大域的探索手法を用いた反応系溶質輸送解析

抄録

Two global optimization methods based on Genetic Algorithms (GA) and Shuffled Complex Evolution (SCE-UA) method were examined to evaluate reaction terms in a solute transport model involving cation exchange reactions. Although the both algorithms can minimize the objective function after succession of generations, SCE-UA method appears superior to GA in that its performance depends less on the selection of the parameter set. In this paper, we proposed the following improvements for the efficient use of the algorithms: 1) the adaptability of the population that deduce impractical parameters was set to 0, and 2) In both methods, global optimum sought was repeated four times for each step, with narrowed search range. These new methods may also be useful for other optimization problems.