An improved quantum-behaved PSO algorithm for a new process optimization problem based on mechanism model of LBE converter

A process optimization method based on LBE converter mechanism model is studied in this paper. According to the characteristic of converter steelmaking, the composition of molten steel plays an important role on quality of steelmaking production. The optimal setting of input raw materials is extremely complex, and the classical low efficiency heuristic model is usually adopted in actual applications. A process optimization model based on mechanism method of LBE converter is proposed in this paper by making the raw material cost and hitting rate of endpoint as objective at the same time, using the energy, mass conservation equations and physical-chemical balances of the steel-making process as constraints, which is also very crucial in the actual production. Those are different from other common research literatures. Evenly, the objective is highly non-linear, lots of constraints are satisfied at the same time, and there is also some complicated physicochemical reaction in the hot bath. It is significant and challenging to solve this sort of complex, multivariate and highly non-linear problem. An evolution quantum-inspired particle swarm optimization algorithm (EQPSO) is proposed to solve the process model in this paper. Instead of position and velocity in the original PSO algorithm, every individual particle is depicted by a wave function Ψ (x,t) and DE evolutionary strategy in EQPSO, which make the particles have quantum behavior. Meanwhile, the global and local convergence ability are improved simultaneously by the operation of differential evolution by introducing mbest, raising the convergence accuracy of EQPSO. The experimental results show the effectiveness of the model with the EQPSO.