改进多目标粒子群算法及其在电弧炉供电优化中的应用  

Improved multi-objective particle-swarm algorithm and its application to electric arc furnace in steelmaking process

作　　者：冯琳[1] 毛志忠[1,2] 袁平[1,2] 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110819 [2]东北大学流程工业综合自动化教育部重点实验室,辽宁沈阳110819

出　　处：《控制理论与应用》2011年第10期1455-1460,共6页Control Theory & Applications

基　　金：国家高新技术研究发展计划资助项目(2007AA04Z194)

摘　　要：针对炼钢过程的供电优化问题,提出了一种改进的多目标粒子群算法(CRMOPSO).文中利用约束条件满意度函数并加权求和的策略将约束条件转化为一个待优化目标;同时为了克服基本多目标粒子群算法在求解复杂优化顺题时、搜索速度较慢,精度较低的缺点,引入变区域加速算子以提高算法收敛速度和精度;针对算法易于早熟收敛的问题,引入混沌算子以提高算法局部搜索能力;进化过程中采用受约束的竞争选择机制(RCS)小生境技术保证种群多样性.建立了新的供电多目标优化模型并将CRMOPSO算法用于该模型优化电弧炉供电过程,达到了减少电量消耗,缩短冶炼时间,延长炉衬使用寿命的目的,表明了该算法的有效性.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.

关 键 词：粒子群算法 多目标优化 供电曲线优化 变区域加速算子 混沌算子 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TM924.4[自动化与计算机技术—控制科学与工程]