基于遗传算法的热电联产工程厂址位置优化方法研究

为进一步优化工程设计,降低工程造价,节约社会资源,本文在假定厂址各项外部条件均已落实的前提下,通过建立非线性的数学函数即宏观费用控制模型,动态量化热电厂位置与可变项目费用之间的关系,并运用遗传算法对该函数模型进行优化计算,求解其最优值,即厂址最优位置.经实例论证,多厂址位置方案比较后,运用遗传算法求解的厂址位置方案最高可节省投资千余万元.结果表明:遗传算法是一种有效的、准确的厂址位置优化方法,既有效地解决了重心法的计算精度低,计算结果适用性差的问题,同时也避免了方案比较法的主观判定,缺乏数据支撑的缺点.其优化结果客观性高,可靠性强,值得在今后的工程中推广应用.

Research on thermoelectric cogeneration project siting optimization method based on genetic algorithm

For further design optimization,reducing engineering cost and saving social resources,this article assumes that external conditions have been committed,establishes nonlinear mathematics functions based on macro costs control model,relationship between dynamic quantitative thermal power plant location and variable project costs,and solutes its optimal value of site optimal location by means of optimization calculation of genetic algorithm.After instance verify and multiple comparisons,site location scheme saves invests up to 10 million yuan(RMB) using genetic algorithm.The results show that the genetic algorithm is an efficient,accurate siting optimization methods;it effectively avoids low calculation accuracy and bad applicability of the method of center of gravity,and also avoids disadvantages of subjective judgment and lack of data support.Optimization result is objective and reliable and worth application and dissemination in the future.