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

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