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

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.