Multi-Objective Optimization of Double-Folded Yagi Antenna Using Genetic Algorithms

2010 ◽  
Author(s):  
Bin Yuan ◽  
Xiu Wang
Keyword(s):  
Genetic Algorithms ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Yagi Antenna

How Genetic Algorithms Handle Pareto-Optimality in Design and Manufacturing

2007 ◽  
pp. 465-482
Author(s):  
N. Chakraborti
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Pareto Optimality ◽  
Multi Objective Optimization ◽  
Recent Past ◽  
Multi Objective ◽  
Manufacturing Sectors ◽  
Design And Manufacturing ◽  
Basic Concepts

An informal analysis is provided for the basic concepts associated with multi-objective optimization and the notion of Pareto-optimality, particularly in the context of genetic algorithms. A number of evolutionary algorithms developed for this purpose are also briefly introduced, and finally, a number of paradigm examples are presented from the materials and manufacturing sectors, where multi-objective genetic algorithms have been successfully utilized in the recent past.

scholarly journals Machine-Learning-Based Parallel Genetic Algorithms for Multi-Objective Optimization in Ultra-Reliable Low-Latency WSNs

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 4913-4926 ◽  
Author(s):  
Yuchao Chang ◽  
Xiaobing Yuan ◽  
Baoqing LI ◽  
Dusit Niyato ◽  
Naofal Al-Dhahir
Keyword(s):  
Machine Learning ◽  
Genetic Algorithms ◽  
Low Latency ◽  
Multi Objective Optimization ◽  
Parallel Genetic Algorithms ◽  
Multi Objective

Improved NSGAII with a New Distribution and its Application in Multi-Objective Reservoir Operation

2011 ◽  
Vol 90-93 ◽  
pp. 2734-2739
Author(s):  
Ruan Yun ◽  
Cui Song Yu
Keyword(s):  
Genetic Algorithms ◽  
Control Strategy ◽  
Reservoir Operation ◽  
Large Scale ◽  
Pareto Front ◽  
Optimization Problem ◽  
Shape Parameter ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
New Distribution

Non-dominated sorting genetic algorithms II (NSGAII) has been widely used for multi- objective optimizations. To overcome its premature shortcoming, an improved NSGAII with a new distribution was proposed in this paper. Comparative to NSGAII, improved NSGAII uses an elitist control strategy to protect its lateral diversity among current non-dominated fronts. To implement elitist control strategy, a new distribution (called dogmatic distribution) was proposed. For ordinary multi-objective optimization problem (MOP), an ordinary exploration ability of improved NSGAII should be maintained by using a larger shape parameter r; while for larger-scale complex MOP, a larger exploration ability of improved NSGAII should be maintained by using a less shape parameter r. The application of improved NSGAII in multi-objective operation of Wohu reservoir shows that improved NSGAII has advantages over NSGAII to get better Pareto front especially for large-scale complex multi-objective reservoir operation problems.

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