一种基于Alopex的参数自适应进化算法及其在软测量建模中的应用

提出了一种基于Alopex的参数自适应进化算法(SaAEA)。SaAEA算法将进化分为两个层面,即种群个体利用AEA算法进化,算法参数利用粒子群算法进化,实现参数的自适应调整。并将差分算法中使用的交叉操作引入到AEA算法以改善种群多样性。SaAEA算法在14个典型测试函数上进行了测试,测试结果表明,与基本的AEA算法相比,SaAEA算法寻优性能有了较大的提高,获得的解的质量和收敛速度均有明显提高。最后,将SaAEA算法应用于乙烯裂解深度神经网络软测量建模,得到的模型有较好的泛化能力。

A self-adaptive Alopex-based evolutionary algorithm and its application to soft sensor modeling

A self-adaptive evolutionary algorithm called SaAEA algorithm was proposed. SaAEA algorithm evolved between two levels, that is, the individuals evolved by AEA (Alopex-based evolutionary algorithm) algorithm and the parameters evolved by PSO (particle swarm optimization) algorithm, and eventually made the algorithm parameters achieve self-adaptive adjustment with the population evolution. At the same time, the crossover strategy used in differential evolution algorithm was introduced in AEA algorithm, to alleviate the problem of premature convergence and achieve population diversity, and overcome the disadvantage of falling into local optimum in AEA algorithm. The SaAEA algorithm was tested on 14 benchmark functions, and simulation results demonstrated that the performance of the improved algorithm was greatly improved comparing with the basic AEA algorithm. The new algorithm maintained the population diversity effectively. The solution quality and convergence rate were significantly improved. Finally, the new algorithm was used for the neural network soft sensor modeling for ethylene cracking severity, and a good result was achieved.