• 工程与应用 •

### 运用含复杂网络结构的多种群遗传算法求解FJSP

1. 西南科技大学 制造科学与工程学院，四川 绵阳 621000
• 出版日期:2021-01-15 发布日期:2021-01-14

### Solving FJSP by Multi-population Genetic Algorithms with Complex Network Structure

SHI Yuqiang, TIAN Yongzheng, ZHANG Yuqi, SHI Xiaoqiu

1. School of Manufacturing Science and Engineering, Southwest University of Science and Technology, Mianyang, Sichuan 621000, China
• Online:2021-01-15 Published:2021-01-14

Abstract:

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.