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焦炉正常推焦过程中存在多个控制参数。通常人工经验法根据参数取值范围选取参数,该方法存在很大的随机性而且难以权衡多个性能指标使得正常推焦过程整体最优。为此,提出一种改进的多目标差分进化算法(IMODEA)。首先,采用了2个自适应性参数缩放比例因子和交叉概率完成变异和交叉操作。其次,提出一种改进的快速非支配排序策略(IFNSS)并利用IFNSS和拥挤度计算共同解决新一代种群选择问题。通过算法性能测试将IMODEA与NSGA-II作了对比,显示了IMODEA在Pareto最优解集空间分布性和收敛性方面更占优势。将IMODEA应用到焦炉正常推焦过程优化模型中,通过实验分析证明了算法的可行性。
Coke oven normal push the process of existence of multiple control parameters. Generally, the artificial experience method selects the parameters according to the range of the parameter values. The method has a great randomness and it is difficult to weigh a plurality of performance indexes to make the normal whole poking process optimal. To this end, an improved multi-objective differential evolution algorithm (IMODEA) is proposed. First, two adaptive scaling factors and crossover probabilities are used to perform mutation and crossover operations. Secondly, we propose an improved fast non-dominated ordering strategy (IFNSS) and use IFNSS and congestion computing together to solve the next generation population selection problem. Comparing IMODEA with NSGA-II through algorithmic performance tests shows that IMODEA is more dominant in the spatial distribution and convergence of the Pareto optimal solution set. The IMODEA is applied to the optimization model of coke oven normal pushing process, and the feasibility of the algorithm is proved through experimental analysis.