An Improved Adaptive Genetic Algorithm and Its Application in Intelligent Course Scheduling System

Wang, Peiping; Xu, Xiaoping; Liu, Chuhong

doi:10.1109/icisce48695.2019.00034

Cited by 4 publications

(1 citation statement)

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“…Genetic Algorithms (GA) and improved GA [13][14][15] assess course satisfaction with penalty function by genetically coding course scheduling constraints. Linear programming [16], simulated annealing (SA) algorithm [17], taboo search (TS) algorithm [18], etc., these algorithms are very popular now.…”

Section: Introductionmentioning

confidence: 99%

Design of College Scheduling Algorithm Based on Improved Genetic Ant Colony Hybrid Optimization

Xie

Zhang

2022

Security and Communication Networks

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With the gradual expansion of college scale, the professional categories in colleges and universities are becoming more and more complete, and the volume of courses is becoming more and more huge. In the meantime, the number of students is growing by leaps and bounds, and the teaching resources are subject to more and more complicated teaching tasks. The workload and the difficulty of scheduling in teaching management are also on the rise year by year. This paper proposes a design of a college scheduling algorithm based on an improved genetic ant colony hybrid optimization algorithm. Firstly, the fitness-enhanced elimination law is proposed to improve the selection process of traditional genetic algorithms. Subsequently, the gene infection crossover method is proposed to ensure the increase of the average fitness value in the evolutionary process. Next, the unnecessary replication operation in the traditional genetic algorithm is removed to enhance the operation speed of the algorithm. Finally, the parallel mechanism of fuzzy adaptive is introduced to improve the convergence and stability of the algorithm. For the ant colony optimization algorithm, a nonuniform pheromone distribution is used according to the position of the current raster relative to the starting point, which makes the initial pheromone concentration of the dominant raster higher and avoids blind search by ants. The ant movement rules are redefined by the directional neighborhood expansion strategy to further shorten the path. The experimental results indicate that the hybrid optimization algorithm outperforms other algorithms in terms of performance in terms of scheduling success and scheduling time, and it can be applied in practical scheduling because of the high quality of courses schedule.

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