化工动态优化问题的混合海鸥优化算法研究

Research on hybrid seagull optimization algorithm for chemical dynamic optimization problem

化工动态优化问题的求解具有重要的理论与现实意义,但是求其最优解具有一定的难度,因此,提出了一种混合海鸥优化算法(HSOA)用于求此问题。首先在海鸥群体产生攻击行为的过程中引入认知部分,避免算法陷入局部最优;其次,引入自然选择的机理,利用适应度值对群体进行排序,用最好的一半个体代替最差的一半个体,同时保留群体的历史最优值,从而找出最优解;最后将改进的算法应用到3个经典的化工问题中,通过实验仿真以及不同的化工方法进行对比和分析,实验结果表明:HSOA算法在一定程度上优于文献解,进一步证明了HSOA算法具有较好的寻优能力。

The solution of chemical dynamic optimization problems has important theoretical and practical significance,but it is difficult to find the optimal solution.Therefore,a hybrid seagull optimization algorithm(HSOA)was proposed to solve this problem.First,the cognitive part was introduced in the process of the seagull group′s aggressive behavior to avoid the algorithm falling into the local optimum.Second,the mechanism of natural selection was introduced by using the fitness value to rank the group and replacing the worst half with the best half,while retaining the historical optimal value of the group,so as to find the optimal solution.Finally,the improved algorithm was applied in three classic chemical engineering problems,and the comparison and analysis were carried out through experimental simulation and different chemical engineering methods.The results show that the HSOA algorithm is better than the literature solution to a certain extent,which further proves that it has better optimization ability.