Improved multi-objective particle-swarm algorithm and its application to electric arc furnace in steelmaking process
改进多目标粒子群算法及其在电弧炉供电优化中的应用

We propose a chaos region changed multi-objective particle-swarm optimization algorithm(CRMOPSO) for optimizing the power supply for the electric arc furnace in a steelmaking process. All index functions with constraints are summed up with different weighting factors into a single performance function to be optimized. To deal with the inherent disadvantage of slower convergence and low accuracy of basic multi-objective particle -swarm algorithm, a variable-domain acceleration operator is introduced to expedite the convergence process the algorithm. Meanwhile, a chaotic operator is employed to prevent the algorithm from prematurity by enhancing the algorithm searching capability around local optimal solutions. A restricted competition selection(RCS) operator is used to guarantee the diversity of populations during the evolution process. After a new power supply model has been built, the CRMOPSO was applied to optimize the steelmaking process; it reduces the electric energy consumption, shorten the melting time and prolong the lifespan of the furnaces lining. The application results show the efficacy of the proposed algorithm.