Adaptive dual model differential evolution algorithm based on cloud model
Zhongquan Hu, Zhou Zhen, Wang Hongbin · 2018
To solve the problem of premature convergence and search stagnation in traditional differential algorithm, this paper presents an adaptive dual model differential evolution algorithm based on cloud model (ADDEC). The variation model of ADDEC is produced by combining basic variation with cloud variation. And the adaptive operators are referenced respectively for scale factor and interactive probability during the operation of variation and crossover. The robustness and the convergence rate of the algorithm are enhanced. Moreover the relationship between global search and local search of the algorithm is improved. The diversity of the population is guaranteed at the same time. It is tested by typical high dimension benchmark function and compared with other algorithms, and the results show that the ADDEC has better precision, high convergence speed and makes it easy to jump out the local optimal solution.