基于多目标连续蚁群算法的热精轧过程负荷分配策略研究  

Research on Hot Finishing Mills Load Distribution Strategies Based on Multi-objective Continuous Ant Colony Optimization Algorithm

作　　者：金阳 Jin Yang(ACRE Coking&Refractory Engineering Consulting Corporation(Dalian),MCC,Dalian Liaoning 116000)

机构地区：[1]中冶焦耐(大连)工程技术有限公司,辽宁大连116000

出　　处：《中国仪器仪表》2022年第12期67-72,共6页China Instrumentation

摘　　要：本文通过对蚁群算法的进一步研究,提出了多目标连续蚁群算法(MOCACO)。算法中,蚂蚁能够在连续空间内爬行,蚂蚁分泌的信息素只留存于当前位置并且通过蚂蚁移动前后的Pareto支配关系对信息素进行更新,每次优化计算后可得到一组非支配最优解。在此基础上,提出了基于MOCACO算法的热精轧负荷分配优化策略,并通过仿真实验证明了MOCACO算法在解决热精轧负荷分配问题上的有效性与优越性。One Multi-objective Continuous Ant Colony Optimization Algorithm is presented.In the MOCACO Algorithm,ants crawl in the continuous space,and the ant pheromones are only retained in the current position and pheromones are updated according to Pareto dominance relationship.A set of non-dominated solutions can be obtained after once optimization.The MOCACO Algorithm is applied in solving the problem of hot finishing mills load distribution and its effectiveness is proved by simulation.

关 键 词：热精轧 负荷分配优化 群智能 蚁群算法 多目标优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TG335[自动化与计算机技术—控制科学与工程]