一种双种群协同进化算法在湿法炼锌过程中的应用

为了求解针铁矿法沉铁过程的多目标协调优化模型,从提高全局寻优能力和解的精度出发,提出一种基于改进全局搜索量子进化算法和局部搜索差分进化算法的双种群协同进化算法.数值仿真验证了该进化算法具有较好的收敛性和求解精度;典型工况的仿真优化结果表明了该多目标协调优化模型指导实际生产的可行性,以及所提出算法的有效性.

A double population co-evolution algorithm for process of zinc hydrometallurgy

In order to solve the multi-objective coordination optimization model of iron precipitation by the goethite
process, and improve the ability of global optimization and the accuracy of the reconciliation, a double population coevolution
algorithm is proposed, which is composed of improved global searching quantum evolution algorithm based on the
sharing function and partial searching difference evolution algorithm based on the individual similarity. Through numerical
simulations, it is proved that the evolutionary algorithm has better convergence and solution accuracy. The simulation results
of typical operating condition show that the multi-objective coordinated optimization model can guide the actual production
feasibly, and the proposed algorithm can solve the model effectively.