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TSMC
2010

Multiobjective Optimization of Temporal Processes

12 years 10 months ago
Multiobjective Optimization of Temporal Processes
Abstract--This paper presents a dynamic predictiveoptimization framework of a nonlinear temporal process. Datamining (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.
Zhe Song, Andrew Kusiak
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TSMC
Authors Zhe Song, Andrew Kusiak
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