Hybrid particle swarm optimization of temperatures of billet reheating process

This paper presents a hybrid particle swarm optimization (HPSO) approach to solving the problem of optimizing the steady-state temperatures of the three zones of a billet reheating furnace. First, a model for predicting billet temperature is constructed based on a heat transfer equation and its boundary conditions. Then, HPSO is used to determine the optimal steady-state temperatures, which are reference inputs for the control of furnace temperature. This approach uses a chaos mechanism to create the initial population, and immunity and cloning to improve the global search capability and search precision of particle swarm optimization.

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