Abstract: The cuckoo search (CS) algorithm has a few disadvantages in the global searching, including slow convergence and high possibility of being trapped in local optimum. In overcome these disadvantages, a effective hybrid CS algorithm based on Rosenbrock local search and Cauchy mutation is proposed to solve constrained numerical and chemical engineering optimization problems. Firstly, good point set method was used to initiate bird nests position, which strengthened the diversity of global searching. Secondly, for the current best position, Rosenbrock local search technique is introduced to improve the convergence speed of CSA. Thirdly, a Gaussian mutation operator would be given on the global optimum of each generation, thus, the algorithm could effectively jump out of local minima. Experimental results are examined with several constrained numerical functions and chemical engineering optimization problems and the results show a promising performance of the proposed algorithm.

Key words: chemical engineering optimization, cuckoo search algorithm, Rosenbrock local search, good point set method, Cauchy mutation

摘要： 针对布谷鸟搜索算法存在收敛速度慢和易陷入局部最优等缺陷，提出一种基于Rosenbrock搜索和柯西变异的混合布谷鸟搜索算法用于求解约束化工优化问题。该算法首先采用佳点集方法对鸟窝位置进行初始化，为全局搜索的多样性奠定基础；然后利用Rosenbrock搜索算法对当前最优位置进行局部搜索，以提高算法的收敛速度；最后对当前最优解进行柯西变异以避免算法陷入局部最优。两个约束化工优化问题的实验结果表明了该混合算法的有效性。

关键词: 化工优化, 布谷鸟搜索算法, Rosenbrock局部搜索, 佳点集方法, 柯西变异