Year: 2025
Type: conference-paper
Abstract: To address the drawbacks of poor search ability and proneness to premature convergence when genetic algorithms are used to solve the Job Shop Scheduling Problem, this paper proposes a novel improved parallel genetic algorithm. First, individuals in the population are classified based on their fitness, and different genetic operators are applied to different categories. Second, a new crossover method is designed to preserve the characteristics of high-quality individuals while enhancing population diversity. Finally, an efficient greedy mutation method is introduced to improve the ability to escape local optima. The proposed algorithm is tested on 11 standard benchmark instances and compared with other intelligent algorithms. Experimental results demonstrate that the proposed algorithm exhibits superior optimization performance.
Authors: Qifu Du, Shijiao Li, Ming Zhai, Wei Peng, Mingfei He, Lixin Zhang, Juin J. Liou, Yifan Hu, Yangjie Cao, Mingfu Zhu, Ronghan Wei
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4413601792