimization

2010 ◽  
pp. 95-108 ◽  
Author(s):  
Sanjoy Das ◽  
Bijaya K. Panigrahi ◽  
Shyam S. Pattnaik
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Artificial Immune Systems ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Swarm Optimization ◽  
Immune Systems ◽  
Multi Objective ◽  
Key Issues ◽  
Nature Inspired Algorithms

This chapter focuses on the concepts of dominance and Pareto-optimality. It then addresses key issues in applying three basic classes of nature inspired algorithms – evolutionary algorithms, particle swarm optimization, and artificial immune systems, to multi-objective optimization problems. As case studies, the most significant multi-objective algorithm from each class is described in detail. Two of these, NSGA-II and MOPSO, are widely used in engineering optimization, while the others show excellent performances. As hybrid algorithms are becoming increasingly popular in optimization, this chapter includes a brief discussion of hybridization within a multi-objective framework.

A Manufacturing Oriented Single Point Search Hyper-Heuristic Scheme for Multi-Objective Optimization

2017 ◽  
Author(s):  
Pei Cao ◽  
Zhaoyan Fan ◽  
Robert Gao ◽  
Jiong Tang
Keyword(s):  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Single Point ◽  
Optimization Techniques ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Practical Applications ◽  
Multi Objective ◽  
Point Search

Multi-objective optimization problems are frequently encountered in engineering analyses. Optimization techniques in practical applications are devised and evaluated mostly for specific problems, and thus may not be generally applicable when applications vary. In this study we formulate a probability matching based hyper-heuristic scheme, then propose four low-level heuristics which can work coherently with the single point search algorithm MOSA/R (Multi-Objective Simulated Annealing Algorithm based on Re-pick) towards multi-objective optimization problems of various properties, namely DTLZ and UF test instances. Making use of the domination amount, crowding distance and hypervolume calculations, the hyper-heuristic scheme could meet different optimization requirements. The approach developed (MOSA/R-HH) exhibits better and more robust performance compared to AMOSA, NSGA-II and MOEA/D as illustrated in the numerical tests. The outcome of this research may potentially benefit various design and manufacturing practices.

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