基于免疫遗传算法的炼钢最优炉次计划研究

针对炼钢最优炉次计划问题难以准确求解的实际情况,建立了一种含有0-1变量的整数规划模型,为了求解该优化模型,提出了一种新的免疫遗传算法.该算法通过将免疫算法和遗传算法相结合,在传统遗传算法中加入免疫算子,并且引入了新的个体选择概率模型,有效防止了算法过早收敛的现象.针对该类优化问题的特性,设计了自适应的交叉率和变异率准则,动态调整交叉率和变异率,提高了该算法的精度.基于工厂的实际数据,进行了仿真实验,实验结果表明该免疫遗传算法比普通遗传算法有着更高的搜索精度,证明了该算法在实际炼钢最优炉次计划问题中的有效性和准确性.

Optimal Furnace Steelmaking Plans Based on Immune Genetic Algorithm

Optimal situation has been studied for steel-making furnaces times solving scheduling problems difficult to establish accurately the integer programming problem containing 0-1 variables. In order to solve the scheduling model, a new immune genetic algorithm has been proposed by immune algorithm and genetic algorithm combined with immune operator to join in the traditional genetic algorithm, and a new model of individual selection probabilities been introduced, and effective algorithm prevents premature convergence phenomenon. According to the characteristics of such scheduling problems, design guidelines crossover and mutation rate adaptive, dynamic adjustment of crossover and mutation rate, and improve the accuracy of the algorithm. Based on actual data factory simulation experimental results show that the immune genetic algorithm has a higher than normal genetic algorithm search accuracy. Prove the validity and accuracy of the algorithm in real scheduling problems.