基于进化逆转操作算子的遗传算法对步进式加热炉炉温优化

Reheating Furnace Temperature Optimization Based on the Inver-Over Operator Genetic Algorithm

针对传统遗传算法对炉温进行优化设定时易陷入局部极小值,较难快速稳定地找到最优炉温值的缺点,引入逆转算子对遗传算法进行改进,使算法的每一代都能从父代继承更多的基因,从而提高算法的局部搜索能力。改进后的算法可以跳出局部极小值,快速稳定地寻找到最优炉温值,进而对加热炉炉温进行优化设定。大量的Matlab仿真结果表明,该改进算法具备可行性与有效性。

According to the traditional genetic algorithm to optimize the temperature setting point easily trapped into local minima, it is difficult to quickly find the optimal stable temperature value, this paper introduces a reversal operator genetic algorithm, which can inherit more gene from their parents, improve the local search ability, make the algorithm jump out of local minimum values, find the optimal temperature value, and optimize the furnace temperature setting points. The Matlab simulation results show that the improved algorithm is feasible and effectiveness.