Optimization Method based on Genetic Algorithms: Difference between revisions
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The design of electromagnetic systems using methods of optimization have been carried out with deterministic methods. However, these methods are not efficient, because the object functions obtained from electromagnetic optimization problems are often highly non-linear, stiff, multiextreme and non-differential. The lack of a single method available to deal with multidimensional problems, including those with several goals to optimize, has generated the need to use numerical processes for optimization. This paper presents a method of global optimization based on genetic algorithms. The Genetic Algorithms are a versatile tool, which can be applied as a global optimization method to problems of electromagnetic engineering, because they are easy to implement to non-differentiable functions and discrete search spaces. It is also shown how, in some cases, genetic algorithms have been applied with success in electromagnetic problems, such as antenna design, far-field prediction, absorber coatings design, etc.[[Category:Scientific Paper]] | The design of electromagnetic systems using methods of optimization have been carried out with deterministic methods. However, these methods are not efficient, because the object functions obtained from electromagnetic optimization problems are often highly non-linear, stiff, multiextreme and non-differential. The lack of a single method available to deal with multidimensional problems, including those with several goals to optimize, has generated the need to use numerical processes for optimization. This paper presents a method of global optimization based on genetic algorithms. The Genetic Algorithms are a versatile tool, which can be applied as a global optimization method to problems of electromagnetic engineering, because they are easy to implement to non-differentiable functions and discrete search spaces. It is also shown how, in some cases, genetic algorithms have been applied with success in electromagnetic problems, such as antenna design, far-field prediction, absorber coatings design, etc. | ||
[[Category:Scientific Paper|optimization method based genetic algorithms]] | |||
Latest revision as of 12:51, 1 January 2017
| Scientific Paper | |
|---|---|
| Title | Optimization Method based on Genetic Algorithms |
| Read in full | Link to paper |
| Author(s) | Jose Luis Lopez-Bonilla |
| Keywords | Electromagnetic Optimization, Genetic Algorithm. |
| Published | 2005 |
| Journal | Apeiron |
| Volume | 12 |
| Number | 4 |
| No. of pages | 16 |
Read the full paper here
Abstract
The design of electromagnetic systems using methods of optimization have been carried out with deterministic methods. However, these methods are not efficient, because the object functions obtained from electromagnetic optimization problems are often highly non-linear, stiff, multiextreme and non-differential. The lack of a single method available to deal with multidimensional problems, including those with several goals to optimize, has generated the need to use numerical processes for optimization. This paper presents a method of global optimization based on genetic algorithms. The Genetic Algorithms are a versatile tool, which can be applied as a global optimization method to problems of electromagnetic engineering, because they are easy to implement to non-differentiable functions and discrete search spaces. It is also shown how, in some cases, genetic algorithms have been applied with success in electromagnetic problems, such as antenna design, far-field prediction, absorber coatings design, etc.