基于混沌蚁群算法的连铸二冷参数多准则优化

Multi-criteria Optimization Based on Chaos Ant Colony Algorithm for Secondary Cooling Parameters in Continuous Casting

针对连铸二冷配水参数设置这一多准则优化问题,提出了基于混沌蚁群算法和二维凝固传热数学模型的二冷参数优化方法.采用控制容积法建立的凝固传热模型主要用于连铸过程仿真,研究铸流表面温度分布和参数搜索空间特性.混沌蚁群算法用于解决二冷参数优化问题,具有蚂蚁觅食过程的混沌和自组织特性,克服了一般蚁群算法收敛速度比较慢、容易出现停滞以及全局搜索能力较低的缺点,对解决多准则优化具有较好的收敛性和鲁棒性.应用此方法进行实际铸机二冷参数优化,结果表明可以明显改进铸坯内部质量.

A method based on CACO （chaos ant colony optimization ） algorithm and 2-D solidification and heat transfer model was presented to optimize the water distribution parameters in secondary cooling for continuous casting - a multi-criteria optimization problem. The solidification and heat transfer model introducing volume control method was mainly used to simulate the continuous casting process, so as to investigate the surface temperature distribution of strands and characters of parameters＇ search space, while the CACO algorithm was used to solve the optimization problem of water distribution parameters in secondary cooling. Characterized by the chaotic and self-organization behavior in ants＇ foraging process, the algorithm rises above such defects of conventional ant colony algorithm as slow convergence rate, easy to stagnate, and low ability in global search. CACO thus shows the superiority in both convergence and robustness when solving multi-criteria optimization problems. The proposed method in application for the optimization of secondary cooling parameters resulted in highly improved quality of CC billet.