提出一种控制参数协进化的差分进化算法(DE-CPCE),实现算法控制参数随种群搜优进展,自适应动态调整。DE-CPCE算法将控制参数作为原始个体的共生个体,且每一个原始个体都有各自的共生个体;算法在对原优化问题进行差分进化搜优的同时,以原始个体进化效率作为共生个体(即控制参数)的评价,并通过共生个体的差分进化操作实现其协进化。DE-CPCE算法能随优化问题搜优进展,自适应动态调整算法控制参数,实时为算法搜优提供最优的控制参数。仿真研究表明,DE-CPCE算法的控制参数具有动态自适应性;并且在与文中所提及的算法(DE/rand/1,DE/best/1,DE/rand-to-best/1,DE/rand/2,DE/best/2,self-adaptive Pareto DE and self-adaptive DE)比较中,该算法能以较高概率求得全局最优值,且收敛速率快,求得最优解的精度高。同时,应用DE-CPCE算法估计SO2催化氧化反应动力学模型参数,结果优于文献报道。
To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.
