Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction

被引:42
作者
Ganjehkaviri, A. [1 ]
Jaafar, M. N. Mohd [1 ]
Hosseini, S. E. [1 ]
Barzegaravval, H. [2 ]
机构
[1] Univ Teknol Malaysia, Fac Mech Engn, Skudai, JB, Malaysia
[2] EORDG, Tehran, Iran
关键词
Multi-objective optimization; Genetic algorithm; Energy system; Pareto convergence; ORGANIC RANKINE-CYCLE; MULTIOBJECTIVE OPTIMIZATION; THERMOECONOMIC OPTIMIZATION; THERMODYNAMIC ANALYSIS; POWER-PLANT; PERFORMANCE; REFRIGERATION; TECHNOLOGIES; EXERGY;
D O I
10.1016/j.energy.2016.12.034
中图分类号
O414.1 [热力学];
学科分类号
摘要
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. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:167 / 177
页数:11
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