Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction
Genetic algorithm (GA) is widely accepted in energy systems optimization especially multi objective method. In multi objective method, a set of solutions called Pareto front is obtained. Due to random nature of GA, finding a unique and reproducible result is not an easy task for multi objective prob...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
Elsevier Ltd
2017
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/76115/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006867319&doi=10.1016%2fj.energy.2016.12.034&partnerID=40&md5=59b9ce75c0619e5f6c41a0327fd785ec |
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| Summary: | Genetic algorithm (GA) is widely accepted in energy systems optimization especially multi objective method. In multi objective method, a set of solutions called Pareto front is obtained. Due to random nature of GA, finding a unique and reproducible result is not an easy task for multi objective problems. Here we discuss the solution uniqueness, accuracy, Pareto convergence, dimension reduction topics and provide quantitative methodologies for the mentioned parameters. Firstly, Pareto frontier goodness and solution accuracy is introduced. Then the convergence of Pareto front is discussed and the related methodology is developed. By comparing two different best points (optimum points) selection method, it is shown that multi objective methods can be reduced to single objective or lower dimensions in objective functions by using ratio method. Our results establish that our proposed method can indeed provide unique solution of satisfactory accuracy and convergence for a multi-objective optimization problem in energy systems. |
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