Algorithm Based on Improved Genetic Algorithm for Job Shop Scheduling Problem

被引：0

作者：

Chen, Xiaohan ^{[1
]}

Zhang, Beike ^{[1
]}

Gao, Dong ^{[1
]}

机构：

[1] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100020, Chaoyang, Peoples R China

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (ICMA) | 2019年

关键词：

Genetic Algorithm; Job Shop Scheduling Problem; Niche; PARTICLE SWARM OPTIMIZATION; SEARCH;

D O I：

10.1109/icma.2019.8816334

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Job Shop Scheduling Problem is a kind of typical optimization management problem. The majorities of this kind of problem is NP-hard problem, traditional genetic algorithm tends to fail into local optimal solution and the algorithm converges quickly. This paper proposed Niche Adaptive Genetic Algorithm, which uses niche technology to enhance the optimization ability of algorithm, uses adaptive mechanism to accelerate the convergence speed of the algorithm. Compared with genetic algorithm and niche genetic algorithm, the test results show that Niche Adaptive Genetic Algorithm can find a better solution and has a stronger robustness.

引用

页码：951 / 956

页数：6

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