A novel evolutionary algorithm for global numerical optimization with continuous variables

作者：Zhao Wenhong; Wang Wei; Wang Yuping^*

来源：Progress in Natural Science, 2008, 18(3): 345-351.

DOI：10.1016/j.pnsc.2007.11.008

• evolutionary algorithm
• decent scale function
• global numerical optimization
• global convergence

Evolutionary algorithms (EAs) are a class of general optimization algorithms which are applicable to functions that are multimodal, non-differentiable, or even discontinuous. In this paper, a novel evolutionary algorithm is proposed to solve global numerical optimization with continuous variables. In order to make the algorithm more robust, the initial population is generated by combining determinate factors with random ones, and a decent scale function is designed to tailor the crossover operator so that it can not only find the decent direction quickly but also keep scanning evenly in the whole feasible space. In addition, to improve the performance of the algorithm, a mutation operator which increases the convergence-rate and ensures the convergence of the proposed algorithm is designed. Then, the global convergence of the presented algorithm is proved in detail. Finally, the presented algorithm is executed to solve 24 benchmark problems, and the results show that the convergence-rate of the proposed algorithm is much faster than that of the compared algorithms.

• 出版日期2008-3-10
• 单位西安电子科技大学