Multiobjective Optimization of Temporal Processes

作者：Song Zhe^*; Kusiak Andrew

来源：IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics , 2010, 40(3): 845-856.

DOI：10.1109/TSMCB.2009.2030667

• Data mining (DM)
• dynamic modeling
• evolutionary algorithms (EAs)
• multiobjective optimization
• nonlinear temporal process
• power plant
• predictive control
• preference-based optimization

This paper presents a dynamic predictive-optimization framework of a nonlinear temporal process. Data-mining (DM) and evolutionary strategy algorithms are integrated in the framework for solving the optimization model. DM algorithms learn dynamic equations from the process data. An evolutionary strategy algorithm is then applied to solve the optimization problem guided by the knowledge extracted by the DM algorithm. The concept presented in this paper is illustrated with the data from a power plant, where the goal is to maximize the boiler efficiency and minimize the limestone consumption. This multiobjective optimization problem can be either transformed into a single-objective optimization problem through preference aggregation approaches or into a Pareto-optimal optimization problem. The computational results have shown the effectiveness of the proposed optimization framework.

• 出版日期2010-6