Modelling and Optimisation of Reheat Furnace

Some problems are known to have computationally demanding objective function, which could turn to be infeasible when large problems are considered. Therefore, fast approximations to the objective function are required. This paper employs portfolio of intelligent systems algorithms for optimising a metal reheat furnace scheduling problem. The proposed system has been evaluated for different techniques of the reheat furnace scheduling problem. Different optimisation methods have been used, namely: particle swarm optimisation (PSO), genetic algorithm (GA) with different classic and advanced versions: GA with chromosome differentiation (GACD), age GA (AGA), and sexual GA (SGA), and finally a mimetic GA (MGA), which is based on combining the GA as a global optimiser and the PSO as a local optimiser. Simulations have been performed to evaluate the systempsilas performance.

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