%0 Journal Article %A SHI Yuqiang %A TIAN Yongzheng %A ZHANG Yuqi %A SHI Xiaoqiu %T Solving FJSP by Multi-population Genetic Algorithms with Complex Network Structure %D 2021 %R 10.3778/j.issn.1002-8331.2006-0313 %J Computer Engineering and Applications %P 257-266 %V 57 %N 2 %X

Multi-population is an effective method to overcome the premature convergence of Genetic Algorithm(GA). However, the traditional Multi-population Genetic Algorithms(MGAs) seldom consider the influence of the structure of sub-populations on the performance of MGAs and the number of sub-populations is limited. Therefore, in order to make up for the above shortcomings, the Multi-population Genetic Algorithms with Complex Network Structures(MGA-CNS) are proposed. Taking the Flexible Job shop Scheduling Problem(FJSP) as an example, the effects of network structure parameters, such as sub-population size, sub-population number, controllable parameter [(α)], controllable parameter [(β)] and initial network size on the performance of the MGA-CNS are studied. The simulation results indicate that:the larger the sub-population size, the better the performance of MGA-CNS; the sub-population number should not be too small or too large; the value of [α] should not be too large, which should not be greater than 0.3; the value of [β] should not be too large, which should not be greater than 0.8; and the initial network size should not be greater than 4. Finally, the MGA-CNS is used to solve more FJSP and compared with many other algorithms to verify its effectiveness.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2006-0313