一种基于偏好的多目标遗传算法在动态模型参数辨识中的应用

针对多目标优化问题，提出了PNSGA算法(preference-based non-dominated sorting genetic algorithm)，是一种NSGA Ⅱ的改进算法，结合Pareto支配和偏好信息定义了新的优于关系；把偏好信息加入快速非支配排序中，引导搜索方向，更方便决策者选择；并进一步分析了加入偏好对拥挤度机制的影响.实验证明该算法能较好地解决动态模型参数辨识的问题，有利于决策者做出决策.

A preference-based non-dominated sorting genetic algorithm for dynamic model parameters identification

A preference-based non-dominated sorting genetic algorithm (PNSGA) was proposed to solve the multi-objective optimization problems. It is an improved method of NSGA Ⅱ(non-dominated sorting genetic algorithm Ⅱ）. A new preference relationship was defined based on Pareto dominance and goal vectors. The goal vectors were designed according to the decision-maker’s preference. This algorithm combined the preference relationship with the fast non-dominated sorting, an important technique in NSGA Ⅱ. The advantage of the algorithm over NSGA Ⅱ in terms of crowding mechanism was analyzed. In the experiments, dynamic model parameters identification problem of the methanol-to-hydrocarbons process was changed into minimum optimization problem by fitting the sampling data. The result demonstrated the effectiveness of the algorithm for dynamic model parameters identification, compared with the conventional methods.